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Abstract Background Advances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed methods highlights the need for simple, rapid, and reliable methods to simulate realistic microbiome data, which is essential for validating and evaluating the performance of these methods. However, generating realistic microbiome data is challenging due to the complex nature of microbiome data, which feature correlation between taxa, sparsity, overdispersion, and compositionality. Current methods for simulating microbiome data are deficient in their ability to capture these important features of microbiome data, or can require exorbitant computational time. Methods We develop MIDASim (MIcrobiome DAta Simulator), a fast and simple approach for simulating realistic microbiome data that reproduces the distributional and correlation structure of a template microbiome dataset. MIDASim is a two-step approach. The first step generates correlated binary indicators that represent the presence-absence status of all taxa, and the second step generates relative abundances and counts for the taxa that are considered to be present in step 1, utilizing a Gaussian copula to account for the taxon-taxon correlations. In the second step, MIDASim can operate in both a nonparametric and parametric mode. In the nonparametric mode, the Gaussian copula uses the empirical distribution of relative abundances for the marginal distributions. In the parametric mode, a generalized gamma distribution is used in place of the empirical distribution. Results We demonstrate improved performance of MIDASim relative to other existing methods using gut and vaginal data. MIDASim showed superior performance by PERMANOVA and in terms of alpha diversity and beta dispersion in either parametric or nonparametric mode. We also show how MIDASim in parametric mode can be used to assess the performance of methods for finding differentially abundant taxa in a compositional model. Conclusions MIDASim is easy to implement, flexible and suitable for most microbiome data simulation situations. MIDASim has three major advantages. First, MIDASim performs better in reproducing the distributional features of real data compared to other methods, at both the presence-absence level and the relative-abundance level. MIDASim-simulated data are more similar to the template data than competing methods, as quantified using a variety of measures. Second, MIDASim makes few distributional assumptions for the relative abundances, and thus can easily accommodate complex distributional features in real data. Third, MIDASim is computationally efficient and can be used to simulate large microbiome datasets. Video Abstract

Abstract Millirobots must have low cost, efficient locomotion, and the ability to track target trajectories precisely if they are to be widely deployed. With current materials and fabrication methods, achieving all of these features in one millirobot remains difficult. We develop a series of graphene-based helical millirobots by introducing asymmetric light pattern distortion to a laser-induced polymer-to-graphene conversion process; this distortion resulted in the spontaneous twisting and peeling off of graphene sheets from the polymer substrate. The lightweight nature of graphene in combine with the laser-induced porous microstructure provides a millirobot scaffold with a low density and high surface hydrophobicity. Magnetically driven nickel-coated graphene-based helical millirobots with rapid locomotion, excellent trajectory tracking, and precise drug delivery ability were fabricated from the scaffold. Importantly, such high-performance millirobots are fabricated at a speed of 77 scaffolds per second, demonstrating their potential in high-throughput and large-scale production. By using drug delivery for gastric cancer treatment as an example, we demonstrate the advantages of the graphene-based helical millirobots in terms of their long-distance locomotion and drug transport in a physiological environment. This study demonstrates the potential of the graphene-based helical millirobots to meet performance, versatility, scalability, and cost-effectiveness requirements simultaneously.

Abstract Frequent and unobtrusive monitoring of cardiovascular conditions with consumer electronics is a widely pursued goal, since it provides the most economic and effective way of preventing and managing cardiovascular diseases (CVDs) ─ the leading causes of death worldwide. However, most current wearable and flexible devices can only support the measurement of one or two types of vital signs, such as heart rate and blood oxygen level, due to the lack of physiological models to link the measured signals to cardiovascular conditions. Here, we report a stroke-volume allocation (SVA) model to quantify the cushioning function of arteries and empower nearly all existing cardiac sensors with new functions, including arterial stiffness evaluation, dynamic blood pressure tracking and classification of CVD-related heart damage. Large-scale clinical data testing involving a hybrid dataset taken from 6 hospitals/research institutes (9 open databases and 4 self-built databases from 878 subjects in total) and diverse measurement approaches was carried out to validate the SVA model. The results show that the SVA-based parameters correlate well with the gold-standard measurements in arterial stiffness and blood pressure and outperform the commonly used vital sign (e.g., blood pressure) alone in detecting abnormalities in cardiovascular systems.

Abstract Tin‐based perovskite solar cells (PSCs) are promising environmentally friendly alternatives to their lead‐based counterparts, yet they currently suffer from much lower device performance. Due to variations in the chemical properties of lead (II) and tin (II) ions, similar treatments may yield distinct effects resulting from differences in underlying mechanisms. In this work, a surface treatment on tin‐based perovskite is conducted with a commonly employed ligand, iso‐butylammonium iodide (iso‐BAI). Unlike the passivation effects previously observed in lead‐based perovskites, such treatment leads to the recrystallization of the surface, driven by the higher solubility of tin‐based perovskite in common solvents. By carefully designing the solvent composition, the perovskite surface is effectively modified while preserving the integrity of the bulk. The treatment led to enhanced surface crystallinity, reduced surface strain and defects, and improved charge transport. Consequently, the best‐performing power conversion efficiency of FASnI3 PSCs increases from 11.8% to 14.2%. This work not only distinguishes the mechanism of surface treatments in tin‐based perovskites from that of lead‐based counterparts, but also underscores the critical role in designing tailor‐made strategies for fabricating efficient tin‐based PSCs.

