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.

The transcription factor IRF8 activates integrin-mediated TGF-ß signaling and promotes neuroinflammation.

Recent epidemiological studies have identified interferon regulatory factor 8 (IRF8) as a susceptibility factor for multiple sclerosis (MS). However, how IRF8 influences the neuroinflammatory disease has remained unknown. By studying the role of IRF8 in experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, we found that Irf8(-/-) mice are resistant to EAE. Furthermore, expression of IRF8 in antigen-presenting cells (APCs, such as macrophages, dendritic cells, and microglia), but not in T cells, facilitated disease onset and progression through multiple pathways. IRF8 enhanced αvß8 integrin expression in APCs and activated TGF-ß signaling leading to T helper 17 (Th17) cell differentiation. IRF8 induced a cytokine milieu that favored growth and maintenance of Th1 and Th17 cells, by stimulating interleukin-12 (IL-12) and IL-23 production, but inhibiting IL-27 during EAE. Finally, IRF8 activated microglia and exacerbated neuroinflammation. Together, this work provides mechanistic bases by which IRF8 contributes to the pathogenesis of MS.

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 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.

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.

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.

V1 interneurons regulate the pattern and frequency of locomotor-like activity in the neonatal mouse spinal cord.

In the mouse spinal cord, V1 interneurons are a heterogeneous population of inhibitory spinal interneurons that have been implicated in regulating the frequency of the locomotor rhythm and in organizing flexor and extensor alternation. By introducing archaerhodopsin into engrailed-1-positive neurons, we demonstrate that the function of V1 neurons in locomotor-like activity is more complex than previously thought. In the whole cord, V1 hyperpolarization increased the rhythmic synaptic drive to flexor and extensor motoneurons, increased the spiking in each cycle, and slowed the locomotor-like rhythm. In the hemicord, V1 hyperpolarization accelerated the rhythm after an initial period of tonic activity, implying that a subset of V1 neurons are active in the hemicord, which was confirmed by calcium imaging. Hyperpolarizing V1 neurons resulted in an equalization of the duty cycle in flexor and extensors from an asymmetrical pattern in control recordings in which the extensor bursts were longer than the flexor bursts. Our results suggest that V1 interneurons are composed of several subsets with different functional roles. Furthermore, during V1 hyperpolarization, the default state of the locomotor central pattern generator (CPG) is symmetrical, with antagonist motoneurons each firing with an approximately 50% duty cycle. We hypothesize that one function of the V1 population is to set the burst durations of muscles to be appropriate to their biomechanical function and to adapt to the environmental demands, such as changes in locomotor speed.

Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.

AIM: Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. METHODS: This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years. RESULTS: In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n = 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p < 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p < 0.05). CONCLUSIONS: KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

The regulation of NONO by USP11 via deubiquitination is linked to the proliferation of melanoma cells.

Ubiquitin-specific protease 11 (USP11) has been implicated in the regulation of DNA repair, apoptosis, signal transduction and cell cycle. It belongs to a USP subfamily of deubiquitinases. Although previous research has shown that USP11 overexpression is frequently found in melanoma and is correlated with a poor prognosis, the potential molecular mechanism of USP11 in melanoma remains indefinitive. Here, we report that USP11 and NONO colocalize and interact with each other in the nucleus of melanoma cells. As a result, the knockdown of USP11 decreases NONO levels. Whereas, overexpression of USP11 increases NONO levels in a dose-dependent manner. Furthermore, we reveal that USP11 protects NONO protein from proteasome-mediated degradation by removing poly-ubiquitin chains conjugated onto NONO. Functionally, USP11 mediated melanoma cell proliferation via the regulation of NONO levels because ablation of USP11 inhibits the proliferation which could be rescued by ectopic expression of NONO protein. Moreover, a significant positive correlation between USP11 and NONO concentrations was found in clinical melanoma samples. Collectively, these results demonstrate that USP11 is a new deubiquitinase of NONO and that the signalling axis of USP11-NONO is significantly involved in melanoma proliferation.

In Situ Gastric pH Imaging with Hydrogel Capsule Isolated Paramagnetic Metallo-albumin Complexes.

