Personalized medicine using DNA biomarkers: a review

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Label-free and amplification-free viral RNA quantification from primate biofluids using a trapping-assisted optofluidic nanopore platform

Viral outbreaks can cause widespread disruption, creating the need for diagnostic tools that provide high performance and sample versatility at the point of use with moderate complexity. Current gold standards such as PCR and rapid antigen tests fall short in one or more of these aspects. Here, we report a label-free and amplification-free nanopore sensor platform that overcomes these challenges via direct detection and quantification of viral RNA in clinical samples from a variety of biological fluids. The assay uses an optofluidic chip that combines optical waveguides with a fluidic channel and integrates a solid-state nanopore for sensing of individual biomolecules upon translocation through the pore. High specificity and low limit of detection are ensured by capturing RNA targets on microbeads and collecting them by optical trapping at the nanopore location where targets are released and rapidly detected. We use this device for longitudinal studies of the viral load progression for Zika and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infections in marmoset and baboon animal models, respectively. The up to million-fold trapping-based target concentration enhancement enables amplification-free RNA quantification across the clinically relevant concentration range down to the assay limit of RT-qPCR as well as cases in which PCR failed. The assay operates across all relevant biofluids, including semen, urine, and whole blood for Zika and nasopharyngeal and throat swab, rectal swab, and bronchoalveolar lavage for SARS-CoV-2. The versatility, performance, simplicity, and potential for full microfluidic integration of the amplification-free nanopore assay points toward a unique approach to molecular diagnostics for nucleic acids, proteins, and other targets.

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis

Background

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a persistent infectious disease threatening human health. The existing diagnostic methods still have significant shortcomings, including a low positivity rate in pathogen-based diagnoses and the inability of immunological diagnostics to detect active TB. Hence, it is urgent to develop new techniques to detect TB more accurate and earlier. This research aims to scrutinize and authenticate DNA methylation markers suitable for tuberculosis diagnosis. Concurrently, Providing a new approach for tuberculosis diagnosis.

Methods

Blood samples from patients with newly diagnosed tuberculosis and healthy controls (HC) were utilized in this study. Examining methylation microarray data from 40 whole blood samples (22TB + 18HC), we employed two procedures: signature gene methylated position analysis and signature region methylated position analysis to pinpoint distinctive methylated positions. Based on the screening results, diagnostic classifiers are constructed through machine learning, and validation was conducted through pyrosequencing in a separate queue (22TB + 18HC). Culminating in the development of a new tuberculosis diagnostic method via quantitative real-time methylation specific PCR (qMSP).

Results

The combination of the two procedures revealed a total of 10 methylated positions, all of which were located in the promoter region. These 10 signature methylated positions facilitated the construction of a diagnostic classifier, exhibiting robust diagnostic accuracy in both cross-validation and external test sets. The LDA model demonstrated the best classification performance, achieving an AUC of 0.83, specificity of 0.8, and sensitivity of 0.86 on the external test set. Furthermore, the validation of signature methylated positions through pyrosequencing demonstrated high agreement with screening outcomes. Additionally, qMSP detection of 2 potential hypomethylated positions (cg04552852 and cg12464638) exhibited promising results, yielding an AUC of 0.794, specificity of 0.720, and sensitivity of 0.816.

Conclusion

Our study demonstrates that the validated signature methylated positions through pyrosequencing emerge as plausible biomarkers for tuberculosis diagnosis. The specific methylation markers in the TSPAN4 gene, identified in whole blood samples, hold promise for improving tuberculosis diagnosis. This approach could significantly enhance diagnostic accuracy and speed, offering a new avenue for early detection and treatment.

Plasmonic nano-bowls for monitoring intra-membrane changes in liposomes, and DNA-based nanocarriers in suspension

Programmable nanoscale carriers, such as liposomes and DNA, are readily being explored for personalized medicine or disease prediction and diagnostics. The characterization of these nanocarriers is limited and challenging due to their complex chemical composition. Here, we demonstrate the utilization of surface-enhanced Raman spectroscopy (SERS), which provides a unique molecular fingerprint of the analytes while reducing the detection limit. In this paper, we utilize a silver coated nano-bowl shaped polydimethylsiloxane (PDMS) SERS substrate. The utilization of nano-bowl surface topology enabled the passive trapping of particles by reducing mobility, which results in reproducible SERS signal enhancement. The biological nanoparticles' dwell time in the nano-trap was in the order of minutes, thus allowing SERS spectra to remain in their natural aqueous medium without the need for drying. First, the geometry of the nano-traps was designed considering nanosized bioparticles of 50-150 nm diameter. Further, the systematic investigation of maximum SERS activity was performed using rhodamine 6 G as a probe molecule. The potential of the optimized SERS nano-bowl is shown through distinct spectral features following surface- (polyethylene glycol) and bilayer- (cholesterol) modification of empty liposomes of around 140 nm diameter. Apart from liposomes, the characterization of the highly crosslinked DNA specimens of only 60 nm in diameter was performed. The modification of DNA gel by liposome coating exhibited unique signatures for nitrogenous bases, sugar, and phosphate groups. Further, the unique sensitivity of the proposed SERS substrate displayed distinct spectral signatures for DNA micelles and drug-loaded DNA micelles, carrying valuable information to monitor drug release. In conclusion, the findings of the spectral signatures of a wide range of molecular complexes and chemical morphology of intra-membranes in their natural state highlight the possibilities of using SERS as a sensitive and instantaneous characterization alternative.

From bedside to recovery: exercise therapy for prevention of post-intensive care syndrome

BACKGROUND

As advancements in critical care medicine continue to improve Intensive Care Unit (ICU) survival rates, clinical and research attention is urgently shifting toward improving the quality of survival. Post-Intensive Care Syndrome (PICS) is a complex constellation of physical, cognitive, and mental dysfunctions that severely impact patients' lives after hospital discharge. This review provides a comprehensive and multi-dimensional summary of the current evidence and practice of exercise therapy (ET) during and after an ICU admission to prevent and manage the various domains of PICS. The review aims to elucidate the evidence of the mechanisms and effects of ET in ICU rehabilitation and highlight that suboptimal clinical and functional outcomes of ICU patients is a growing public health concern that needs to be urgently addressed.

