Design of a high-performance optical tweezer for nanoparticle trapping

Integrated optical nanotweezers offer a novel paradigm for optical trapping, as their ability to confine light at the nanoscale leads to extremely high gradient forces. To date, nanotweezers have been realized either as photonic crystal or as plasmonic nanocavities. Here, we propose a nanotweezer device based on a hybrid photonic/plasmonic cavity with the goal of achieving a very high quality factor-to-mode volume (Q/V) ratio. The structure includes a 1D photonic crystal dielectric cavity vertically coupled to a bowtie nanoantenna. A very high Q/V ~ 107 (λ/n)−3 with a resonance transmission T = 29 % at λR = 1381.1 nm has been calculated by 3D finite element method, affording strong light–matter interaction and making the hybrid cavity suitable for optical trapping. A maximum optical force F = −4.4 pN, high values of stability S = 30 and optical stiffness k = 90 pN/nm W have been obtained with an input power Pin = 1 mW, for a polystyrene nanoparticle with a diameter of 40 nm. This performance confirms the high efficiency of the optical nanotweezer and its potential for trapping living matter at the nanoscale, such as viruses, proteins and small bacteria

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data.

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BACKGROUND: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Early Post-treatment Prostate-specific Antigen at 4 Weeks and Abiraterone and Enzalutamide Treatment for Advanced Prostate Cancer: An International Collaborative Analysis.

Background Declines in prostate-specific antigen (PSA) levels at 12wk are used to evaluate treatment response in metastatic castration-resistant prostate cancer (mCRPC). PSA fall by ≥30% at 4wk (PSA4w30) has been reported to be associated with better outcome in a single-centre cohort study.Objective To evaluate clinical relevance of early PSA decline in mCRPC patients treated with next-generation hormonal treatments (NGHTs) such as abiraterone and enzalutamide.Design, setting, and participants This was a retrospective multicentre analysis. Eligible patients received NGHT for mCRPC between 6 January 2006 and 31 December 2017 in 13 cancer centres worldwide, and had PSA levels assessed at baseline and at 4 and/or 12wk after treatment. PSA response was defined as a ≥30% decline (progression as a ≥25% increase) from baseline.Outcome measurements and statistical analysis Association with overall survival (OS) was analysed using landmark multivariable Cox regression adjusting for previous chemotherapy, including cancer centre as a shared frailty term.Results and limitations We identified 1358 mCRPC patients treated with first-line NGHT (1133 had PSA available at 4wk, and 948 at both 4 and 12wk). Overall, 583 (52%) had a PSA4w30; it was associated with longer OS (median: 23; 95% confidence interval [CI]: 21-25) compared with no change (median: 17; 95% CI: 15-18) and progression (median: 13; 95% CI: 10-15). A PSA12w30 was associated with lower mortality (median OS 22 vs 14; hazard ratio=0.57; 95% CI=0.48-0.67; p<0.001). PSA4w30 strongly correlated with PSA12w30 (ρ=0.91; 95% CI=0.90-0.92; p<0.001). In total, 432/494 (87%) with a PSA4w30 achieved a PSA12w30. Overall, 11/152 (7%) patients progressing at 4wk had a PSA12w30 (1% of the overall population).Conclusions PSA changes in the first 4wk of NGHT therapies are strongly associated with clinical outcome from mCRPC and can help guide early treatment switch decisions.Patient summary Prostate-specific antigen changes at 4wk after abiraterone/enzalutamide treatment are important to determine patients' outcome and should be taken into consideration in clinical practice

Circulating AR copy number and outcome to enzalutamide in docetaxel-treated metastatic castration-resistant prostate cancer