Abnormal gastric pH (pH > 3) has instructive significance for early diagnosis of various diseases, including cancer. However, for low patient compliance, limited penetration depth, high dependence on physiological function or unsafety issue, in situ noninvasive monitoring gastric pH is challenged. Herein, we developed a hydrogel capsule isolated human serum albumin-manganese complex (HSA-Mn) for in situ magnetic resonance imaging (MRI) gastric pH monitoring for the first time. In this strategy, the rotation motion restriction of Mn2+ after binding to HSA significantly increased the R1 (longitudinal relaxation rate) signal, and its high correlation with protonation imparted the HSA-Mn system sensitive responsiveness to varying pH (R1(pH 7)/R1(pH 1) = 8.2). Moreover, a screw jointed hydrogel capsule with signal confinement and internal standard abilities was designed. Such a nanoporous hydrogel capsule with size selectivity to surrounding molecules enabled a stable and sensitive response to different pH simulated gastric fluid within 0.5 h. In addition, with the unique structural outline and stable MRI characteristics, the capsule could also work as an internal standard, which facilitates the collection of signals and trace of the capsule in vivo. Through validating in a rabbit model, the precise abnormal gastric pH recognition capacity of the HSA-Mn hydrogel capsule was amply confirmed. Hence, the hydrogel capsule isolated HSA-Mn system strategy with great biocompatibility could be expected to be a potent tool for in situ anti-disturbance MRI of gastric pH in future clinical application.

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.

A first-in-human proof-of-concept trial of intravaginal artesunate to treat cervical intraepithelial neoplasia 2/3 (CIN2/3).

OBJECTIVE: Most treatment options for cervical intraepithelial neoplasia 2/3 (CIN2/3) are either excisional or ablative, and require sequential visits to health care providers. Artesunate, a compound that is WHO-approved for treatment of acute malaria, also has cytotoxic effect on squamous cells transformed by HPV. We conducted a first-in-human Phase I dose-escalation study to assess the safety and efficacy of self-administered artesunate vaginal inserts in biopsy-confirmed CIN2/3. METHODS: Safety analyses were based on patients who received at least one dose, and were assessed by the severity, frequency, and duration of reported adverse events. Tolerability was assessed as the percentage of subjects able to complete their designated dosing regimen. Modified intention-to-treat analyses for efficacy and viral clearance were based on patients who received at least one dose for whom endpoint data were available. Efficacy was defined as histologic regression to CIN1 or less. Viral clearance was defined as absence of HPV genotoype (s) detected at baseline. RESULTS: A total of 28 patients received 1, 2, or 3 five-day treatment cycles at study weeks 0, 2, and 4, respectively, prior to a planned, standard-of-care resection at study week 15. Reported adverse events were mild, and self-limited. In the modified intention-to-treat analysis, histologic regression was observed in 19/28 (67.9%) subjects. Clearance of HPV genotypes detected at baseline occurred in 9 of the 19 (47.4%) subjects whose lesions underwent histologic regression. CONCLUSIONS: Self-administered vaginal artesunate inserts were safe and well-tolerated, at clinically effective doses to treat CIN2/3. These findings support proceeding with Phase II clinical studies.

A urine-based Exosomal gene expression test stratifies risk of high-grade prostate Cancer in men with prior negative prostate biopsy undergoing repeat biopsy.

BACKGROUND: Initial prostate biopsy often fails to identify prostate cancer resulting in patient anxiety, especially when clinical features such as prostate specific antigen (PSA) remain elevated, leading to the need for repeat biopsies. Prostate biomarker tests, such as the ExoDx™ Prostate (IntelliScore), or EPI test, have been shown to provide individualized risk assessment of clinically significant prostate cancer at initial biopsy; however, the performance in the repeat biopsy setting is not well established. METHODS: As part of a previous prospective clinical validation study evaluating the performance of the EPI test, we collected first-catch, non-DRE urine samples across 22 sites from men with at least one prior negative biopsy scheduled to undergo a repeat prostate biopsy to rule out prostate cancer. All men were 50 years or older with a PSA 2-10 ng/mL. Exosomal mRNA was extracted and expression of three genomic markers, PCA3, ERG and SPDEF was measured. The resulting EPI score was correlated with biopsy results. RESULTS: 229 men with a prior negative biopsy underwent repeat biopsies. ExoDx Prostate demonstrated good performance ruling out high-grade (Grade group 2, GG2, or higher) prostate cancer (HGPCa) using the previously validated 15.6 cut point in the initial biopsy setting. The EPI test yielded an NPV of 92% independent of other clinical features and would have avoided 26% of unnecessary biopsies while missing only five patients with HGPCa (2.1%). Furthermore, the EPI test provided additional information at a cut-point of 20 and 29.6 with an NPV of 94%, potentially delaying 35 and 61% of unnecessary biopsies, respectively. AUC curves and Net Health Benefit Analyses demonstrated superior performance of ExoDx Prostate over PSA and clinical only risk calculators, i.e. ERSPC. CONCLUSIONS: The EPI test provided good performance using the 15.6 cut-point for ruling out HGPCa / GG2 or higher in men undergoing a repeat prostate biopsy with a PSA of 2-10 ng/ml. Furthermore, the test utilizes gene expression data independent of clinical features to predict the likelihood of HGPCa / GG2 on a subsequent needle biopsy.