Abstract Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people’s daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Abstract Background Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS. Results We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios. Conclusions Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. Video Abstract

The application of two-photon lithography has been limited by the low writing speed. The authors reported a multi-focus improved lithography platform that employs up to 2000 laser foci to perform parallel fabrication with 90 nm resolution via optimised polymerisation and single-pulse printing strategy.

Abstract Background Electronic cigarettes (ECs) have been widely used by young individuals in the U.S. while being considered less harmful than conventional tobacco cigarettes. However, ECs have increasingly been regarded as a health risk, producing detrimental chemicals that may cause, combined with poor oral hygiene, substantial inflammation in gingival and subgingival sites. In this paper, we first report that EC smoking significantly increases the odds of gingival inflammation. Then, through mediation analysis, we seek to identify and explain the mechanism that underlies the relationship between EC smoking and gingival inflammation via the oral microbiome. Methods We collected saliva and subgingival samples from 75 EC users and 75 non-users between 18 and 34 years in age and profiled their microbial compositions via 16S rRNA amplicon sequencing. We conducted raw sequence data processing, denoising and taxonomic annotations using QIIME2 based on the expanded human oral microbiome database (eHOMD). We then created functional annotations (i.e., KEGG pathways) using PICRUSt2. Results We found significant increases in α-diversity for EC users and disparities in β-diversity between EC users and non-users. We also found significant disparities between EC users and non-users in the relative abundance of 36 microbial taxa in the saliva site and 71 microbial taxa in the subgingival site. Finally, we found that 1 microbial taxon in the saliva site and 18 microbial taxa in the subgingival site significantly mediated the effects of EC smoking on gingival inflammation. The mediators on the genus level, for example, include Actinomyces, Rothia, Neisseria, and Enterococcus in the subgingival site. In addition, we report significant disparities between EC users and non-users in the relative abundance of 71 KEGG pathways in the subgingival site. Conclusions These findings reveal that continued EC use can further increase microbial dysbiosis that may lead to periodontal disease. Our findings also suggest that continued surveillance for the effect of ECs on the oral microbiome and its transmission to oral diseases is needed.

Abstract Background Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes. Results To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well. Conclusions The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.

BackgroundPediatric cancer patients in China often present at an advanced stage of disease resulting in lower survival and poorer health outcomes. One factor hypothesized to contribute to delays in pediatric cancer has been the online health information–seeking (OHIS) behaviors by caregivers. ObjectiveThis study aims to examine the association between OHIS behaviors by caregivers and delays for Chinese pediatric cancer patients using a mixed methods approach. MethodsThis study used a mixed methods approach, specifically a sequential explanatory design. OHIS behavior by the caregiver was defined as the way caregivers access information relevant to their children’s health via the Internet. Delays in pediatric cancer were defined as any one of the following 3 types of delay: patient delay, diagnosis delay, or treatment delay. The quantitative analysis methods included descriptive analyses, Student t tests, Pearson chi-square test, and binary logistic regression analysis, all performed using Stata. The qualitative analysis methods included conceptual content analysis and the Colaizzi method. ResultsA total of 303 pediatric cancer patient-caregiver dyads was included in the quantitative survey, and 29 caregivers completed the qualitative interview. Quantitative analysis results revealed that nearly one-half (151/303, 49.8%) of patients experienced delays in pediatric cancer, and the primary type of delay was diagnosis delay (113/303, 37.3%), followed by patient delay (50/303, 16.5%) and treatment delay (24/303, 7.9%). In this study, 232 of the 303 (76.6%) caregiver participants demonstrated OHIS behaviors. When those engaged in OHIS behaviors were compared with their counterparts, the likelihood of patient delay more than doubled (odds ratio=2.21; 95% CI 1.03-4.75). Qualitative analysis results showed that caregivers’ OHIS behaviors impacted the cancer care pathway by influencing caregivers’ symptom appraisal before the first medical contact and caregivers’ acceptance of health care providers’ diagnostic and treatment decisions. ConclusionsOur findings suggest that OHIS among Chinese pediatric caregivers may be a risk factor for increasing the likelihood of patient delay. Our government and society should make a concerted effort to regulate online health information and improve its quality. Specialized freemium consultations provided by health care providers via online health informatic platforms are needed to shorten the time for caregivers’ cancer symptom appraisal before the first medical contact.

Summary: Budd-Chiari syndrome (BCS) is characterized by hepatic venous outflow obstruction, posing life-threatening risks in severe cases. Reported risk factors include inherited and acquired hypercoagulable states or other predisposing factors. However, many patients have no identifiable etiology, and causes of BCS differ between the West and East. This study recruited 500 BCS patients and 696 normal individuals for whole-exome sequencing and developed a polygenic risk scoring (PRS) model using PLINK, LASSOSUM, BLUP, and BayesA methods. Risk factors for venous thromboembolism and vascular malformations were also assessed for BCS risk prediction. Ultimately, we discovered potential BCS risk mutations, such as rs1042331, and the optimal BayesA-generated PRS model presented an AUC >0.9 in the external replication cohort. This model provides particular insights into genetic risk differences between China and the West and suggests shared genetic risks among BCS, venous thromboembolism, and vascular malformations, offering different perspectives on BCS pathogenesis.