MAIN BODY

This review commences with a brief overview of the current relationship between PICS and ET, describing the latest research on this topic. It subsequently summarises the use of ET in ICU, hospital wards, and post-hospital discharge, illuminating the problematic transition between these settings. The following chapters focus on the effects of ET on physical, cognitive, and mental function, detailing the multi-faceted biological and pathophysiological mechanisms of dysfunctions and the benefits of ET in all three domains. This is followed by a chapter focusing on co-interventions and how to maximise and enhance the effect of ET, outlining practical strategies for how to optimise the effectiveness of ET. The review next describes several emerging technologies that have been introduced/suggested to augment and support the provision of ET during and after ICU admission. Lastly, the review discusses future research directions.

CONCLUSION

PICS is a growing global healthcare concern. This review aims to guide clinicians, researchers, policymakers, and healthcare providers in utilising ET as a therapeutic and preventive measure for patients during and after an ICU admission to address this problem. An improved understanding of the effectiveness of ET and the clinical and research gaps that needs to be urgently addressed will greatly assist clinicians in their efforts to rehabilitate ICU survivors, improving patients' quality of survival and helping them return to their normal lives after hospital discharge.

Nanomaterials for detection of biomolecules and delivering therapeutic agents in theragnosis: A review

Nanomaterials are emerging facts used to deliver therapeutic agents in living systems. Nanotechnology is used as a compliment by implementing different kinds of nanotechnological applications such as nano-porous structures, functionalized nanomaterials, quantum dots, carbon nanomaterials, and polymeric nanostructures. The applications are in the initial stage, which led to achieving several diagnoses and therapy in clinical practice. This review conveys the importance of nanomaterials in post-genomic employment, which includes the design of immunosensors, immune assays, and drug delivery. In this view, genomics is a molecular tool containing large databases that are useful in choosing an apt molecular inhibitor such as drug, ligand and antibody target in the drug delivery process. This study identifies the expression of genes and proteins in analysis and classification of diseases. Experimentally, the study analyses the design of a disease model. In particular, drug delivery is a boon area to treat cancer. The identified drugs enter different phase trails (Trails I, II, and III). The genomic information conveys more essential entities to the phase I trials and helps to move further for other trails such as trails-II and III. In such cases, the biomarkers play a crucial role by monitoring the unique pathological process. Genetic engineering with recombinant DNA techniques can be employed to develop genetically engineered disease models. Delivering drugs in a specific area is one of the challenging issues achieved using nanoparticles. Therefore, genomics is considered as a vast molecular tool to identify drugs in personalized medicine for cancer therapy.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Evaluation of Mitochondrial Function in Blood Samples Shows Distinct Patterns in Subjects with Thyroid Carcinoma from Those with Hyperplasia

Metabolic adaptations are a hallmark of cancer and may be exploited to develop novel diagnostic and therapeutic tools. Only about 50% of the patients who undergo thyroidectomy due to suspicion of thyroid cancer actually have the disease, highlighting the diagnostic limitations of current tools. We explored the possibility of using non-invasive blood tests to accurately diagnose thyroid cancer. We analyzed blood and thyroid tissue samples from two independent cohorts of patients undergoing thyroidectomy at the Hospital Universitario 12 de Octubre (Madrid, Spain). As expected, histological comparisons of thyroid cancer and hyperplasia revealed higher proliferation and apoptotic rates and enhanced vascular alterations in the former. Notably, they also revealed increased levels of membrane-bound phosphorylated AKT, suggestive of enhanced glycolysis, and alterations in mitochondrial sub-cellular distribution. Both characteristics are common metabolic adaptations in primary tumors. These data together with reduced mtDNA copy number and elevated levels of the mitochondrial antioxidant PRX3 in cancer tissue samples suggest the presence of mitochondrial oxidative stress. In plasma, cancer patients showed higher levels of cfDNA and mtDNA. Of note, mtDNA plasma levels inversely correlated with those in the tissue, suggesting that higher death rates were linked to lower mtDNA copy number. In PBMCs, cancer patients showed higher levels of PGC-1α, a positive regulator of mitochondrial function, but this increase was not associated with a corresponding induction of its target genes, suggesting a reduced activity in cancer patients. We also observed a significant difference in the PRDX3/PFKFB3 correlation at the gene expression level, between carcinoma and hyperplasia patients, also indicative of increased systemic metabolic stress in cancer patients. The correlation of mtDNA levels in tissue and PBMCs further stressed the interconnection between systemic and tumor metabolism. Evaluation of the mitochondrial gene ND1 in plasma, PBMCs and tissue samples, suggested that it could be a good biomarker for systemic oxidative metabolism, with ND1/mtDNA ratio positively correlating in PBMCs and tissue samples. In contrast, ND4 evaluation would be informative of tumor development, with ND4/mtDNA ratio specifically altered in the tumor context. Taken together, our data suggest that metabolic dysregulation in thyroid cancer can be monitored accurately in blood samples and might be exploited for the accurate discrimination of cancer from hyperplasia.

Fluorescence biosensing of the leukemia gene by combining Target-Programmed controllable signal inspiring engineering

Clinical diagnosis urgently requires ultrasensitive, accurate and rapid monitoring of low-abundance biomarkers. A biosensing strategy capable of detecting target genes at the femtomolar scale was designed in this work. In the biosensing strategy, the target can induce the specially designed hairpin probe H1 to self-fold and form a 3' blunt-ended structure. When there are the hybrid double-stranded P1-T1, ligase, polymerase and nickase, the target gene was recycled, and at the same time the system produces a lot of T1 and T2. T1 and T2 can simultaneously trigger HCR, causing the modified fluorophore FAM on the DNA strand to move away from the quencher group BHQ. The amplified fluorescent signal can be captured by a fluorescence instrument. It is exciting for us that three signal amplifications are involved to achieve femtomolar detection of target genes, namely target recycling, dual-triggered HCR of T1 and T2, and HCR. In addition, it still has good detection ability in actual samples simulated by serum. We expect that the sensing strategy proposed in this paper offers great potential for biomarker detection of leukemia for early clinical diagnosis.