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.

Indispensable Amino Acid-Deficient Diets Induce Seizures in Ketogenic Diet-Fed Rodents, Demonstrating a Role for Amino Acid Balance in Dietary Treatments for Epilepsy.

Background: Low protein amounts are used in ketogenic diets (KDs), where an essential (indispensable) amino acid (IAA) can become limiting. Because the chemically sensitive, seizurogenic, anterior piriform cortex (APC) is excited by IAA limitation, an imbalanced KD could exacerbate seizure activity. Objective: We questioned whether dietary IAA depletion worsens seizure activity in rodents fed KDs. Methods: In a series of 6 trials, male rats or gerbils of both sexes (6-8/group) were given either control diets (CDs) appropriate for each trial, a KD, or a threonine-devoid (ThrDev) diet for ≥7 d, and tested for seizures using various stimuli. Microchip analysis of rat APCs was also used to determine if changes in transcripts for structures relevant to seizurogenesis are affected by a ThrDev diet. Glutamate release was measured in microdialysis samples from APCs during the first meal after 7 d on a CD or a ThrDev diet. Results: Adult rats showed increased susceptibility to seizures in both chemical (58%) and electroshock (doubled) testing after 7 d on a ThrDev diet compared with CD (each trial, P ≤ 0.05). Seizure-prone Mongolian gerbils had fewer seizures after receiving a KD, but exacerbated seizures (68%) after 1 meal of KD minus Thr (KD-T compared with CD, P < 0.05). In kindled rats fed KD-T, both counts (19%) and severities (77%) of seizures were significantly elevated (KD-T compared with CD, P < 0.05). Gene transcript changes were consistent with enhanced seizure susceptibility (7-21 net-fold increases, P = 0.045-0.001) and glutamate release into the APC was increased acutely (4-fold at 20 min, 2.6-fold at 60 min, P < 0.05) after 7 d on a ThrDev diet. Conclusion: Seizure severity in rats and gerbils was reduced after KDs and exacerbated by ThrDev, both in KD- and CD-fed animals, consistent with the mechanistic studies. We suggest that a complete protein profile in KDs may improve IAA balance in the APC, thereby lowering the risk of seizures.

Programmable and multiparameter DNA-based logic platform for cancer recognition and targeted therapy.

The specific inventory of molecules on diseased cell surfaces (e.g., cancer cells) provides clinicians an opportunity for accurate diagnosis and intervention. With the discovery of panels of cancer markers, carrying out analyses of multiple cell-surface markers is conceivable. As a trial to accomplish this, we have recently designed a DNA-based device that is capable of performing autonomous logic-based analysis of two or three cancer cell-surface markers. Combining the specific target-recognition properties of DNA aptamers with toehold-mediated strand displacement reactions, multicellular marker-based cancer analysis can be realized based on modular AND, OR, and NOT Boolean logic gates. Specifically, we report here a general approach for assembling these modular logic gates to execute programmable and higher-order profiling of multiple coexisting cell-surface markers, including several found on cancer cells, with the capacity to report a diagnostic signal and/or deliver targeted photodynamic therapy. The success of this strategy demonstrates the potential of DNA nanotechnology in facilitating targeted disease diagnosis and effective therapy.

Amyotrophic lateral sclerosis-related VAPB P56S mutation differentially affects the function and survival of corticospinal and spinal motor neurons.