Perovskite light‐emitting diodes (PeLEDs) based on 3D mixed Br‐Cl compositions have shown good color stability and efficiency in the sky‐blue color range. However, when the Cl/Br ratio increases to reach deep‐blue emission, the performance of the PeLEDs decreases drastically, with the external quantum efficiency (EQE) typically below 1%. Such performance decay is due largely to the poor morphology of the emissive layer. Herein, theoretical and experimental approach is combined to investigate the evolution of solvated lead complexes in the precursor solution of mixed Br–Cl perovskites. It is found that the energetically favorable exchange of halide ions in the lead complexes drives the precipitation of CsCl, leading to extremely low precursor solubility at high Cl/Br ratios and consequently poor perovskite film coverage. Based on the findings, a metastable dissolution strategy for perovskite film preparation is proposed and deep‐blue PeLEDs with a record high EQE of 4% in 3D‐based PeLEDs and excellent spectral stability is achieved.

Sorafenib (SFN) is a multi-kinase inhibitor drug for the treatment of advanced hepatocellular carcinoma (HCC), but its limited efficacy is a major obstacle to the clinical outcomes of patients with HCC. We aimed to explore a novel molecular mechanism underlying the chemosensitivity of HCC to SFN, and to identify a promising therapeutic target for HCC treatment. In this study, bioinformatic analysis revealed that DDX24 was associated with poor survival in HCC cases, and significantly related to the pathways modulating tumor development. DDX24 regulated HCC cell proliferation and migration potentials. Moreover, reduction of DDX24 promoted the sorafenib-mediated inhibition of HCC cell growth and migration, the elevation of sorafenib-induced HCC cell apoptosis. DDX24 overexpression suppressed the inhibitory effect of SFN on cell proliferation and migration and reduced the apoptosis induced by SFN. Further, DDX24, combined with SFN treatment, presented a synergistic enhancement of the sensitivity of SFN to the growth and migration of HCC cells via AKT/ERK and the epithelial-mesenchymal transition (EMT) pathways, and that it modulated apoptosis via the caspase/PARP pathway. Mechanistically, SNORA18 served as a target gene for DDX24, regulating the chemosensitivity of sorafenib-treated HCC cells. Furthermore, SNORA18 knockdown or overexpression could partially reverse the inhibition or elevation of cell viability, colony formation and migration induced by DDX24 in sorafenib-treated HCC cells, respectively. Collectively, our results suggest that DDX24 regulates the chemosensitivity of HCC to SFN by mediating the expression of SNORA18, which may act as an effective therapeutic target for improving SFN efficiency in HCC treatment.

Here, the authors present ConQuR, a conditional quantile regression method that removes microbiome batch effects through non-parametric modeling of complex microbial read counts, while preserving the signals of interest.

Abstract An altered colonic microbiota probably increases colorectal adenoma (CRA) and cancer (CRC) risk, but large, unbiased fecal collections are needed to examine the relationship of gut microbiota diversity and composition to colorectal carcinogenesis. This study assessed whether fecal immunochemical tests (FITs) from CRA/CRC screening may fulfill this requirement. Using FIT, self-collected by members of Kaiser Permanente Hawaii (KPH), as well as interspersed quality control (QC) specimens, DNA was extracted and amplified to generate 16S rRNA microbiome profiles rarified at 10,000 reads. CRA/CRC were diagnosed by colonoscopy and histopathology. Covariates were from electronic KPH records. Of 921 participants’ FIT devices, 538 (58%) yielded at least 10,000 rRNA reads and 1016 species-level variants mapped to 46 genera. Of the 538 evaluable participants, 63 (11.7%) were FIT-negative per protocol, and they were considered negative for CRA/CRC. Of the 475 FIT + participants, colonoscopy and pathologic review revealed that 8 (1.7%) had CRC, 71 (14.9%) had high-risk CRA, 107 (22.5%) had low-risk CRA, and 289 (60.8%) did not have CRA/CRC. Men were 2.27-fold [95% confidence interval (CI) 1.32–3.91] more likely than women to be FIT+ . Men also had 1.96-fold (CI 1.24–3.07) higher odds of low-risk CRA, with similar trends for high-risk CRA and CRC. CRA/CRC were not associated with overweight, obesity, diabetes, or antibiotic prescriptions in this study. QC analysis across 24 batches of FIT devices revealed QC outliers in four batches. With or without exclusion of the four QC-outlier batches, as well as lenient (1000-read) rarefaction, CRA/CRC had no consistent, statistically significant associations with fecal microbiome alpha diversity, beta diversity or genera relative abundance. CRA/CRC had expected associations with male sex but not with microbiome metrics. Fecal microbiome profiling using DNA extracted from at-home collected, re-used FIT devices is feasible, albeit with substantial challenges. Using FITs for prospective microbiome studies of CRA/CRC risk should consider the impact of the current findings on statistical power and requisite sample sizes.

BackgroundElderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population.MethodsRetrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts.ConclusionNosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.

Objectives: Pancreatic undifferentiated carcinoma accounts for 2%–7% of pancreatic carcinomas. We aimed to investigate the pathological and genetic characteristics of pancreatic undifferentiated carcinoma with osteoclast-like giant cells and the key points of treatment.Methods: The clinical data and follow-up results of four patients diagnosed with pancreatic undifferentiated carcinoma with osteoclast-like giant cells between May 2015 and May 2020 at the First Affiliated Hospital of Xi’an Jiaotong University were retrospectively analyzed.Results: Chief complaints included “pain and discomfort in the upper abdomen” (2/4), “nausea and vomiting” (1/4) or no symptoms (1/4). Preoperative mildly elevated tumor markers included carcinoembryonic antigen (1/4) and CA19-9 (1/4). The tumors were located in the tail of the pancreas in three patients and the head and neck in one patient. Tumor metastasis was found in pancreatic adipose tissue in two patients and lymph node metastasis in one patient, with microscopic heterogeneous mononuclear cells and scattered osteoclast-like giant cells of various sizes. One patient (1/4) had a mucinous cystic tumor of the pancreas, and two patients (2/4) had adenocarcinoma of the pancreatic duct. Only one patient received postoperative gemcitabine combined with albumin-bound paclitaxel chemotherapy.Conclusion: Currently, treatment guidelines are lacking for PUC-OGC, and prognosis varies markedly. More cases must be reported to clarify its origination. The long-term follow-up of diagnosed patients and genetic mutation testing can also contribute to improving treatment and prognosis of this disease.

