Purpose
The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method ...to facilitate diagnosis and prognosis in MS.
Methods
This paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier.
Results
For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs.
Conclusion
We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method.
To assess the ability of a new posterior pole protocol to detect areas with significant differences in retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) thickness in patients with ...multiple sclerosis versus healthy control subjects; in addition, to assess the correlation between RNFL and GCL thickness, disease duration, and the Expanded Disability Status Scale (EDSS).
We analyzed 66 eyes of healthy control subjects and 100 eyes of remitting-relapsing multiple sclerosis (RR-MS) patients. Double analysis based on first clinical symptom onset (CSO) and conversion to clinically definite MS (CDMS) was performed. The RR-MS group was divided into subgroups by CSO and CDMS year: CSO-1 (≤ 5 years) and CSO-2 (≥ 6 years), and CDMS-1 (≤ 5 years) and CDMS-2 (≥ 6 years).
Significant differences in RNFL and GCL thickness were found between the RR-MS group and the healthy controls and between the CSO and CDMS subgroups and in both layers. Moderate to strong correlations were found between RNFL and GCL thickness and CSO and CDMS. Furthermore, we observed a strong correlation with EDSS 1 year after the OCT examination.
The posterior pole protocol is a useful tool for assessing MS and can reveal differences even in early stages of the disease. RNFL thickness shows a strong correlation with disability status, while GCL thickness correlates better with disease duration.
The protein kinase C (PKC) agonist bryostatin-1 has shown significant ex-vivo potency to revert HIV-1 latency, compared with other latency reversing agents (LRA). The safety of this candidate LRA ...remains to be proven in treated HIV-1-infected patients.
In this pilot, double-blind phase I clinical-trial (NCT 02269605), we included aviraemic HIV-1-infected patients on triple antiretroviral therapy to evaluate the effects of two different single doses of bryostatin-1 (10 or 20 μg/m) compared with placebo.
Twelve patients were included, four in each arm. Bryostatin-1 was well tolerated in all participants. Two patients in the 20 μg/m arm developed grade 1 headache and myalgia. No detectable increases of cell-associated unspliced (CA-US) HIV-1-RNA were observed in any study arm, nor differences in HIV-1 mRNA dynamics between arms (P = 0.44). The frequency of samples with low-level viraemia did not differ between arms and low-level viraemia did not correlate with CA-US HIV-1-RNA levels (P = 0.676). No changes were detected on protein kinase C (PKC) activity and in biomarkers of inflammation (sCD14 and interleukin-6) in any study arm. After the single dose of bryostatin-1, plasma concentrations were under detection limits in all the patients in the 10 μg/m arm, and below 50 pg/ml (0.05 nmol/l) in those in the 20 μg/m arm.
Bryostatin-1 was safe at the single doses administered. However, the drug did not show any effect on PKC activity or on the transcription of latent HIV, probably due to low plasma concentrations. This study will inform next trials aimed at assessing higher doses, multiple dosing schedules or combination studies with synergistic drugs.
To investigate superficial retinal microvascular plexuses detected by optical coherence tomography angiography (OCT-A) in multiple sclerosis (MS) subjects and compare them with healthy controls.
A ...total of 92 eyes from 92 patients with relapsing-remitting MS and 149 control eyes were included in this prospective observational study. OCT-A imaging was performed using Triton Swept-Source OCT (Topcon Corporation, Japan). The vessel density (VD) percentage in the superficial retinal plexus and optic disc area (6 x 6 mm grid) was measured and compared between groups.
MS patients showed a significant decrease VD in the superior (p = 0.005), nasal (p = 0.029) and inferior (p = 0.040) parafoveal retina compared with healthy subjects. Patients with disease durations of more than 5 years presented lower VD in the superior (p = 0.002), nasal (p = 0.017) and inferior (p = 0.022) parafoveal areas compared with healthy subjects. Patients with past optic neuritis episodes did not show retinal microvasculature alterations, but patients with an EDSS score of less than 3 showed a significant decrease in nasal (p = 0.024) and superior (p = 0.006) perifoveal VD when compared with healthy subjects.
MS produces a decrease in retinal vascularization density in the superficial plexus of the parafoveal retina. Alterations in retinal vascularization observed in MS patients are independent of the presence of optic nerve inflammation. OCT-A has the ability to detect subclinical vascular changes and is a potential biomarker for diagnosing the presence and progression of MS.
