A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

INTRODUCTION/OBJECTIVE: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. METHODS: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. RESULTS: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). CONCLUSIONS: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

Prospective observational study to validate a next-generation sequencing blood RNA signature to predict early kidney transplant rejection.

The objective of this study was to validate the performance of Tutivia, a peripheral blood gene expression signature, in predicting early acute rejection (AR) post-kidney transplant. Recipients of living or deceased donor kidney transplants were enrolled in a nonrandomized, prospective, global, and observational study (NCT04727788). The main outcome was validation of the area under the curve (AUC) of Tutivia vs serum creatinine at biopsy alone, or Tutivia + serum creatinine at biopsy. Of the 151 kidney transplant recipients, the mean cohort age was 53 years old, and 64% were male. There were 71% (107/151) surveillance/protocol biopsies and 29% (44/151) for-cause biopsies, with a 31% (47/151) overall rejection rate. Tutivia (AUC 0.69 [95% CI: 0.59-0.77]) and AUC of Tutivia + creatinine at biopsy (0.68 [95% CI: 0.59-0.77]) were greater than the AUC of creatinine at biopsy alone (0.51.4 [95% CI: 0.43-0.60]). Applying a model cut-off of 50 (scale 0-100) generated a high- and low-risk category for AR with a negative predictive value of 0.79 (95% CI: 0.71-0.86), a positive predictive value of 0.60 (95% CI: 0.45-0.74), and an odds ratio of 5.74 (95% CI: 2.63-12.54). Tutivia represents a validated noninvasive approach for clinicians to accurately predict early AR, beyond the current standard of care.

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer.

BACKGROUND: PreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. MATERIALS AND METHODS: Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. RESULTS: Analytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. CONCLUSION: In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.

Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression.

AIMS: To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. METHODS: We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up. RESULTS: In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively. CONCLUSIONS: Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.

The RNA-binding protein LRPPRC promotes resistance to CDK4/6 inhibition in lung cancer.

Kinase inhibitors against Cyclin Dependent Kinase 4 and 6 (CDK4/6i) are promising cancer therapeutic drugs. However, their effects are limited by primary or acquired resistance in virtually all tumor types. Here, we demonstrate that Leucine Rich Pentatricopeptide Repeat Containing (LRPPRC) controls CDK4/6i response in lung cancer by forming a feedback loop with CDK6. LRPPRC binds to CDK6-mRNA, increasing the stability and expression of CDK6. CDK6 and its downstream E2F Transcription Factor 1 (E2F1), bind to the LRPPRC promoter and elevate LRPPRC transcription. The activation of the LRPPRC-CDK6 loop facilitates cell cycle G1/S transition, oxidative phosphorylation, and cancer stem cell generation. Gossypol acetate (GAA), a gynecological medicine that has been repurposed as a degrader of LRPPRC, enhances the CDK4/6i sensitivity in vitro and in vivo. Our study reveals a mechanism responsible for CDK4/6i resistance and provides an enlightening approach to investigating the combinations of CDK4/6 and LRPPRC inhibitors in cancer therapy.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules.

PURPOSE: Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. METHODS: LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. RESULTS: Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). CONCLUSION: Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

ExoDx prostate test as a predictor of outcomes of high-grade prostate cancer - an interim analysis.

BACKGROUND: Patient outcomes were assessed based on a pre-biopsy ExoDx Prostate (EPI) score at 2.5 years of the 5-year follow-up of ongoing prostate biopsy Decision Impact Trial of the ExoDx Prostate (IntelliScore). METHODS: Prospective, blinded, randomized, multisite clinical utility study was conducted from June 2017 to May 2018 (NCT03235687). Urine samples were collected from 1049 men (≥50 years old) with a PSA 2-10 ng/mL being considered for a prostate biopsy. Patients were randomized to EPI vs. standard of care (SOC). All had an EPI test, but only EPI arm received results during biopsy decision process. Clinical outcomes, time to biopsy and pathology were assessed among low (<15.6) or high (≥15.6) EPI scores. RESULTS: At 2.5 years, 833 patients had follow-up data. In the EPI arm, biopsy rates remained lower for low-risk EPI scores than high-risk EPI scores (44.6% vs 79.0%, p < 0.001), whereas biopsy rates were identical in SOC arm regardless of EPI score (59.6% vs 58.8%, p = 0.99). Also in the EPI arm, the average time from EPI testing to first biopsy was longer for low-risk EPI scores compared to high-risk EPI scores (216 vs. 69 days; p < 0.001). Similarly, the time to first biopsy was longer with EPI low-risk scores in EPI arm compared to EPI low-risk scores in SOC arm (216 vs 80 days; p < 0.001). At 2.5 years, patients with low-risk EPI scores from both arms had less HGPC than high-risk EPI score patients (7.9% vs 26.8%, p < 0.001) and the EPI arm found 21.8% more HGPC than the SOC arm. CONCLUSIONS: This follow-up analysis captures subsequent biopsy outcomes and demonstrates that men receiving EPI low-risk scores (<15.6) significantly defer the time to first biopsy and remain at a very low pathologic risk by 2.5-years after the initial study. The EPI test risk stratification identified low-risk patients that were not found with the SOC.