Abstract Background Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates. Methods As an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy. Results Thorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives. Conclusions We present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis. Video Abstract

Lung cancers are the leading cause of cancer deaths worldwide and pose a grave threat to human life and health. Non-small cell lung cancer (NSCLC) is the most frequent malignancy occupying 80% of all lung cancer subtypes. Except for other mutations (e.g., KRASG12V/D) that are also vital for the occurrence, KRASG12C gene mutation is a significant driving force of NSCLC, with a prevalence of approximately 14% of all NSCLC patients. However, there are only a few therapeutic drugs targeting KRASG12C mutations currently. Here, we synthesized hydrocarbon-stapled peptide 3 that was much shorter and more stable with modest KRASG12C binding affinity and the same anti-tumor effect based on the α-helical peptide mimic SAH-SOS1A. The stapled peptide 3 effectively induced G2/M arrest and apoptosis, inhibiting cell growth in KRAS-mutated lung cancer cells via disrupting the KRAS-mediated RAF/MEK/ERK signaling, which was verified from the perspective of genomics and proteomics. Peptide 3 also exhibited strong anti-trypsin and anti-chymotrypsin abilities, as well as good plasma stability and human liver microsomal metabolic stability. Overall, peptide 3 retains the equivalent anti-tumor activity of SAH-SOS1A but with improved stability and affinity, superior to SAH-SOS1A. Our work offers a structural optimization approach of KRASG12C peptide inhibitors for cancer therapy.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.

BackgroundAt present, immunotherapy has become an important treatment for lung cancer. With the widespread use of immune checkpoint inhibitors (ICIs), we must be strict with the emergence of immune related adverse events (irAEs). There are also some patients who do not respond to immunotherapy. However, there was no biomarkers to predict the safety and efficacy of immunotherapy. The selection of immunotherapy beneficiaries contributes to improving the efficacy and safety of lung cancer treatment.MethodThe electronic medical records of 221 lung cancer patients with complete clinical data who received immunotherapy from the First Affiliated Hospital of Xi ‘an Jiaotong University from November 2020 to October 2021 were collected and followed up. IBM SPSS Statistic 26.0 and R 4.1.2 software were used for statistical analysis and mapping.Results1. A total of 221 lung cancer patients receiving immunotherapy were included in the study. Higher baseline levels of IL-1β (7.88 vs 16.16pg/mL, P=0.041) and IL-2 (1.28 vs 2.48pg/mL, P=0.001) were significantly associated with irAEs. Higher levels of IL-5 (2.64 vs 5.68pg/mL, P=0.013), IFN-α (1.70 vs 3.56pg/mL, P=0.004) and IFN-γ (6.14 vs 21.31pg/mL, P=0.022) after the first cycle therapy were associated with irAEs. There was no statistical significance between cytokines and irAEs after the second cycle therapy. Higher IL-5 levels in peripheral blood (9.50 vs 3.57pg/mL, P=0.032) were associated with the occurrence of irAEs after the third cycle therapy. 2.The efficacy of immunotherapy was assessed in 142 lung cancer patients. There was no statistical significance between baseline cytokine levels and clinical benefit. After the first cycle therapy, the level of serum cytokines had no statistical significance with the occurrence of immunotherapy clinical benefit. Lower serum levels of IL-10 (2.66 vs 1.26pg/mL, P=0.016) and IL-17 (8.47 vs 2.81pg/mL, P=0.015) were associated with clinical benefit after the second cycle therapy. Lower serum levels of IL-6 (10.19 vs 41.07pg/mL, P=0.013) and IL-8 (8.01 vs 17.22pg/mL, P=0.039) were associated with clinical benefit of immunotherapy after the third cycle therapy.Conclusion1. Baseline IL-1β and IL-2 levels in peripheral blood were associated with the occurrence of irAEs in lung cancer patients. The levels of IL-5, IFN-α and IFN-γ during treatment were associated with irAEs. 2. Baseline cytokine levels in peripheral blood were not associated with immunotherapy efficacy. The levels of IL-6, IL-8, IL-10, and IL-17 levels during treatment were associated with immunotherapy efficacy.

Close to half of the world population have smartphones, while a flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting‐edge healthcare applications, such as single‐molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky devices. Herein, recent advances in sensors in modern smartphones are reviewed and how they can potentially offer widespread and easy‐to‐implement solutions to emerging healthcare applications, including biosensing, point‐of‐care testing, pollution monitoring, etc., are discussed.