Vaccinia-related kinase (VRK) 1 is a serin/threonine kinase that plays an important role in DNA damage response (DDR), phosphorylating some proteins involved in this process such as 53BP1, NBS1 or ...H2AX, and in the cell cycle progression. In addition, VRK1 is overexpressed in many cancer types and its correlation with poor prognosis has been determined, showing VRK1 as a new therapeutic target in oncology. Using in vitro selection, high-affinity DNA aptamers to VRK1 were selected from a library of ssDNA. Selection was monitored using the enzyme-linked oligonucleotide assay (ELONA), and the selected aptamer population was cloned and sequenced. Three aptamers were selected and characterized. These aptamers recognized the protein kinase VRK1 with an affinity in the nanomolar range and showed a high sensibility. Moreover, the treatment of the MCF7 breast cell line with these aptamers resulted in a decrease in cyclin D1 levels, and an inhibition of cell cycle progression by G1 phase arrest, which induced apoptosis in cells. These results suggest that these aptamers are specific inhibitors of VRK1 that might be developed as potential drugs for the treatment of cancer.
To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different ...machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer-Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease.
Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population.
Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL.
Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.
Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process ...and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT).
A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested to develop two predictive models: MS diagnosis model and MS disability course prediction model.
For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC ≥ 0.8).
This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.
•Machine learning techniques to diagnose and predict the disability course of multiple sclerosis (MS) are evaluated.•Retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT), is analysed as axonal damage quantification.•MS diagnosis and MS disability course prediction models were developed based on RNFL thickness and clinical data.•Results corroborated RNFL as a powerful biomarker of MS.•Long-term prediction of MS disability course could help clinicians apply more specific treatments.
Lung cancer is the leading cause of cancer-related death worldwide. Its late diagnosis and consequently poor survival make necessary the search for new therapeutic targets. The mitogen-activated ...protein kinase (MAPK)-interacting kinase 1 (MNK1) is overexpressed in lung cancer and correlates with poor overall survival in non-small cell lung cancer (NSCLC) patients. The previously identified and optimized aptamer from our laboratory against MNK1, apMNKQ2, showed promising results as an antitumor drug in breast cancer in vitro and in vivo. Thus, the present study shows the antitumor potential of apMNKQ2 in another type of cancer where MNK1 plays a significant role, such as NSCLC. The effect of apMNKQ2 in lung cancer was studied with viability, toxicity, clonogenic, migration, invasion, and in vivo efficacy assays. Our results show that apMNKQ2 arrests the cell cycle and reduces viability, colony formation, migration, invasion, and epithelial-mesenchymal transition (EMT) processes in NSCLC cells. In addition, apMNKQ2 reduces tumor growth in an A549-cell line NSCLC xenograft model. In summary, targeting MNK1 with a specific aptamer may provide an innovative strategy for lung cancer treatment.
Background
Triple-negative breast cancer (TNBC) remains a difficult breast cancer subtype to treat as it exhibits a particularly aggressive behavior. The dysregulation of distinct signaling pathways ...underlies this aggressive behavior, with an overactivation of MAP kinase interacting kinases (MNKs) promoting tumor cell behavior, and driving proliferation and migration. Therefore, MNK1 is an excellent target to impair the progression of TNBC and indeed, an MNK1-specific aptamer has proved to be efficient in inhibiting TBNC cell proliferation in vitro. Although polyethyleneimine-coated iron oxide nanoparticles (PEI–IONPs) have been used as transfection and immunomodulating agents, no study has yet addressed the benefits of using these nanoparticles as a magnetic carrier for the delivery of a functional aptamer.
Results
Here, we tested the antitumor effect of a PEI–IONP complexed to the functional MNK1b-specific aptamer in vitro and in vivo. We demonstrated that these apMNKQ2@PEI–IONP nanoconjugates delivered three times more apMNKQ2 to MDA-MB-231 cells than the aptamer alone, and that this enhanced intracellular delivery of the aptamer had consequences for MNK1 signaling, reducing the amount of MNK1 and its target the phospho(Ser209)-eukaryotic initiation factor 4E (eIF4E). As a result, a synergistic effect of the apMNKQ2 and PEI–IONPs was observed that inhibited MDA-MB-231 cell migration, probably in association with an increase in the serum and glucocorticoid-regulated kinase-1 (SGK1) and the phospho(Thr346)-N-myc down-regulated gene 1 (NDRG1). However, intravenous administration of the apMNKQ2 alone did not significantly impair tumor growth in vivo, whereas the PEI–IONP alone did significantly inhibit tumor growth. Significantly, tumor growth was not inhibited when the apMNKQ2@PEI–IONP nanocomplex was administered, possibly due to fewer IONPs accumulating in the tumor. This apMNKQ2-induced reversion of the intrinsic antitumor effect of the PEI–IONPs was abolished when an external magnetic field was applied at the tumor site, promoting IONP accumulation.
Conclusions
Electrostatic conjugation of the apMNKQ2 aptamer with PEI–IONPs impedes the accumulation of the latter in tumors, which appears to be necessary for PEI–IONPs to exert their antitumor activity.
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