Improved DNA extraction on bamboo paper and cotton is tightly correlated with their crystallinity and hygroscopicity

DNA extraction, a vital pre-requisite for most biological studies, continues to be studied extensively. According to some studies, DNA shows a certain degree of absorbability on filter paper made of plant fiber-based adsorbent material. However, the principle underlying such specific adsorption as well as plant species associated with plant fiber-based adsorbents and optimized extraction conditions have not yet been studied. This study demonstrates the tight correlation between crystallinity and hygroscopicity in plant fiber-based adsorbents used for DNA extraction and proposes the concept of DNA adsorption on plant fiber-based adsorbents, for the first time. We also explored optimal extracting and eluting conditions and developed a novel plant fiber-based DNA extraction method that was quadruple times more powerful than current approaches. Starting with the screening of various types of earthed plant fiber-based adsorbents, we went on to mine new plant fiber-based adsorbents, bamboo paper and degreased cotton, and succeeded in increasing their efficiency of DNA extraction to 4.2 times than that of current approaches. We found a very strong correlation between the crystallinity and hygroscopicity of plant fiber-based adsorbents which showed efficiency for DNA extraction, and thus propose a principle that potentially governs such specific adsorption processes, in the hope that this information may guide related multidisciplinary research studies in the future. Nanodrop, electrophoresis and PCR were selected to demonstrate the quantity, quality, integrity and utility of the extracted DNA. Furthermore, crystallinity, hygroscopicity, pore size distribution and composition of plant fiber-based adsorbents were studied to explore their correlation in an attempt to understand the principle underlying this particular type of adsorption. The findings of this study may be further extended to the extraction of other types of nucleic acids with similar biochemical properties.

Utility of Plasma Protein Biomarkers and Mid-infrared Spectroscopy for Diagnosing Fracture-related Infections: A Pilot Study

OBJECTIVES

To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns in patients with confirmed fracture-related infections (FRIs) with those in controls without infection.

DESIGN

Prospective case-control study.

SETTING

Academic, Level 1 trauma center.

PATIENTS

Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region.

INTERVENTION

Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay techniques. Fourier transform infrared spectroscopy of dried films was used to obtain MIR spectra of plasma samples.

MAIN OUTCOME MEASUREMENTS

The main outcome measurements included plasma protein levels and MIR spectra of samples.

RESULTS

Multivariate analysis-based predictive model developed using enzyme-linked immunosorbent assay-based biomarkers had sensitivity, specificity, and accuracy of 69.2% ± 0.0%, 99.9% ± 1.0%, and 84.5% ± 0.6%, respectively, with platelet-derived growth factor-AB/BB, C-reactive protein, and MIG selected as the minimum number of variables explaining group differences ( P < 0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9% ± 6.2%, 71.9% ± 5.9%, and 70.9% ± 4.8%, respectively, with 6 wavenumbers as explanatory variables ( P < 0.05).

CONCLUSIONS

This pilot study demonstrates the feasibility of using a select panel of plasma proteins and Fourier transform infrared spectroscopy to diagnose FRIs. Preliminary data suggest that the measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRIs. Further investigation of these biomarkers in a larger cohort of patients is warranted.

LEVEL OF EVIDENCE

Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches

Simple Summary

Prostate cancer affects men of all racial and ethnic groups and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to late detection of the disease. There is growing evidence that suggests the contribution of an individual’s genetic profile to prostate cancer. Currently used prostate cancer treatments have serious adverse effects; therefore, new research is focusing on alternative treatment options such as the use of genetic biomarkers for targeted gene therapy, nanotechnology for controlled targeted treatment, and further exploring medicinal plants for new anticancer agents. In this review, we describe the recent advances in prostate cancer research.

Abstract

Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

Evaluation of cancer immunotherapy using mini-tumor chips

Rationale: Predicting tumor responses to adjuvant therapies can potentially help guide treatment decisions and improve patient survival. Currently, tumor pathology, histology, and molecular profiles are being integrated into personalized profiles to guide therapeutic decisions. However, it remains a grand challenge to evaluate tumor responses to immunotherapy for personalized medicine. Methods: We present a microfluidics-based mini-tumor chip approach to predict tumor responses to cancer immunotherapy in a preclinical model. By uniformly infusing dissociated tumor cells into isolated microfluidic well-arrays, 960 mini-tumors could be uniformly generated on-chip, with each well representing the ex vivo tumor niche that preserves the original tumor cell composition and dynamic cell-cell interactions and autocrine/paracrine cytokines. Results: By incorporating time-lapse live-cell imaging, our mini-tumor chip allows the investigation of dynamic immune-tumor interactions as well as their responses to cancer immunotherapy (e.g., anti-PD1 treatment) in parallel within 36 hours. Additionally, by establishing orthotopic breast tumor models with constitutive differential PD-L1 expression levels, we showed that the on-chip interrogation of the primary tumor's responses to anti-PD1 as early as 10 days post tumor inoculation could predict the in vivo tumors' responses to anti-PD1 at the endpoint of day 24. We also demonstrated the application of this mini-tumor chip to interrogate on-chip responses of primary tumor cells isolated from primary human breast and renal tumor tissues. Conclusions: Our approach provides a simple, quick-turnaround solution to measure tumor responses to cancer immunotherapy.