Water exchange rates measure active transport and homeostasis in neural tissue.

For its size, the brain is the most metabolically active organ in the body. Most of its energy demand is used to maintain stable homeostatic physiological conditions. Altered homeostasis and active states are hallmarks of many diseases and disorders. Yet there is currently no direct and reliable method to assess homeostasis and absolute basal activity of cells in the tissue noninvasively without exogenous tracers or contrast agents. We propose a novel low-field, high-gradient diffusion exchange nuclear magnetic resonance (NMR) method capable of directly measuring cellular metabolic activity via the rate constant for water exchange across cell membranes. Exchange rates are 140 ± 16 s - 1 under normal conditions in viable ex vivo neonatal mouse spinal cords. High repeatability across samples suggest that values are absolute and intrinsic to the tissue. Using temperature and drug (ouabain) perturbations, we find that the majority of water exchange is metabolically active and coupled to active transport by the sodium-potassium pump. We show that this water exchange rate is sensitive primarily to tissue homeostasis and provides distinct functional information. In contrast, the apparent diffusion coefficient (ADC) measured with submillisecond diffusion times is sensitive primarily to tissue microstructure but not activity. Water exchange appears independently regulated from microstructural and oxygenation changes reported by ADC and T 1 relaxation measurements in an oxygen-glucose deprivation model of stroke; exchange rates remain stable for 30-40 min before dropping to levels similar to the effect of ouabain and never completely recovering when oxygen and glucose are restored.

Cost-effectiveness analysis of LungLB for the clinical management of patients with indeterminate pulmonary nodules.

BACKGROUND: There is currently a need for additional diagnostic information to help guide treatment decisions and to properly determine the best treatment pathway for patients identified with indeterminate pulmonary nodules (IPNs). The aim of this study was to demonstrate the incremental cost-effectiveness of LungLB compared to the current clinical diagnostic pathway (CDP) in the management of patients with IPNs, from a US payer's perspective. METHODS: A decision tree and Markov model hybrid was chosen from a payer perspective in the US setting, based on published literature, to assess the incremental cost-effectiveness of LungLB compared to the current CDP in the management of patients with IPNs. Primary endpoints of the analysis include expected costs, life years (LYs), and quality-adjusted life years (QALYs) for each arm of the model, as well as an incremental cost-effectiveness ratio (ICER), which is calculated as the incremental costs per QALY, and net monetary benefit (NMB). RESULTS: We find that, with the inclusion of LungLB to the current CDP diagnostic pathway, expected LYs over the typical patient's lifespan increase by 0.07 years and QALYs increase by 0.06. The average patient in the CDP arm will pay approximately $44,310 over their lifespan, while a patient in the LungLB arm will pay $48,492, resulting in a difference of $4,182. The differentials between the CDP and LungLB arms of the model in costs and QALYs yield an ICER of $75,740 per QALY and an incremental NMB of $1,339. CONCLUSION: This analysis provides evidence that LungLB, in conjunction with CDP, is a cost-effective alternative compared to the current CDP alone in a US setting for individuals with IPNs.

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years.

BACKGROUND: Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence. METHODS: In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis. RESULTS: The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001. CONCLUSIONS: PDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.

Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes.

INTRODUCTION AND OBJECTIVE: The lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study. METHODS: KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes. RESULTS: A total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m2, urine albumin-creatinine ratio was 69 mg/g, and HbA1c was 7.7%. After testing, a clinical encounter in the first month occurred in 13%, 43%, and 53% of low-risk, intermediate-risk, and high-risk patients, respectively and 46%, 61%, and 71% had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on SGLT2 inhibitors (OR = 4.56; 95% CI 3.00-6.91 vs low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95% CI 1.53-4.01) compared to low-risk patients. CONCLUSIONS: The combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care.

Barrow Innovation Center: A 5-Year Update and Future Direction.