The substitution of Proline with Serine at residue 56 (P56S) of vesicle-associated membrane protein-associated protein B (VAPB) has been linked to an atypical autosomal dominant form of familial amyotrophic lateral sclerosis 8 (ALS8). To investigate the pathogenic mechanism of P56S VAPB in ALS, we generated transgenic (Tg) mice that heterologously express human wild-type (WT) and P56S VAPB under the control of a pan-neuronal promoter Thy1.2. While WT VAPB Tg mice did not exhibit any overt motor behavioral phenotypes, P56S VAPB Tg mice developed progressive hyperactivities and other motor abnormalities. VAPB protein was accumulated as large punctate in the soma and proximal dendrites of both corticospinal motor neurons (CSMNs) and spinal motor neurons (SMNs) in P56S VAPB Tg mice. Concomitantly, a significant increase of endoplasmic reticulum stress and unfolded protein response and the resulting up-regulation of pro-apoptotic factor CCAAT/enhancer-binding protein homologous protein expression were observed in the CSMNs and SMNs of P56S VAPB Tg mice. However, only a progressive loss of CSMNs but not SMNs was found in P56S VAPB Tg mice. In SMNs, P56S VAPB promoted a rather selective translocation of VAPB protein onto the postsynaptic site of C-boutons that altered the morphology of C-boutons and impaired the spontaneous rhythmic discharges of SMNs. Therefore, these findings provide new pathophysiological mechanisms of P56S VAPB that differentially affect the function and survival of CSMNs and SMNs in ALS8.

Characterization of sacral interneurons that mediate activation of locomotor pattern generators by sacrocaudal afferent input.

Identification of the neural pathways involved in retraining the spinal central pattern generators (CPGs) by afferent input in the absence of descending supraspinal control is feasible in isolated rodent spinal cords where the locomotor CPGs are potently activated by sacrocaudal afferent (SCA) input. Here we study the involvement of sacral neurons projecting rostrally through the ventral funiculi (VF) in activation of the CPGs by sensory stimulation. Fluorescent labeling and immunostaining showed that VF neurons are innervated by primary afferents immunoreactive for vesicular glutamate transporters 1 and 2 and by intraspinal neurons. Calcium imaging revealed that 55% of the VF neurons were activated by SCA stimulation. The activity of VF neurons and the sacral and lumbar CPGs was abolished when non-NMDA receptors in the sacral segments were blocked by the antagonist CNQX. When sacral NMDA receptors were blocked by APV, the sacral CPGs were suppressed, VF neurons with nonrhythmic activity were recruited and a moderate-drive locomotor rhythm developed during SCA stimulation. In contrast, when the sacral CPGs were activated by SCA stimulation, rhythmic and nonrhythmic VF neurons were recruited and the locomotor rhythm was most powerful. The activity of 73 and 27% of the rhythmic VF neurons was in-phase with the ipsilateral and contralateral motor output, respectively. Collectively, our studies indicate that sacral VF neurons serve as a major link between SCA and the hindlimb CPGs and that the ability of SCA to induce stepping can be enhanced by the sacral CPGs. The nature of the ascending drive to lumbar CPGs, the identity of subpopulations of VF neurons, and their potential role in activating the locomotor rhythm are discussed.

Genomic analysis in active surveillance: predicting high-risk disease using tissue biomarkers.

PURPOSE OF REVIEW: For patients newly diagnosed with prostate cancer, the most significant question is whether the 'truly malignant' disease has been identified. This review will provide an overview of current prostate cancer genomic and biomarker discovery - validation strategies geared towards identifying aggressive, clinically significant disease at the time of diagnosis. RECENT FINDINGS: Based on recent findings the prostate cancer aggressive disease phenotype develops as a result of mutations (TP53, PTEN), structural events (TMPRSS2-ETS), epigenetic changes (EZH2, DAB2IP, histone alteration), and transcriptional modifications (SChLAP, PCAT-1). Copy number variability and dysregulation of specific pathways including androgen receptor signaling, PTEN/PAKT and TGF-ß continue to play an important role in invasion and metastasis. SUMMARY: Given the current challenges for applying prostate cancer genomics to clinical management, this review will incorporate some of the current novel genomic approaches and techniques including systems-based precise pathology platforms, and the role of fluid-based assays, notably, exosomes and circulating tumor cells (liquid biopsy), as tools for future diagnostic-treatment algorithms.

U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer.

Aims: Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy.Materials and Methods: A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e., clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care.Results: PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1M females members. Over 6-years, savings increased to $12.5M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr.Limitations: For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data.Conclusions: Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.