Increasing evidence has elucidated that the microbiome plays a critical role in many human diseases. Apart from continuous and binary traits that measure the extent or presence of a disease, multi-categorical outcomes including variations/subtypes of a disease or ordinal levels of disease severity are commonly seen in clinical studies. On top of that, studies with clustered design (i.e., family-based and longitudinal studies) are popular alternatives to population-based ones as they are able to identify characteristics on both individual and population levels and to investigate the trajectory of traits of interest over time. However, existing methods for microbiome association analysis are inadequate to handle multi-categorical outcomes, neither independent nor clustered data. We propose a microbiome kernel association test with multi-categorical outcomes (MiRKAT-MC). Our method is versatile to deal with both nominal and ordinal outcomes for independent and clustered data. In addition, it incorporates multiple ecological distances to allow for different association patterns between outcomes and microbiome compositions to be incorporated. A computationally efficient pseudo-permutation strategy is used to evaluate the statistical significance. Comprehensive simulations show that MiRKAT-MC preserves the nominal type I error and increases statistical powers under various scenarios and data types. We also apply MiRKAT-MC to real data sets with nominal and ordinal outcomes to gain biological insights. MiRKAT-MC is easy to implement, and freely available via an R package at https://github.com/Zhiwen-Owen-Jiang/MiRKATMC with a Graphical User Interface through R Shinny also available.

Perovskite light emitting diodes suffer from operational stability, showing rapid decay of performance within minutes to hours after turn-on. Here, the authors investigate how the steric and Coulomb interaction of ammonium passivation molecules with varying alkyl chain length can improve device stability by suppressing iodide ion migration.

Abstract Background In human microbiome studies, it is crucial to evaluate the association between microbial group (e.g., community or clade) composition and a host phenotype of interest. In response, a number of microbial group association tests have been proposed, which account for the unique features of the microbiome data (e.g., high-dimensionality, compositionality, phylogenetic relationship). These tests generally fall in the class of aggregation tests which amplify the overall group association by combining all the underlying microbial association signals, and, therefore, they are powerful when many microbial species are associated with a given host phenotype (i.e., low sparsity). However, in practice, the microbial association signals can be highly sparse, and this is especially the situation where we have a difficulty to discover the microbial group association. Methods Here, we introduce a powerful microbial group association test for sparse microbial association signals, namely, microbiome higher criticism analysis (MiHC). MiHC is a data-driven omnibus test taken in a search space spanned by tailoring the higher criticism test to incorporate phylogenetic information and/or modulate sparsity levels and including the Simes test for excessively high sparsity levels. Therefore, MiHC robustly adapts to diverse phylogenetic relevance and sparsity levels. Results Our simulations show that MiHC maintains a high power at different phylogenetic relevance and sparsity levels with correct type I error controls. We also apply MiHC to four real microbiome datasets to test the association between respiratory tract microbiome and smoking status, the association between the infant’s gut microbiome and delivery mode, the association between the gut microbiome and type 1 diabetes status, and the association between the gut microbiome and human immunodeficiency virus status. Conclusions In practice, the true underlying association pattern on the extent of phylogenetic relevance and sparsity is usually unknown. Therefore, MiHC can be a useful analytic tool because of its high adaptivity to diverse phylogenetic relevance and sparsity levels. MiHC can be implemented in the R computing environment using our software package freely available at https://github.com/hk1785/MiHC .

This study aims to construct recombinant lentiviral vectors containing the human stem cell leukemia (SCL) gene and investigate their in vitro transfection efficiency in Interstitial Cells of Cajal (ICC) of guinea pig bladders. In this study, the human SCL gene was successfully cloned, and the recombinant lentivirus GV287-SCL was successfully constructed. The titer of the recombinant lentivirus was 5 × 108 TU /mL. After transfecting the ICCs with the lentiviral vector at different MOIs, the optimal MOI was determined to be 10.0, and the optimal transfection time was determined to be 3 days. The amplification product of the lentivirus transfection group was consistent with the target fragment, indicating that the SCL gene had been successfully introduced into ICCs. In conclusion, the recombinant lentiviral vector GV287-SCL was successfully constructed and transfected into the in vitro cultured ICCs. The successful expression of SCL in ICCs may provide an experimental basis for the in vivo transfection of the SCL gene.

A free-induction-decay (FID) type optically-pumped rubidium atomic magnetometer driven by a radio-frequency (RF) magnetic field is presented in this paper. Influences of parameters, such as the temperature of rubidium vapor cell, the power of pump beam, and the strength of RF magnetic field and static magnetic field on the amplitude and the full width at half maximum (FWHM) of the FID signal, have been investigated in the time domain and frequency domain. At the same time, the sensitivities of the magnetometer for the single-pass and the triple-pass probe beam cases have been compared by changing the optical path of the interaction between probe beam and atomic ensemble. Compared with the sensitivity of ∼21.2 pT/Hz1/2 in the case of the single-pass probe beam, the amplitude of FID signal in the case of the triple-pass probe beam has been significantly enhanced, and the sensitivity has been improved to ∼13.4 pT/Hz1/2. The research in this paper provids a reference for the subsequent study of influence of different buffer gas pressure on the FWHM and also a foundation for further improving the sensitivity of FID rubidium atomic magnetometer by employing a polarization-squeezed light as probe beam, to achieve a sensitivity beyond the photo-shot-noise level.

Abstract Colloidal quantum dot (QD), a solution‐processable nanoscale optoelectronic building block with well‐controlled light absorption and emission properties, has emerged as a promising material system capable of interacting with various photonic structures. Integrated QD/photonic structures have been successfully realized in many optical and optoelectronic devices, enabling enhanced performance and/or new functionalities. In this review, the recent advances in this research area are summarized. In particular, the use of four typical photonic structures, namely, diffraction gratings, resonance cavities, plasmonic structures, and photonic crystals, in modulating the light absorption (e.g., for solar cells and photodetectors) or light emission (e.g., for color converters, lasers, and light emitting diodes) properties of QD‐based devices is discussed. A brief overview of QD‐based passive devices for on‐chip photonic circuit integration is also presented to provide a holistic view on future opportunities for QD/photonic structure‐integrated optoelectronic systems.