OBJECTIVE: The rich history of neurosurgical innovation served as a model for the Barrow Innovation Center's establishment in 2016. The center's accomplishments are summarized in hopes of fostering the development of similar centers and initiatives within the neurosurgical and broader medical community. METHODS: A retrospective review (January 2016-July 2021) of patent filings, project proposals, and funding history was used to generate the data presented in this operational review. RESULTS: Through the 5-year period of analysis, 55 prior art searches were conducted on new patentable ideas. A total of 87 provisional patents, 25 Patent Cooperation Treaty applications, and 48 national stage filings were submitted. In partnership with Arizona State University, the University of Arizona, California Polytechnic State University, and Texas A&M University, a total of 27 multidisciplinary projects were conducted with input from multispecialty engineers and scientists. These efforts translated into 1 startup company and 2 licensed patents to commercial companies, with most remaining ideas and project efforts awaiting interest from industry. CONCLUSIONS: The multidisciplinary collaborative environment embodied by the Barrow Innovation Center has revolutionized the innovative and entrepreneurial environment of its home institution and enabled neurosurgical residents to get a unique educational experience within the realm of innovation. The bottleneck within the workflow of ideas from conception to commercialization appears to be the establishment of commercial partners; therefore, future efforts within the center will be to establish a panel of industry partnerships to enhance the exposure of ideas to interested companies.

Motoneuronal Regulation of Central Pattern Generator and Network Function.

This chapter reviews recent work showing that vertebrate motoneurons can trigger spontaneous rhythmic activity in the developing spinal cord and can modulate the function of several different central pattern generators later in development. In both the embryonic chick and the fetal mouse spinal cords, antidromic activation of motoneurons can trigger bouts of rhythmic activity. In the neonatal mouse, optogenetic manipulation of motoneuron firing can modulate the frequency of fictive locomotion activated by a drug cocktail. In adult animals, motoneurons have been shown to regulate swimming in the zebrafish, and vocalization in fish and frogs. We discuss the significance of these findings and the degree to which motoneurons may be considered a part of these central pattern generators.

Predicting high-grade prostate cancer at initial biopsy: clinical performance of the ExoDx (EPI) Prostate Intelliscore test in three independent prospective studies.

BACKGROUND: The ability to discriminate indolent from clinically significant prostate cancer (PC) at the initial biopsy remains a challenge. The ExoDx Prostate (IntelliScore) (EPI) test is a noninvasive liquid biopsy that quantifies three RNA targets in urine exosomes. The EPI test stratifies patients for risk of high-grade prostate cancer (HGPC; ≥ Grade Group 2 [GG] PC) in men ≥ 50 years with equivocal prostate-specific antigen (PSA) (2-10 ng/mL). Here, we present a pooled meta-analysis from three independent prospective-validation studies in men presenting for initial biopsy decision. METHODS: Pooled data from two prospective multi-site validation studies and the control arm of a clinical utility study were analyzed. Performance was evaluated using the area under the receiver-operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity for discriminating ≥ GG2 from GG1 and benign pathology. RESULTS: The combined cohort (n = 1212) of initial-biopsy subjects had a median age of 63 years and median PSA of 5.2 ng/mL. The EPI AUC (0.70) was superior to PSA (0.56), Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) (0.62), and The European Randomized Study of Screening for Prostate Cancer (ERSPC) (0.59), (all p-values <0.001) for discriminating GG2 from GG1 and benign histology. The validated cutoff of 15.6 would avoid 23% of all prostate biopsies and 30% of "unnecessary" (benign or Gleason 6/GG1) biopsies, with an NPV of 90%. CONCLUSIONS: EPI is a noninvasive, easy-to-use, urine exosome-RNA assay that has been validated across 3 independent prospective multicenter clinical trials with 1212 subjects. The test can discriminate high-grade (≥GG2) from low-grade (GG1) cancer and benign disease. EPI effectively guides the biopsy-decision process independent of PSA and other standard-of-care factors.

Engineering DNA on the Surface of Upconversion Nanoparticles for Bioanalysis and Therapeutics.

Surface modification of inorganic nanomaterials with biomolecules has enabled the development of composites integrated with extensive properties. Lanthanide ion-doped upconversion nanoparticles (UCNPs) are one class of inorganic nanomaterials showing optical properties that convert photons of lower energy into higher energy. Additionally, DNA oligonucleotides have exhibited powerful capabilities for organizing various nanomaterials with versatile topological configurations. Through rational design and nanotechnology, DNA-based UCNPs offer predesigned functionality and potential. To fully harness the capabilities of UCNPs integrated with DNA, various DNA-UCNP composites have been developed for diagnosis and therapeutics. In this review, beginning with the introduction of the UCNPs and the conjugation of DNA strands on the surface of UCNPs, we present an overview of the recent progress of DNA-UCNP composites while focusing on their applications for bioanalysis and therapeutics.

Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease.

BACKGROUND: The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes (T2D) and early-stage chronic kidney disease (CKD). The following studies describe the analytical validation of the KidneyIntelX assay including impact of observed methodologic variability on the composite risk score. METHODS: Analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2) based on Clinical Laboratory Standards Institute (CLSI) guidelines. Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. Analysis of cross-reactivity and interfering substances was also performed. RESULTS: Assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. Mean within-laboratory imprecision coefficient of variation (CV) was established as less than 9% across all assays in a multi-lot study. The linear range of the assays was determined as 12-5807 pg/mL, 969-23,806 pg/mL and 4256-68,087 pg/mL for KIM-1, sTNFR-1 and sTNFR-2, respectively. The average risk score CV% was less than 5%, with 98% concordance observed for assignment of risk categories. Cross-reactivity between critical assay components in a multiplexed format did not exceed 1.1%. CONCLUSIONS: The set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. Notably, reproducibility of the composite risk score demonstrated that expected analytical laboratory variation did not impact the assigned risk category, and therefore, the clinical validity of the reported results.

Optogenetic Activation of V1 Interneurons Reveals the Multimodality of Spinal Locomotor Networks in the Neonatal Mouse.

In the spinal cord, classes of interneurons have been studied in vitro to determine their role in producing or regulating locomotion. It is unclear whether all locomotor behaviors are produced by the same circuitry or engage different subsets of neurons. Here, in neonatal mice of either sex, we test this idea by comparing the actions of a class of spinal, inhibitory interneuron (V1) expressing channelrhodopsin driven by the engrailed-1 transcription factor on the rhythms elicited by different methods. We find that, although the overall locomotor activities in vitro are similar, V1 interneuron depolarization produces opposite effects depending of the mode of activation of the locomotor circuitry. The differential behavior of V1 neurons suggests that their function depends on how the locomotor rhythm is activated and is consistent with the idea that the functional organization of the corresponding locomotor networks also differs.SIGNIFICANCE STATEMENT The neural networks dictating the execution of fictive locomotion are located in the spinal cord. It is generally assumed that the mode of activation of these spinal networks should not change the recruitment or function of neurons. Here, we manipulated the activity of a class of interneuron (V1), which targets these networks and found that their activation induces opposite effects depending on the mode of activation. This suggests that the mode of activation of the spinal networks differentially recruits either V1 interneurons or other interneurons, or both.

Payer budget impact of an artificial intelligence in vitro diagnostic to modify diabetic kidney disease progression.

AIM: To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic to predict kidney function decline in Type 2 Diabetic Kidney Disease (T2DKD) patients, stages 1-3b. MATERIALS AND METHODS: We developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with the use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stages 1-3b T2DKD patients was followed for 5 years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions. RESULTS: Projected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year 2 after adoption. Sensitivity analysis based on the assessment of the most conservative definition of progression and a 5% reduction in progression rate attributed to KidneyIntelX, resulted in a projected 5-year savings of $145 million associated with KidneyIntelX. LIMITATIONS AND CONCLUSIONS: Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.

In vivo activation of pH-responsive oxidase-like graphitic nanozymes for selective killing of Helicobacter pylori.

Helicobacter pylori infection is a major etiological factor in gastric diseases. However, clinical antibiotic therapy for H. pylori is limited by continuously decreased therapeutic efficacy and side effects to symbiotic bacteria. Herein, we develop an in vivo activatable pH-responsive graphitic nanozyme, PtCo@Graphene (PtCo@G), for selective treatment of H. pylori. Such nanozymes can resist gastric acid corrosion, exhibit oxidase-like activity to stably generate reactive oxygen species only in acidic gastric milieu and demonstrate superior selective bactericidal property. C18-PEGn-Benzeneboronic acid molecules are modified on PtCo@G, improving its targeting capability. Under acidic gastric pH, graphitic nanozymes show notable bactericidal activity toward H. pylori, while no bacterial killing is observed under intestinal conditions. In mouse model, high antibacterial capability toward H. pylori and negligible side effects toward normal tissues and symbiotic bacteria are achieved. Graphitic nanozyme displays the desired enzyme-like activities at corresponding physiological sites and may address critical issues in clinical treatment of H. pylori infections.