Nevin Manimala

J Phys Condens Matter. 2021 Nov 24. doi: 10.1088/1361-648X/ac3cef. Online ahead of print.

ABSTRACT

We report a experimental study of the motion of 1μm single particles interacting with functionalized walls at low and moderate ionic strengths conditions. The 3D particle’s trajectories were obtained by analyzing the diffracted particle images (point spread function). The studied particle/wall systems include negatively charged particles interacting with bare glass, glass covered with polyelectrolytes and glass covered with a lipid monolayer. In the low salt regime (pure water) we observed a retardation effect of the short-time diffusion coefficients when the particle interacts with a negatively charged wall; this effect is more severe in the perpendicular than in the lateral component. The decrease of the diffusion as a function of the particle-wall distance h was similar regardless the origin of the negative charge at the wall. When surface charge was screened or salt was added to the medium (10mM), the diffusivity curves recover the classical hydrodynamic behavior. Electroviscous theory based on the thin electrical double layer (EDL) approximation reproduces the experimental data except for small h. On the other hand, 2D numerical solutions of the electrokinetic equations showed good qualitative agreement with experiments. The numerical model also showed that the hydrodynamic and Maxwellian part of the electroviscous total drag tend to zero as h → 0 and how this is linked with the merging of both EDL’s at close proximity.

PMID:34818642 | DOI:10.1088/1361-648X/ac3cef

Phys Med Biol. 2021 Nov 24. doi: 10.1088/1361-6560/ac3d15. Online ahead of print.

ABSTRACT

Recent medical image segmentation methods heavily rely on large-scale training data and high-quality annotations. However, these resources are hard to obtain due to the limitation of medical images and professional annotators. How to utilize limited annotations and maintain the performance is an essential yet challenging problem. In this paper, we try to tackle this problem in a self-learning manner by proposing a Generative Adversarial Semi-supervised Network (GASNet). We use limited annotated images as main supervision signals, and the unlabeled images are manipulated as extra auxiliary information to improve the performance. More specifically, we modulate a segmentation network as a generator to produce pseudo labels for unlabeled images. To make the generator robust, we train an uncertainty discriminator with generative adversarial learning to determine the reliability of the pseudo labels. To further ensure dependability, we apply feature mapping loss to obtain statistic distribution consistency between the generated labels and the real labels. Then the verified pseudo labels are used to optimize the generator in a self-learning manner. We validate the effectiveness of the proposed method on right ventricle dataset, Sunnybrook dataset, STACOM, ISIC dataset, and Kaggle lung dataset. We obtain 0.8402 to 0.9121, 0.8103 to 0.9094, 0.9435 to 0.9724, 0.8635 to 0.886, and 0.9697 to 0.9885 dice coefficient with 1/8 to 1/2 proportion of densely annotated labels, respectively. The improvements are up to 28.6 points higher than the corresponding fully supervised baseline.

PMID:34818627 | DOI:10.1088/1361-6560/ac3d15

Med Image Anal. 2021 Nov 11;75:102304. doi: 10.1016/j.media.2021.102304. Online ahead of print.

ABSTRACT

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, “Multi-scAle heteroGeneity analysIs and Clustering” (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N = 4403). We then applied MAGIC to imaging data from Alzheimer’s disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

PMID:34818611 | DOI:10.1016/j.media.2021.102304

Comput Biol Med. 2021 Sep 30;139:104909. doi: 10.1016/j.compbiomed.2021.104909. Online ahead of print.

ABSTRACT

A phenomenon called alternans, which is a beat-to-beat alternation in action potential (AP) duration, sometimes precedes fatal cardiac arrhythmias. Alternans-suppressing electrical stimulus protocols are often represented as perturbations to the dynamics of membrane potential or AP duration variables in nonlinear models of cardiac tissue. Controllability analysis has occasionally been applied to cardiac AP models to determine whether different control or perturbation strategies are capable of suppressing alternans or other unwanted behavior. Since almost all previous cardiac controllability studies have focused on low-dimensional models, we conducted the present study to assess controllability of a higher-dimensional model, specifically the Luo Rudy dynamic (LRd) model of a cardiac ventricular myocyte. Higher-dimensional models are of interest because they provide information on the influence of a wider range of measurable quantities, including ionic concentrations, on controllability. After computing modal controllability measures, we found that larger eigenvalues of a linearized LRd model were on average more strongly controllable through perturbations to calcium-ion concentrations compared with perturbations to other variables. When only membrane potential was adjusted, the best time to apply perturbations (in the sense of maximizing controllability of the largest alternans eigenvalue) was near the AP peak time for shorter cycle lengths. Controllability results were found to be similar for both the default model parameters and for an alternans-promoting parameter set. Additionally, we developed several alternans-suppressing state feedback controllers that were tested in simulations. For the scenarios examined, our controllability measures correctly predicted which strategies and perturbation timings would lead to better feedback controller performance.