Nucleic Acid Biomarkers in Waldenström Macroglobulinemia and IgM-MGUS: Current Insights and Clinical Relevance

Waldenström Macroglobulinemia (WM) is an indolent lymphoplasmacytic lymphoma, characterized by the production of excess immunoglobulin M monoclonal protein. WM belongs to the spectrum of IgM gammopathies, ranging from asymptomatic IgM monoclonal gammopathy of undetermined significance (IgM-MGUS), through IgM-related disorders and asymptomatic WM to symptomatic WM. In recent years, its complex genomic and transcriptomic landscape has been extensively explored, hereby elucidating the biological mechanisms underlying disease onset, progression and therapy response. An increasing number of mutations, cytogenetic abnormalities, and molecular signatures have been described that have diagnostic, phenotype defining or prognostic implications. Moreover, cell-free nucleic acid biomarkers are increasingly being investigated, benefiting the patient in a minimally invasive way. This review aims to provide an extensive overview of molecular biomarkers in WM and IgM-MGUS, considering current shortcomings, as well as potential future applications in a precision medicine approach.

FPGA Integrated Optofluidic Biosensor for Real-Time Single Biomarker Analysis

Integrated optofluidic biosensors can fill the need for sensitive, amplification-free, multiplex single molecule detection which is relevant for containing the spread of infectious diseases such as COVID-19. Here, we demonstrate a rapid sample-to-answer scheme that uses a field programmable gate array (FPGA) to enable live monitoring of single particle fluorescence analysis on an optofluidic chip. Fluorescent nanobeads flowing through a micro channel are detected with 99% accuracy and particle concentrations in clinically relevant ranges from 3.4×104 to 3.4 × 106/ml are determined within seconds to a few minutes without the need for post-experiment data extraction and analysis. In addition, other extract salient experimental parameters such as dynamic flow rate changes can be monitored in real time. The sensor is validated with real-time fluorescence detection of single bacterial plasmid DNA at attomolar concentrations, showing excellent promise for implementation as a point of care (POC) diagnostic tool.

MiR-378a inhibits glucose metabolism by suppressing GLUT1 in prostate cancer

Prostate cancer (PCa) is the fifth leading cause of cancer related deaths worldwide, in part due to a lack of molecular stratification tools that can distinguish primary tumours that will remain indolent from those that will metastasise. Amongst potential molecular biomarkers, microRNAs (miRs) have attracted particular interest because of their high stability in body fluids and fixed tissues. These small non-coding RNAs modulate several physiological and pathological processes, including cancer progression. Herein we explore the prognostic potential and the functional role of miRs in localised PCa and their relation to nodal metastasis. We define a 7-miR signature that is associated with poor survival independently of age, Gleason score, pathological T state, N stage and surgical margin status and that is also prognostic for disease-free survival in patients with intermediate-risk localised disease. Within our 7-miR signature, we show that miR-378a-3p (hereafter miR-378a) levels are low in primary tumours compared to benign prostate tissue, and also lower in Gleason score 8-9 compared to Gleason 6-7 PCa. We demonstrate that miR-378a impairs glucose metabolism and reduces proliferation in PCa cells through independent mechanisms, and we identify glucose transporter 1 (GLUT1) messenger RNA as a direct target of miR-378a. We show that GLUT1 inhibition hampers glycolysis, leading to cell death. Our data provides a rational for a new PCa stratification strategy based on miR expression, and it reveals that miR-378a and GLUT1 are potential therapeutic targets in highly aggressive glycolytic PCa.

Characterizing Clinical Heterogeneity in a Large Inpatient Addiction Treatment Sample: Confirmatory Latent Profile Analysis and Differential Levels of Craving and Impulsivity

Background:

Individuals with substance use disorders (SUDs) have highly heterogeneous presentations and identifying more homogeneous subgroups may foster more personalized treatment. This study used SUD and other psychiatric indicators to characterize latent subgroups of patients in a large inpatient addiction treatment program. The resulting subgroups were then analyzed with respect to differences on clinically informative motivational mechanisms.

Methods:

Patients (n = 803) were assessed for severity of SUD (ie, alcohol use disorder, drug use disorder), post-traumatic stress disorder, anxiety disorders, and major depressive disorder. Confirmatory latent profile analysis (CLPA) was used to identify latent subgroups, hypothesizing 4 subgroups. Subgroups were then characterized with respect to multiple indicators of impulsivity (ie, delay discounting and impulsive personality traits via the UPPS-P) and craving.

Results:

The CLPA confirmed the hypothesized 4-profile solution according to all indicators (eg, entropy = 0.90, all posterior probabilities ⩾.92). Profile 1 (n = 229 [32.2%], 24.9% female, median age in range of 45-49) reflected individuals with high alcohol severity and low psychiatric severity (HAlc/LPsy). Profile 2 (n = 193 [27.1%], 29.3% female, median age in range of 35-39) reflected individuals with high drug and psychiatric severity (HDrug/HPsy). Profile 3 (n = 160 [22.5%], 37.6% female, median age in range of 45-49) reflected individuals with high alcohol severity and psychiatric severity (HAlc/HPsy). Profile 4 (n = 130 [18.3%], 19.4% female, median age in range of 35-39) reflected individuals with high drug severity and low psychiatric severity (HDrug/LPsy). Both high comorbid psychiatric severity subgroups exhibited significantly higher craving and facets of impulsivity.

Conclusions:

The results provide further evidence of 4 latent subgroups among inpatients receiving addiction treatment, varying by alcohol versus other drugs and low versus high psychiatric comorbidity. Furthermore, they reveal the highest craving and impulsivity in the high psychiatric comorbidity groups, suggesting targets for more intensive clinical intervention in these patients.

