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ObjectiveTo determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.MethodsPatients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.ResultsParticipants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.InterpretationWithin a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

ObjectiveTo create a novel neurological vital sign and reliably capture MS-related limb disability in less than 5 min.MethodsConsecutive patients meeting the 2010 MS diagnostic criteria and healthy controls were offered enrollment. Participants completed finger and foot taps wearing the MYO-band© (accelerometer, gyroscope, and surface electromyogram sensors). Signal processing was performed to extract spatiotemporal features from raw sensor data. Intraclass correlation coefficients (ICC) assessed intertest reproducibility. Spearman correlation and multivariable regression methods compared extracted features to physician- and patient-reported disability outcomes. Partial least squares regression identified the most informative extracted textural features.ResultsBaseline data for 117 participants with MS (EDSS 1.0-7.0) and 30 healthy controls were analyzed. ICCs for final selected features ranged from 0.80 to 0.87. Time-based features distinguished cases from controls (P = 0.002). The most informative combination of extracted features from all three sensors strongly correlated with physician EDSS (finger taps rs  = 0.77, P

Contrast-enhanced ultrasound could assess whether cancer chemotherapeutic agents work in days, rather than waiting 2–3 months, as is typical using the Response Evaluation Criteria in Solid Tumors (RECIST), therefore avoiding toxic side effects and expensive, ineffective therapy. A total of 40 mice were implanted with human colon cancer cells: treatment-sensitive mice in control (n = 10, receiving saline) and treated (n = 10, receiving bevacizumab) groups and treatment-resistant mice in control (n = 10) and treated (n = 10) groups. Each mouse was imaged using 3D dynamic contrast-enhanced ultrasound with Definity microbubbles. Curvature learning, an unsupervised learning approach, quantized pixels into three classes—blue, yellow, and red—representing normal, intermediate, and high cancer probability, both at baseline and after treatment. Next, a curvature learning score was calculated for each mouse using statistical measures representing variations in these three color classes across each frame from cine ultrasound images obtained during contrast administration on a given day (intra-day variability) and between pre- and post-treatment days (inter-day variability). A Wilcoxon rank-sum test compared score distributions between treated, treatment-sensitive mice and all others. There was a statistically significant difference in tumor score between the treated, treatment-sensitive group (n = 10) and all others (n = 30) (p = 0.0051). Curvature learning successfully identified treatment response, detecting changes in tumor perfusion before changes in tumor size. A similar technique could be developed for humans. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates an imaging method for distinguishing different lesions in brain cancer patients using MRI scans. Glioblastoma is a highly aggressive brain tumor, and it is crucial to differentiate between actual tumor recurrence, radiation-induced damage, and side effects from a common treatment called Bevacizumab. Traditional MRI techniques often struggle to make these distinctions, leading to challenges in treatment decisions. This study explores the use of a specific MRI measurement, called Apparent Diffusion Coefficient (ADC) values, to improve diagnostic accuracy. By identifying unique patterns in ADC values, this method could help better determine the nature of brain lesions, ultimately leading to more precise and effective treatment plans for glioblastoma patients. Objectives: Glioblastomas (GBM) are the most common primary invasive neoplasms of the brain. Distinguishing between lesion recurrence and different types of treatment related changes in patients with GBM remains challenging using conventional MRI imaging techniques. Therefore, accurate and precise differentiation between true progression or pseudoresponse is crucial in deciding on the appropriate course of treatment. This retrospective study investigated the potential of apparent diffusion coefficient (ADC) map values derived from diffusion-weighted imaging (DWI) as a noninvasive method to increase diagnostic accuracy in treatment response. Methods: A cohort of 21 glioblastoma patients (mean age: 59.2 ± 11.8, 12 Male, 9 Female) that underwent treatment with bevacizumab were selected. The ADC values were calculated from the DWI images obtained from a standardized brain protocol across 1.5-T and 3-T MRI scanners. Ratios were calculated for rADC values. Lesions were classified as bevacizumab-induced cytotoxicity based on characteristic imaging features (well-defined regions of restricted diffusion with persistent diffusion restriction over the course of weeks without tissue volume loss and absence of contrast enhancement). The rADC value was compared to these values in radiation necrosis and recurrent lesions, which were concluded in our prior study. The nonparametric Wilcoxon signed rank test with p < 0.05 was used for significance. Results: The mean ± SD age of the selected patients was 59.2 ± 11.8. ADC values and corresponding mean rADC values for bevacizumab-induced cytotoxicity were 248.1 ± 67.2 and 0.39 ± 0.10, respectively. These results were compared to the ADC values and corresponding mean rADC values of tumor progression and radiation necrosis. Significant differences between rADC values were observed in all three groups (p < 0.001). Bevacizumab-induced cytotoxicity had statistically significant lower ADC values compared to both tumor recurrence and radiation necrosis. Conclusion: The study demonstrates the potential of ADC values as noninvasive imaging biomarkers for differentiating recurrent glioblastoma from radiation necrosis and bevacizumab-induced cytotoxicity. [ABSTRACT FROM AUTHOR]

Collagen 1 (Col1) fibers play an important role in tumor interstitial macromolecular transport and cancer cell dissemination. Our goal was to understand the influence of Col1 fibers onwater diffusion, and to examine the potential of using noninvasive diffusion tensor imaging (DTI) to indirectly detect Col1 fibers in breast lesions. We previously observed, in human MDA-MB-231 breast cancer xenografts engineered to fluoresce under hypoxia, relatively low amounts of Col1 fibers in fluorescent hypoxic regions. These xenograft tumors together with human breast cancer samples were used here to investigate the relationship between Col1 fibers, water diffusion and anisotropy, and hypoxia. Hypoxic low Col1 fiber containing regions showed decreased apparent diffusion coefficient (ADC) and fractional anisotropy (FA) compared to normoxic high Col1 fiber containing regions. Necrotic high Col1 fiber containing regions showed increased ADC with decreased FA values compared to normoxic viable high Col1 fiber regions that had increased ADC with increased FA values. A good agreement of ADC and FA patterns was observed between in vivo and ex vivo images. In human breast cancer specimens, ADC and FA decreased in low Col1 containing regions. Our data suggest that a decrease in ADC and FA values observed within a lesion could predict hypoxia, and a pattern of high ADC with low FA values could predict necrosis. Collectively the data identify the role of Col1 fibers in directed water movement and support expanding the evaluation of DTI parameters as surrogates for Col1 fiber patterns associated with specific tumor microenvironments as companion diagnostics and for staging. [ABSTRACT FROM AUTHOR]