In the present study, we aimed to evaluate the association of circulating AR copy number (CN) and outcome in a cohort of patients with advanced castration-resistant prostate cancer (CRPC) treated with enzalutamide after docetaxel. Fifty-nine CRPC patients were evaluated. AR CN was analyzed with real-time and digital PCR in the serum collected at starting of treatment. Progressive disease was defined on the basis of Prostate Cancer Working Group 2 criteria. AR CN gain was found in 21 of 59 (36%) patients. Median baseline PSA, alkaline phosphatase and lactate dehydrogenase levels were higher in the AR CN gained group (p = 0.007, p = 0.003, p = 0.0009, respectively). Median PFS of patients with AR CN gain was 2.4 (95%CI: 1.9-3.2) vs. 4.0 months (95%CI: 3.0-6.5) of those with no gain (p = 0.0004). Median OS of patients with AR CN gain was 6.1 (95%CI: 3.4-8.6) vs. 14.1 months (95%CI: 8.2-20.5) of those with no gain (p = 0.0003). At multivariate analysis, PSA decline ≥ 50% and AR CN showed a significant association with PFS (p = 0.008 and p = 0.002, respectively) and OS (p = 0.009 and p = 0.001, respectively). These findings indicate that the detection of circulating AR CN gain is a promising non-invasive biomarker for outcome prediction to enzalutamide treatment in CRPC patients

Transition metal dichalcogenide dimer nanoantennas for tailored light–matter interactions

Transition metal dichalcogenides have emerged as promising materials for nanophotonic resonators because of their large refractive index, low absorption within a large portion of the visible spectrum, and compatibility with a wide range of substrates. Herein, we use these properties to fabricate WS2 double-pillar nanoantennas in a variety of geometries enabled by the anisotropy in the crystal structure. Using dark-field spectroscopy, we reveal multiple Mie resonances, to which we couple WSe2 monolayer photoluminescence and achieve Purcell enhancement and an increased fluorescence by factors up to 240 for dimer gaps of 150 nm. We introduce postfabrication atomic force microscope repositioning and rotation of dimer nanoantennas, achieving gaps as small as 10 ± 5 nm, which enables a host of potential applications, including strong Purcell enhancement of single-photon emitters and optical trapping, which we study in simulations. Our findings highlight the advantages of using transition metal dichalcogenides for nanophotonics by exploring applications enabled by their properties

Histone deacetylases as new therapy targets for platinum-resistant epithelial ovarian cancer

Introduction: In developed countries, ovarian cancer is the fourth most common cancer in women. Due to the nonspecific symptomatology associated with the disease many patients with ovarian cancer are diagnosed late, which leads to significantly poorer prognosis. Apart from surgery and radiotherapy, a substantial number of ovarian cancer patients will undergo chemotherapy and platinum based agents are the mainstream first-line therapy for this disease. Despite the initial efficacy of these therapies, many women relapse; therefore, strategies for second-line therapies are required. Regulation of DNA transcription is crucial for tumour progression, metastasis and chemoresistance which offers potential for novel drug targets. Methods: We have reviewed the existing literature on the role of histone deacetylases, nuclear enzymes regulating gene transcription. Results and conclusion: Analysis of available data suggests that a signifant proportion of drug resistance stems from abberant gene expression, therefore HDAC inhibitors are amongst the most promising therapeutic targets for cancer treatment. Together with genetic testing, they may have a potential to serve as base for patient-adapted therapies

Association between AIRE gene polymorphism and rheumatoid arthritis: a systematic review and meta-analysis of case-control studies.

Autoimmune regulator (AIRE) is a transcription factor that functions as a novel player in immunological investigations. In the thymus, it has a pivotal role in the negative selection of naive T-cells during central tolerance. Experimental studies have shown that single nucleotide polymorphism (SNP) alters transcription of the AIRE gene. SNPs thereby provide a less efficient negative selection, propagate higher survival of autoimmune T-cells, and elevate susceptibility to autoimmune diseases. To date, only rheumatoid arthritis (RA) has been analysed by epidemiological investigations in relation to SNPs in AIRE. In our meta-analysis, we sought to encompass case-control studies and confirm that the association between SNP occurrence and RA. After robust searches of Embase, PubMed, Cochrane Library, and Web of Science databases, we found 19 articles that included five independent studies. Out of 11 polymorphisms, two (rs2075876, rs760426) were common in the five case-control studies. Thus, we performed a meta-analysis for rs2075876 (7145 cases and 8579 controls) and rs760426 (6696 cases and 8164 controls). Our results prove that rs2075876 and rs760426 are significantly associated with an increased risk of RA in allelic, dominant, recessive, codominant heterozygous, and codominant homozygous genetic models. These findings are primarily based on data from Asian populations