Molecular biomarkers in multiple sclerosis

Multiple sclerosis (MS) is a highly heterogenous disease regarding radiological, pathological, and clinical characteristics and therapeutic response, including both the efficacy and safety profile of treatments. Accordingly, there is a high demand for biomarkers that sensitively and specifically apprehend the distinctive aspects of the MS heterogeneity, and that can aid in better understanding of the disease diagnosis, prognosis, prediction of the treatment response, and, finally, in the development of new treatments. Currently, clinical characteristics (e.g., relapse rate and disease progression) and magnetic resonance imaging play the most important role in the clinical classification of MS and assessment of its course. Molecular biomarkers (e.g., immunoglobulin G (IgG) oligoclonal bands, IgG index, anti-aquaporin-4 antibodies, neutralizing antibodies against interferon-beta and natalizumab, anti-varicella zoster virus and anti-John Cunningham (JC) virus antibodies) complement these markers excellently. This review provides an overview of exploratory, validated and clinically useful molecular biomarkers in MS which are used for prediction, diagnosis, disease activity and treatment response.

Self-assembled DNA dendrons as signal amplifiers in a DNA probe-based chemiluminescence assay for enhanced colorimetric detection of short target cDNA

DNA dendrons are used as signal amplifiers to increase the colorimetric detection of short target cDNA.

Target DNA- and pH-responsive DNA hydrogel-based capillary assay for the optical detection of short SARS-CoV-2 cDNA

DNA is recognized as a powerful biomarker for clinical diagnostics because its specific sequences are closely related to the cause and development of diseases. However, achieving rapid, low-cost, and sensitive detection of short-length target DNA still remains a considerable challenge. Herein, we successfully combine the catalytic hairpin assembly (CHA) technique with capillary action to develop a new and cost-effective method, a target DNA- and pH-responsive DNA hydrogel–based capillary assay, for the naked eye detection of 24 nt short single-stranded target DNA. Upon contact of target DNA, three individual hairpin DNAs hybridize with each other to sufficiently amplify Y-shaped DNA nanostructures (Y-DNA) until they are completely consumed via CHA cycling reactions. Each arm of the resultant Y-DNA contains sticky ends with i-motif DNA structure-forming sequences that can be self-assembled in an acidic environment (pH 5.0) to form target DNA- and pH-responsive DNA hydrogels by means of i-motif DNA-driven crosslinking. When inserting a capillary tube in the resultant solution, the liquid level inside clearly reduces due to the decrease in capillary force induced by the gels. In this way, the developed assay demonstrates sensitive and quantitative detection, with a detection limit of approximately 10 pM of 24 nt short complementary DNA (cDNA) targeting SARS-CoV-2 RNA genes at room temperature within 1 h. The assay is further shown to successfully detect target cDNA in serum, and it is also applied to detect several types of target sequences. Requiring no analytic equipment, precise temperature control, or enzymatic reactions, the developed DNA hydrogel–based capillary assay has potential as a promising naked eye detection platform for target DNA in resource-limited clinical settings.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

A novel approach to imaging and visualization of minute amounts of DNA in small volume samples

This report presents a novel approach for detecting and visualizing small to trace amounts of DNA in a sample. By utilizing both the change in emission spectrum and change in fluorescence lifetime, there is a significant increase in detection sensitivity allowing for the imaging/visualizing of a picograms amount of DNA in a microliters volume. As in the previous reports, one of the oldest DNA intercalators, Ethidium Bromide (EtBr), is employed as a model system. With this new approach, it is feasible to visualize just a few hundred picograms of DNA without the need for prior DNA amplification. The sensitivity can later be largely improved by using an intercalator that exhibits a higher affinity to DNA and a larger fluorescence change upon binding to DNA (e.g., ethidium homodimer, YOYO, or Diamond nucleic acid dyes).

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip

Nucleic acids and proteins are the two most important target types used in molecular diagnostics. In many instances, simultaneous sensitive and accurate detection of both biomarkers from the same sample would be desirable, but standard detection methods are highly optimized for one type and not cross-compatible. Here, we report the simultaneous multiplexed detection of SARS-CoV-2 RNAs and antigens with single molecule sensitivity. Both analytes are isolated and labeled using a single bead-based solid-phase extraction protocol, followed by fluorescence detection on a multi-channel optofluidic waveguide chip. Direct amplification-free detection of both biomarkers from nasopharyngeal swab samples is demonstrated with single molecule detection sensitivity, opening the door for ultrasensitive dual-target analysis in infectious disease diagnosis, oncology, and other applications.

Randomized test-treatment studies with an outlook on adaptive designs

Background

Diagnostic accuracy studies aim to examine the diagnostic accuracy of a new experimental test, but do not address the actual merit of the resulting diagnostic information to a patient in clinical practice. In order to assess the impact of diagnostic information on subsequent treatment strategies regarding patient-relevant outcomes, randomized test-treatment studies were introduced. Various designs for randomized test-treatment studies, including an evaluation of biomarkers as part of randomized biomarker-guided treatment studies, are suggested in the literature, but the nomenclature is not consistent.

Methods

The aim was to provide a clear description of the different study designs within a pre-specified framework, considering their underlying assumptions, advantages as well as limitations and derivation of effect sizes required for sample size calculations. Furthermore, an outlook on adaptive designs within randomized test-treatment studies is given.

Results

The need to integrate adaptive design procedures in randomized test-treatment studies is apparent. The derivation of effect sizes induces that sample size calculation will always be based on rather vague assumptions resulting in over- or underpowered study results. Therefore, it might be advantageous to conduct a sample size re-estimation based on a nuisance parameter during the ongoing trial.

Conclusions

Due to their increased complexity, compared to common treatment trials, the implementation of randomized test-treatment studies poses practical challenges including a huge uncertainty regarding study parameters like the expected outcome in specific subgroups or disease prevalence which might affect the sample size calculation. Since research on adaptive designs within randomized test-treatment studies is limited so far, further research is recommended.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-021-01293-y).