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

Abstract Hepatocellular carcinoma (HCC) is a common malignant tumor with insidious onset and rapid progression. Its treatment is often difficult owing to tumor resistance. In this study, we aimed to understand the different biological characteristics, gene expression profiles, and drug resistance mechanisms of HCC cells cultured under different conditions. A conventional adherence method and a liquid overlay technique were used to prepare two‐ and three‐dimensional cultures of Bel‐7402 and 5‐fluorouracil (5‐Fu)‐resistant Bel‐7402 (Bel‐7402/5‐Fu) cells. Morphological characteristics were assessed via microscopy, and cell cycle distribution and apoptotic rate were obtained using flow cytometry. Cell sensitivity to different concentrations of drugs was detected with 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assays. Gene expression profiles and signal transduction pathways of Bel‐7402 and Bel‐7402/5‐Fu cells under different culture conditions were determined using gene chips. Cells in three‐dimensional culture were suspended and they grew into dense multicellular spheroid (MCS) structures, aggregating with each other. In contrast to cells in the two‐dimensional culture, cell cycle arrest was observed in MCSs. The sensitivity of Bel‐7402 cells in the two‐dimensional culture to drugs at high concentrations was significantly higher than that of cells in the three‐dimensional culture (p

Abstract Narrow bandgap semiconductor‐based photodetectors often suffer from high room‐temperature noise and are therefore operated at low temperatures. Here, a hybrid poly(3‐hexylthiophene) (P3HT): HgTe quantum dot (QD) phototransistor is reported, which exhibits high sensitivity and fast photodetection up to 2400 nm wavelength range at room temperature. The active layer of the phototransistor consists of HgTe QDs well dispersed in a P3HT matrix. Fourier‐transform infrared spectra confirm that chemical grafting between P3HT and HgTe QDs is realized after undergoing prolonged coblend stirring and a ligand exchange process. Thanks to the shifting of the charge transport into the P3HT and the partial passivation of the surface traps of HgTe QDs in the blend, the P3HT: HgTe QD hybrid phototransistor shows significantly improved gate‐voltage tuning, 15 times faster response, and ≈80% reduction in the noise level compared to a pristine HgTe QD control device. More than 1011 Jones specific detectivity (estimated from the noise spectral density measured at 1 kHz) is achieved at room temperature, and the response time (measured at 22 mW cm−2 illumination intensity) of the device is less than 1.5 µs. That is comparable to commercial epitaxially grown IR photodetectors operated in the same wavelength range.

The appropriate use of subsidies is the key to promote the development of energy-saving products (ESPs). However, subsidy-related deception behavior frequently occurs. Considering the relevant stakeholders, a game model including governments' subsidy policies, manufacturers' environmental quality measures, and customer environmental awareness (CEA) was constructed. We analyzed the crucial influencing factors of governments' and manufacturers' strategies. Quantitative analyses were performed to verify the modeling analyses and to demonstrate the influence of the game parameters. The results indicate that government regulation is necessary to keep manufacturers honest. Increases in penalties, the subsidy coefficient, environmental quality, and CEA all promoted manufacturer integrity. The results further reveal that the equilibrium probability of manufacturer's integrity decreased with both the sales price of ESPs and the cost of government inspections. Moreover, as the cost coefficient of ESPs increased, the government enhanced the relevant regulations. Collectively, these results suggest strategies to reduce subsidy-related deception behavior and improve the effectiveness of government regulations.

Abstract Background Lysinibacillus sphaericus has been widely used in integrated mosquito control program and it is one of the minority bacterial species unable to metabolize carbohydrates. In consideration of the high genetic conservation at genomic level and difficulty of genetic horizontal transfer, it is hypothesized that effective restriction-modification (R-M) systems existed in mosquitocidal L. sphaericus. Results In this study, six type II R-M systems including LspC3–41I were predicted in L. sphaericus C3–41 genome. It was found that the cell free extracts (CFE) from this strain shown similar restriction and methylation activity on exogenous Bacillus/Escherichia coli shuttle vector pBU4 as the HaeIII, which is an isoschizomer of BspRI. The Bsph_0498 (encoding the predicted LspC3–41IR) knockout mutant Δ0498 and the complement strain RC0498 were constructed. It was found that the unmethylated pBU4 can be digested by the CFE of C3–41 and RC0498, but not by that of Δ0498. Furthermore, the exogenous plasmid pBU4 can be transformed at very high efficacy into Δ0498, low efficacy into RC0498, but no transformation into C3–41, indicating that LspC3–41I might be a major determinant for the genetic restriction barrier of strain C3–41. Besides, lspC3–41IR and lspC3–41IM genes are detected in other two strains besides C3–41 of the tested 16 L. sphaericus strains, which all belonging to serotype H5 and MLST sequence type (ST) 1. Furthermore, the three strains are not horizontal transferred, and this restriction could be overcome by in vitro methylation either by the host CFE or by commercial methytransferase M. HaeIII. The results provide an insight to further study the genetic restriction, modification and evolution of mosquitocidal L. sphaericus, also a theoretical basis and a method for the genetic manipulations of L. sphaericus. Conclusions LspC3–41I is identified as the major determinant for the restriction barrier of L. sphaericus C3–41. Only three strains of the tested 16 L. sphaericus strains, which all belonging to serotype H5 and ST1 by MLST scheme, contain LspC3–41I system. Two different methods can be used to overcome the restriction barrier of the three isolates to get transformants efficiently: 1) to methylate plasmid DNA prior to the electroporation; and 2) to delete the major restriction endonuclease encoding gene lspC3–41IR.

This paper reports a procedure of building a functionalized reduced graphene oxide (rGO) library and an electronic nose based on the library. Functionalized rGO was prepared by reducing and modifying graphene oxide (GO) with chemically diverse amine ligands. We used the unmodified rGO and eight different functionalized rGO to build the chemiresistive sensor arrays in the electronic nose. Four potential cancer-related biomarkers in breath, ethanol, 2-ethylhexanol, nonanal and ethylbenzene, were used as the model volatile organic compounds (VOCs) analytes. The electronic nose showed linear responses to the model VOCs with high sensitivity of 25 ppm. Principal component analysis (PCA) results demonstrated the successful discrimination of the four model VOCs and binary VOC mixtures. The procedure of building the rGO library was simple and yielded consistent performance. We expected more versatile and sensitive electronic noses to be constructed based on the functionalized rGO library with potential applications in recognition and quantitative measurement of VOCs for health monitoring and disease diagnosis.

Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference.

Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential abundance analysis: the individual-based univariate differential abundance analysis and the group-based multivariate differential abundance analysis. The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. The multivariate analysis evaluates the overall shift in the abundance of microbiome composition between two conditions, which provides useful preliminary differential information for the necessity of follow-up validation studies. In this paper, we present a novel Adaptive multivariate two-sample test for Microbiome Differential Analysis (AMDA) to examine whether the composition of a taxa-set are different between two conditions. Our simulation studies and real data applications demonstrated that the AMDA test was often more powerful than several competing methods while preserving the correct type I error rate. A free implementation of our AMDA method in R software is available at https://github.com/xyz5074/AMDA.

Serious environmental and human health problems caused by the abuse of antibiotics have attracted worldwide concern. Recently, metal−organic frameworks (MOFs) with high porosity have drawn wide attention for their effects in the adsorption and removal of pollutants from complex matrices. Herein, a high-stable metal organic framework (MOF), i.e., ((ZnCl2)3(L)2·DMF)n, where L=1,3,5-tris((pyridin-4-ylthio)methyl)benzene), MOF 1, was applied to adsorb and remove tetracycline from sewage and dairy products. The results showed that MOF 1 exhibited a strong performance in the adsorption of tetracycline. The effects of initial pH values, adsorbent dose, contact time and ionic strength of the adsorption performance of MOF 1 were investigated. The adsorption kinetics best fit the pseudo-second order model, and the adsorption isotherms matched the Langmuir adsorption model well. It was indicated that both chemical adsorption and physical adsorption play an important role in the adsorption process, and the adsorption of tetracycline was homogeneous and occurred on a monolayer on the surface of MOF 1. Additionally, the stability of MOF 1 and the details of the adsorption mechanism were also investigated. Thus, this study provides a new candidate for the application of MOFs-based adsorbents in the removal of antibiotics from sewage and dairy products.

[This corrects the article DOI: 10.1371/journal.pone.0056823.].

Monolayer nano-sphere arrays attract great research interest as they can be used as templates to fabricate various nano-structures. Plasma etching, and in particular high-frequency plasma etching, is the most commonly used method to obtain non-close-packed monolayer arrays. However, the method is still limited in terms of cost and efficiency. In this study, we demonstrate that a low frequency (40 kHz) plasma etching system can be used to fabricate non-close-packed monolayer arrays of polystyrene (PS) nano-spheres with smooth surfaces and that the etching rate is nearly doubled compared to that of the high-frequency systems. The study reveals that the low-frequency plasma etching process is dominated by a thermal evaporation etching mechanism, which is different from the atom-scale dissociation mechanism that underlines the high-frequency plasma etching. It is found that the polystyrene nano-sphere size can be precisely controlled by either adjusting the etching time or power. Through introducing oxygen as the assisting gas in the low frequency plasma etching system, we achieved a coalesced polystyrene nano-sphere array and used it as a template for metal-assisted chemical etching. We demonstrate that the method can significantly improve the aspect ratio of the silicon nanowires to over 200 due to the improved flexure rigidity.

Abstract Formamidinium lead iodide (FAPbI3) is a newly developed hybrid perovskite that potentially can be used in high-efficiency solution-processed solar cells. Here, the temperature-dependent dynamic optical properties of three types of FAPbI3 perovskite films (fabricated using three different precursor systems) are comparatively studied. The time-resolved photoluminescence (PL) spectra reveal that FAPbI3 films made from the new precursor (a mixture of formamidinium iodide and hydrogen lead triiodide) exhibit the longest lifetime of 439 ns at room temperature, suggesting a lower number of defects and lower non-radiative recombination losses compared with FAPbI3 obtained from the other two precursors. From the temperature-dependent PL spectra, a phase transition in the films is clearly observed. Meanwhile, exciton-binding energies of 8.1 and 18 meV for the high- and low-temperature phases are extracted, respectively. Importantly, the PL spectra for all of the samples show a single peak at room temperature, whereas at liquid-helium temperature the emission features two peaks: one in higher energy displaying a fast decay (0.5 ns) and a second red-shifted peak with a decay of up to several microseconds. These two emissions, separated by ~18 meV, are attributed to free excitons and bound excitons with singlet and triplet characters, respectively.