Lateral field excited quartz crystal microbalances for biosensing applications

The most common bulk acoustic wave device used in biosensing applications is the quartz crystal microbalance (QCM), in which a resonant pure shear acoustic wave is excited via electrodes on both major faces of a thin AT-cut quartz plate. For biosensing, the QCM is used to detect the capture of a target by a target-capture film. The sensitivity of the QCM is typically based solely on the detection of mechanical property changes, as electrical property change detection is limited by the electrode on its sensing surface. A modification of the QCM called the lateral field excited (LFE) QCM (LFE-QCM) has been developed with a bare sensing surface as both electrodes are now on a single face of the quartz plate. Compared to the QCM, the LFE-QCM exhibits significantly higher sensitivity to both electrical and mechanical property changes. This paper presents theoretical and experimental aspects of LFE-QCMs. In particular, the presence and strength of the usual and newfound LFE-QCM modes depend on the electrical properties of the film and/or sensing environment. This work also presents examples of experimental setups for measuring the response of an LFE-QCM, followed by results of LFE-QCMs used to detect liquid electrical and mechanical properties, chemical targets, and biological targets. Finally, details are given about the attachment of various target-capture films to the LFE-QCM surface to capture biomarkers associated with diseases such as cancer.

Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine

Accurately predicting the onset and course of a disease in an individual is a major unmet challenge in medicine due to the complex and dynamic nature of disease progression. Continuous data from wearable technologies and biomarker data with a fine time resolution provide a unique opportunity to learn more about disease evolution and to usher in a new era of personalized and real-time medicine. Herein, we propose the potential of real-time, continuously measured physiological data as a noninvasive biomarker approach for detecting disease transitions, using allogeneic hematopoietic stem cell transplant (HCT) patient care as an example. Additionally, we review a recent computational technique, the landscape dynamic network biomarker method, that uses biomarker data to identify transition states in disease progression and explore how to use it with both biomarker and physiological data for earlier detection of graft-versus-host disease specifically. Throughout, we argue that increased collaboration across multiple fields is essential to realizing the full potential of wearable and biomarker data in a new paradigm of personalized and real-time medicine.

Single-molecule analysis of nucleic acid biomarkers - A review

Nucleic acids are important biomarkers for disease detection, monitoring, and treatment. Advances in technologies for nucleic acid analysis have enabled discovery and clinical implementation of nucleic acid biomarkers. However, challenges remain with technologies for nucleic acid analysis, thereby limiting the use of nucleic acid biomarkers in certain contexts. Here, we review single-molecule technologies for nucleic acid analysis that can be used to overcome these challenges. We first discuss the various types of nucleic acid biomarkers important for clinical applications and conventional technologies for nucleic acid analysis. We then discuss technologies for single-molecule in vitro and in situ analysis of nucleic acid biomarkers. Finally, we discuss other ultra-sensitive techniques for nucleic acid biomarker detection.

MiRNA-Based Inspired Approach in Diagnosis of Prostate Cancer

Prostate cancer is one of the most encountered cancer diseases in men worldwide and in consequence it requires the improvement of therapeutic strategies. For the clinical diagnosis, the standard approach is represented by solid biopsy. From a surgical point of view, this technique represents an invasive procedure that may imply several postoperative complications. To overcome these impediments, many trends are focusing on developing liquid biopsy assays and on implementing them in clinical practice. Liquid samples (blood, urine) are rich in analytes, especially in transcriptomic information provided by genetic markers. Additionally, molecular characterization regarding microRNAs content reveals outstanding prospects in understanding cancer progression mechanisms. Moreover, these analytes have great potential for prostate cancer early detection, more accurate prostate cancer staging and also for decision making respecting therapy schemes. However, there are still questionable topics and more research is needed to standardize liquid biopsy-based techniques.

Precision cell-free DNA extraction for liquid biopsy by integrated microfluidics

Cell-free DNA (cfDNA) has been implicated as an important biomarker in cancer management. Thus, efficient techniques for cfDNA extraction are necessary for precision medicine. We developed a centrifugation-free cfDNA extraction microfluidic chip capable of extracting cfDNA from plasma samples through microfluidic circuits within 15 min under vacuum pressure using an immiscible solvent. The microfluidic chip had excellent performance that was comparable to the most widely used commercial product (QIAamp kit) in terms of extraction efficiency, purity, and quality of DNA samples. The microfluidic chip was validated for the continuous monitoring of HER-2 type breast cancer and was able to successfully detect a point mutation in phosphatidylinositol-4,5-bisphosphate 3-kinase (PIK3CA) during severe liver metastasis. The chip effectively eliminates the repetitive centrifugation processes and dramatically shortened the sample preparation time. The proposed platform could facilitate the development of a sample-to-answer system for use in liquid biopsy of cancers.

Recent advances of tissue-interfaced chemical biosensors

This review discusses recent advances of tissue interfaced chemical biosensors, highlights current challenges and gives an outlook on future possibilities.

Molecular biomarkers in multiple sclerosis

Multiple sclerosis (MS) is an inflammatory-neurodegenerative disease of the central nervous system presenting with significant inter- and intraindividual heterogeneity. However, the application of clinical and imaging biomarkers is currently not able to allow individual characterization and prediction. Complementary, molecular biomarkers which are easily quantifiable come from the areas of immunology and neurobiology due to the causal pathomechanisms and can excellently complement other disease characteristics. Only a few molecular biomarkers have so far been routinely used in clinical practice as their validation and transfer take a long time. This review describes the characteristics that an ideal MS biomarker should have and the challenges of establishing new biomarkers. In addition, clinically relevant and promising biomarkers from the blood and cerebrospinal fluid are presented which are useful for MS diagnosis and prognosis as well as for the assessment of therapy response and side effects.

A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock

Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., “big data” training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population.

Recent Progress of Rare-Earth Doped Upconversion Nanoparticles: Synthesis, Optimization, and Applications

Upconversion is a nonlinear optical phenomenon that involves the emission of high-energy photons by sequential absorption of two or more low-energy excitation photons. Due to their excellent physiochemical properties such as deep penetration depth, little damage to samples, and high chemical stability, upconversion nanoparticles (UCNPs) are extensively applied in bioimaging, biosensing, theranostic, and photochemical reactions. Here, recent achievements in the synthesis, optimization, and applications of UCNP-based nanomaterials are reviewed. The state-of-the-art approaches to synthesize UCNPs in the past few years are introduced first, followed by a summary of several strategies to optimize upconversion emissive properties and various applications of UCNPs. Lastly, the challenges and future perspectives of UCNPs are provided as a conclusion.

Liquid Biopsy in Oligometastatic Prostate Cancer-A Biologist's Point of View

Prostate cancer (PCa) is the main cause of cancer-related mortality in males and the diagnosis, treatment, and care of these patients places a great burden on healthcare systems globally. Clinically, PCa is highly heterogeneous, ranging from indolent tumors to highly aggressive disease. In many cases treatment-generally either radiotherapy (RT) or surgery-can be curative. Several key genetic and demographic factors such as age, family history, genetic susceptibility, and race are associated with a high incidence of PCa. While our understanding of PCa, which is mainly based on the tools of molecular biology-has improved dramatically in recent years, efforts to better understand this complex disease have led to the identification of a new type of PCa-oligometastatic PCa. Oligometastatic disease should be considered an individual, heterogeneous entity with distinct metastatic phenotypes and, consequently, wide prognostic variability. In general, patients with oligometastatic disease typically present less biologically aggressive tumors whose metastatic potential is more limited and which are slow-growing. These patients are good candidates for more aggressive treatment approaches. The main aim of the presented review was to evaluate the utility of liquid biopsy for diagnostic purposes in PCa and for use in monitoring disease progression and treatment response, particularly in patients with oligometastatic PCa. Liquid biopsies offer a rapid, non-invasive approach whose use t is expected to play an important role in routine clinical practice to benefit patients. However, more research is needed to resolve the many existing discrepancies with regard to the definition and isolation method for specific biomarkers, as well as the need to determine the most appropriate markers. Consequently, the current priority in this field is to standardize liquid biopsy-based techniques. This review will help to improve understanding of the biology of PCa, particularly the recently defined condition known as "oligometastatic PCa". The presented review of the body of evidence suggests that additional research in molecular biology may help to establish novel treatments for oligometastatic PCa. In the near future, the treatment of PCa will require an interdisciplinary approach involving active cooperation among clinicians, physicians, and biologists.

Developing RNA aptamers for potential treatment of neurological diseases

AMPA receptor antagonists are drug candidates for potential treatment of a number of CNS diseases that involve excessive receptor activation. To date, small-molecule compounds are the dominating drug candidates in the field. However, lower potency, cross activity and poor water solubility are generally associated with these compounds. Here we show the potential of RNA-based antagonists or RNA aptamers as drug candidates and some strategies to discover these aptamers from a random sequence library (∼10¹⁴ sequences). As an alternative to small molecule compounds, our aptamers exhibit higher potency and selectivity toward AMPA receptors. Because aptamers are RNA molecules, they are naturally water soluble. We also discuss the major challenges of translating RNA aptamers as lead molecules into drugs/treatment options.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Analysis of protein expression in glioma is relevant for several aspects in the study of its pathology. Numerous proteins have been described as biomarkers with applications in diagnosis, prognosis, classification, state of tumor progression, and cell differentiation state. These analyses of biomarkers are also useful to characterize tumor neurospheres (NS) generated from glioma patients and glioma models. Tumor NS provide a valuable in vitro model to assess different features of the tumor from which they are derived and can more accurately mirror glioma biology. Here we describe a detailed method to analyze biomarkers in tumor NS using immunohistochemistry (IHC) on paraffin-embedded tumor NS.

Extraction of DNA from complex biological sample matrices using guanidinium ionic liquid modified magnetic nanocomposites

A series of guanidinium ionic liquid modified magnetic chitosan/graphene oxide (GIL-MCGO) nanocomposites have been prepared for DNA extraction via magnetic solid-phase extraction technology. These nanocomposites are of only 20 nanometers in diameter. Single stranded DNA or DNA sodium salts that were absorbed by GIL-MCGO could be quickly collected by an external magnet and extracted. The DNA extraction efficiency of 11 GIL-MCGO nanocomposites was evaluated using NanoDrop. Factors that could impact the DNA extraction process, such as pH, temperature, extraction time, and ionic strength were systematically investigated via single-factor experimental analysis. Under the optimum extraction conditions, a maximum DNA extraction capacity of 233.0 ± 0.4 mg g⁻¹ of GIL-MCGO nanocomposite was achieved. The solid phase extraction method based on GIL-MCGO nanocomposites has been demonstrated with the extraction of DNA from a series of complex sample matrices, including single stranded DNA samples, salmon sperm DNA sodium salt, human whole blood and E. coli cell lysate. The DNA extracted by using the GIL-MCGO nanocomposites are well suitable for PCR amplifications. In addition, an initial study on the interaction between GIL-MCGO and DNA was conducted: the preference of GIL-MCGO on DNA absorption with varying base composition was tested. Only a slight loss in the DNA extraction efficiency of GIL-MCGO was observed after four extraction–desorption cycles, proving excellent regeneration performance and recyclability of the GIL-MCGO nanocomposites in the DNA extraction process.

The DNA extracted from biological samples by using the GIL-MCGO nanocomposites are well suitable for PCR amplifications.