BackgroundBlack men who have sex with men (MSM) experience disproportionate rates of HIV infection in the USA, despite being no more likely to engage in sexual risk behaviors than other MSM racial/ethnic groups. HIV pre-exposure prophylaxis (PrEP) has been shown to reduce risk of HIV acquisition; however, rates of PrEP use among Black MSM remain low. Clinical, psychosocial, and structural factors have been shown to impact PrEP use and adherence among Black MSM. Care coordination of HIV prevention services has the potential to improve PrEP use and adherence for Black MSM, as it has been shown to improve HIV-related care outcomes among people living with HIV.MethodsClient-centered care coordination (C4) is a multi-level intervention designed to address clinical, psychosocial, and structural barriers to HIV prevention services for Black MSM within HPTN 073, a PrEP demonstration project among Black MSM in three cities in the USA. The current study examined the implementation process of C4, specifically investigating the activities, cost, time, and outcomes associated with the C4 intervention.ResultsOn average, participants engaged in five care coordination encounters. The vast majority of care coordination activities were conducted by counselors, averaging 30 min per encounter. The cost of care coordination was relatively low with a mean cost of $8.70 per client encounter.ConclusionAlthough client-centered care coordination was initially implemented in well-resourced communities with robust HIV research and service infrastructure, our findings suggest that C4 can be successfully implemented in resource constrained communities.

Germline mutations in the LKB1 gene (also known as STK11) cause the Peutz-Jeghers Syndrome, and somatic loss of LKB1 has emerged as causal event in a wide range of human malignancies, including melanoma, lung cancer, and cervical cancer. The LKB1 protein is a serine-threonine kinase that phosphorylates AMP-activated protein kinase (AMPK) and other downstream targets. Conditional knockout studies in mouse models have consistently shown that LKB1 loss promotes a highly-metastatic phenotype in diverse tissues, and human studies have demonstrated a strong association between LKB1 inactivation and tumor recurrence. Furthermore, LKB1 deficiency confers sensitivity to distinct classes of anticancer drugs. The ability to reliably identify LKB1-deficient tumors is thus likely to have important prognostic and predictive implications. Previous research studies have employed polyclonal antibodies with limited success, and there is no widely-employed immunohistochemical assay for LKB1. Here we report an assay based on a rabbit monoclonal antibody that can reliably detect endogenous LKB1 protein (and its absence) in mouse and human formalin-fixed, paraffin-embedded tissues. LKB1 protein levels determined through this assay correlated strongly with AMPK phosphorylation both in mouse and human tumors, and with mRNA levels in human tumors. Our studies fully validate this immunohistochemical assay for LKB1 in paraffin-embedded formalin tissue sections. This assay should be broadly useful for research studies employing mouse models and also for the development of human tissue-based assays for LKB1 in diverse clinical settings.

Head and neck squamous cell carcinoma (HNSCC) is a frequently fatal heterogeneous disease. Beyond the role of human papilloma virus (HPV), no validated molecular characterization of the disease has been established. Using an integrated genomic analysis and validation methodology we confirm four molecular classes of HNSCC (basal, mesenchymal, atypical, and classical) consistent with signatures established for squamous carcinoma of the lung, including deregulation of the KEAP1/NFE2L2 oxidative stress pathway, differential utilization of the lineage markers SOX2 and TP63, and preference for the oncogenes PIK3CA and EGFR. For potential clinical use the signatures are complimentary to classification by HPV infection status as well as the putative high risk marker CCND1 copy number gain. A molecular etiology for the subtypes is suggested by statistically significant chromosomal gains and losses and differential cell of origin expression patterns. Model systems representative of each of the four subtypes are also presented.

Lung adenocarcinoma (LAD) has extreme genetic variation among patients, which is currently not well understood, limiting progress in therapy development and research. LAD intrinsic molecular subtypes are a validated stratification of naturally-occurring gene expression patterns and encompass different functional pathways and patient outcomes. Patients may have incurred different mutations and alterations that led to the different subtypes. We hypothesized that the LAD molecular subtypes co-occur with distinct mutations and alterations in patient tumors.The LAD molecular subtypes (Bronchioid, Magnoid, and Squamoid) were tested for association with gene mutations and DNA copy number alterations using statistical methods and published cohorts (n = 504). A novel validation (n = 116) cohort was assayed and interrogated to confirm subtype-alteration associations. Gene mutation rates (EGFR, KRAS, STK11, TP53), chromosomal instability, regional copy number, and genomewide DNA methylation were significantly different among tumors of the molecular subtypes. Secondary analyses compared subtypes by integrated alterations and patient outcomes. Tumors having integrated alterations in the same gene associated with the subtypes, e.g. mutation, deletion and underexpression of STK11 with Magnoid, and mutation, amplification, and overexpression of EGFR with Bronchioid. The subtypes also associated with tumors having concurrent mutant genes, such as KRAS-STK11 with Magnoid. Patient overall survival, cisplatin plus vinorelbine therapy response and predicted gefitinib sensitivity were significantly different among the subtypes.The lung adenocarcinoma intrinsic molecular subtypes co-occur with grossly distinct genomic alterations and with patient therapy response. These results advance the understanding of lung adenocarcinoma etiology and nominate patient subgroups for future evaluation of treatment response.