Systematic identification of lincRNA-based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma

Copy number alterations (CNAs) of lincRNAs act as one of important mechanisms in disrupting lincRNA expression which may play critical roles during tumorigenesis in lung adenocarcinoma (LUAD). The copy number alterations of lincRNAs can mark the spectrum of cancer progression and may serve as biomarkers for prognosis in LUAD, however it is rarely studied. We analyzed RNASeq data for 488 LUAD patients from TCGA portal and 58 healthy subjects to identify prognostic lincRNAs predictive of patient survival. Computational analysis entailing integration of expression and copy number alteration data revealed five prognostic lincRNAs: RBPMS-AS1, TDRKH-AS1, LINC00578, RP11-470 M17.2 and LINC00941. The copy number alterations in the LINC00578 and RP11-470 M17.2 genes were positively associated with the longer overall survival of LUAD patients. The CNA in LINC00941 was negatively associated with the longer overall survival. Copy number amplification significantly correlated with increased expression of TDRKH-AS1, which regulates telomere organization and EZH2-mediated epigenetic silencing of CDKN1A, CDKN1B and IL24. Decreased survival of LUAD patients was associated with high LINC00941 expression. The LINC00941 regulates the PI3K-AKT signaling pathway, focal adhesion by influencing potential targets, such as KRAS proto-oncogene GTPase and VEGFC. These lincRNA-based prognostic biomarkers may destroy important cancer-related biological processes contributing to LUAD prognosis. In summary, we demonstrate the prognostic potential of four differentially expressed lincRNAs with copy number alterations (RBPMS-AS1, TDRKH-AS1, LINC00578 and RP11-470 M17.2) that are positively associated with longer overall survival of LUAD patients. One differentially expressed lincRNA LINC00941 with copy number alterations was negatively associated with longer overall survival of LUAD patients.

Nucleic Acid Detection by a Target-Assisted Proximity Proteolysis Reaction

Nucleic acid analysis plays an important role in diagnosing diseases as well as understanding biology. Despite advances in technology, there is still a need to develop a rapid and simple method to detect specific nucleic acids, especially in remote locations and low-resource cases. Here, we proposed a proximity proteolysis reaction in which the reaction between protease and zymogen is enhanced in the presence of a target molecule. The pair of proteins was site-specifically modified with oligonucleotides, and the conjugates were used to develop a method of detecting nucleic acids. Target DNA and RNA could be detected in less than 1 h at sub-nanomolar concentrations based on an absorbance signal. The assay method was resistant to interference by biological matrixes, and its sensitivity could be improved when combined with an isothermal nucleic acid amplification method. The results demonstrated the feasibility of this proximity proteolysis reaction as a new platform technology for detecting specific nucleic acid sequences.

Establishing a stable, repeatable platform for measuring changes in sperm DNA methylation

BACKGROUND

Several independent research groups have shown that alterations in human sperm methylation profiles correlate with decreased fecundity and an increased risk of poor embryo development. Moving these initial findings from the lab into a clinical setting where they can be used to measure male infertility though requires a platform that is stable and robust against batch effects that can occur between sample runs. Operating parameters must be established, performance characteristics determined, and guidelines set to ensure repeatability and accuracy. The standard for technical validation of a lab developed test (LDT) in the USA comes from the Clinical Laboratory Improvement Amendments (CLIA). However, CLIA was introduced in 1988, before the advent of genome-wide profiling and associated computational analysis. This, coupled with its intentionally general nature, makes its interpretation for epigenetic assays non-trivial.

RESULTS

Here, we present an interpretation of the CLIA technical validation requirements for profiling DNA methylation and calling aberrant methylation using the Illumina Infinium platform (e.g., the 450HM and MethylationEPIC). We describe an experimental design to meet these requirements, the experimental results obtained, and the operating parameters established.

CONCLUSIONS

The CLIA guidelines, although not intended for high-throughput assays, can be interpreted in a way that is consistent with modern epigenetic assays. Based on such an interoperation, Illumina's Infinium platform is quite amenable to usage in a clinical setting for diagnostic work.

Behavior change is not one size fits all: psychosocial phenotypes of childhood obesity prevention intervention participants

Variability in individuals' responses to interventions may contribute to small average treatment effects of childhood obesity prevention interventions. But, neither the causes of this individual variability nor the mechanism by which it influences behavior are clear. We used qualitative methods to characterize variability in students' responses to participating in a childhood obesity prevention intervention and psychosocial characteristics related to the behavior change process. We interviewed 18 students participating in a school-based curriculum and policy behavior change intervention. Descriptive coding, summary, and case-ordered descriptive meta-matrices were used to group participants by their psychosocial responses to the intervention and associated behavior changes. Four psychosocial phenotypes of responses emerged: (a) Activated-successful behavior-changers with strong internal supports; (b) Inspired-motivated, but not fully successful behavior-changers with some internal supports, whose taste preferences and food environment overwhelmed their motivation; (c) Reinforced-already practiced target behaviors, were motivated, and had strong family support; and (d) Indifferent-uninterested in behavior change and only did target behaviors if family insisted. Our findings contribute to the field of behavioral medicine by suggesting the presence of specific subgroups of participants who respond differently to behavior change interventions and salient psychosocial characteristics that differentiate among these phenotypes. Future research should examine the utility of prospectively identifying psychosocial phenotypes for improving the tailoring of nutrition behavior change interventions.

Development and evaluation of a multimodal marker of major depressive disorder

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R² values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

NiO Nanoparticles for Exceptionally Stable DNA Adsorption and Its Extraction from Biological Fluids

Selective extraction of a small amount of nucleic acids from complex biological samples containing a high concentration of proteins is critical for bioanalytical chemistry. A number of previously published studies have focused on long, double-stranded DNA such as plasmid DNA. On the other hand, we are interested in short oligonucleotides. Nucleic acids have a negatively charged phosphate backbone that interacts with metal oxides strongly, and this may be used to distinguish them from proteins. In this work, a few metal oxide nanoparticles were screened, including NiO, CoO, ZnO, TiO₂, CeO₂, and Fe₃O₄ for DNA recovery. NiO had the highest DNA adsorption efficiency from mixtures containing bovine serum albumin or human blood serum. The adsorption of DNA by NiO was further characterized as a function of the pH, salt concentration, DNA length, and DNA sequence. The adsorption mechanism was studied by adding competing chemicals or denaturing agents. A striking observation was the extremely high adsorption affinity of NiO, much higher than that of the other tested oxides. Polyphosphate was the most effective agent for displacing adsorbed DNA, whereas simple inorganic phosphate was less effective. NiO was able to concentrate DNA from a serum mixture by 33- to 55-fold, depending on the serum concentration. NiO is thus a promising candidate for extracting DNA from biological samples.