PMID:34818582 | DOI:10.1016/j.compbiomed.2021.104909

J Microbiol Methods. 2021 Nov 21:106378. doi: 10.1016/j.mimet.2021.106378. Online ahead of print.

ABSTRACT

Listeria monocytogenes belongs to the category of facultative anaerobic bacteria, and is the pathogen of listeriosis, potentially lethal disease for humans. There are many similarities between L. monocytogenes and other non-pathogenic Listeria species, which causes great difficulties for their correct identification. The level of L. monocytogenes contamination in food remains high according to statistics from the Food and Drug Administration. This situation leads to food recall and destruction, which has caused huge economic losses to the food industry. Therefore, the identification of Listeria species is very important for clinical treatment and food safety. This work aims to explore an efficient classification algorithm which could easily and reliably distinguish Listeria species. We attempted to classify Listeria species by incorporating denoising autoencoder (DAE) and machine learning algorithms in matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition, convolutional neural networks were used to map the high dimensional original mass spectrometry data to low dimensional core features. By analyzing MALDI-TOF MS data via incorporating DAE and support vector machine (SVM), the identification accuracy of Listeria species was 100%. The proposed classification algorithm is fast (range of seconds), easy to handle, and, more importantly, this method also allows for extending the identification scope of bacteria. The DAE model used in our research is an effective tool for the extraction of MALDI-TOF mass spectrometry features. Despite the fact that the MALDI-TOF MS dataset examined in our research had high dimensionality, the DAE + SVM algorithm was still able to exploit the hidden information embedded in the original MALDI-TOF mass spectra. The experimental results in our work demonstrated that MALDI-TOF mass spectrum combined with DAE + SVM could easily and reliably distinguish Listeria species.

PMID:34818574 | DOI:10.1016/j.mimet.2021.106378

J Speech Lang Hear Res. 2021 Nov 24:1-23. doi: 10.1044/2021_JSLHR-20-00205. Online ahead of print.

ABSTRACT

PURPOSE: Meaningful changes in picture naming responses may be obscured when measuring accuracy instead of quality. A statistic that incorporates information about the severity and nature of impairments may be more sensitive to the effects of treatment.

METHOD: We analyzed data from repeated administrations of a naming test to 72 participants with stroke aphasia in a clinical trial for anomia therapy. Participants were divided into two groups for analysis to demonstrate replicability. We assessed reliability among response type scores from five raters. We then derived four summary statistics of naming ability and their changes over time for each participant: (a) the standard accuracy measure, (b) an accuracy measure adjusted for item difficulty, (c) an accuracy measure adjusted for item difficulty for specific response types, and (d) a distance measure adjusted for item difficulty for specific response types. While accuracy measures address the likelihood of a correct response, the distance measure reflects that different response types range in their similarity to the target. Model fit was assessed. The frequency of significant improvements and the average magnitude of improvements for each summary statistic were compared between treatment groups and a control group. Effect sizes for each model-based statistic were compared with the effect size for the standard accuracy measure.

RESULTS: Interrater and intrarater reliability were near perfect, on average, though compromised somewhat by phonological-level errors. The effects of treatment were more evident, in terms of both frequency and magnitude, when using the distance measure versus the other accuracy statistics.

CONCLUSIONS: Consideration of item difficulty and response types revealed additional effects of treatment on naming scores beyond those observed for the standard accuracy measure. The results support theories that assume naming ability is decomposable into subabilities rather than being monolithic, suggesting new opportunities for measuring treatment outcomes. Supplemental Material https://doi.org/10.23641/asha.17019515.

PMID:34818508 | DOI:10.1044/2021_JSLHR-20-00205

J Speech Lang Hear Res. 2021 Nov 24:1-15. doi: 10.1044/2021_JSLHR-20-00464. Online ahead of print.

ABSTRACT

PURPOSE: This study examined the relationship between utterance length, syntactic complexity, and the probability of making an error at the utterance level.

METHOD: The participants in this study included 830 Spanish-speaking first graders who were learning English at school. Story retells in both Spanish and English were collected from all children. Generalized mixed linear models were used to examine within-child and between-children effects of utterance length and subordination on the probability of making an error at the utterance level.

RESULTS: The relationship between utterance length and grammaticality was found to differ by error type (omission vs. commission), language (Spanish vs. English), and level of analysis (within-child vs. between-children). For errors of commission, the probability of making an error increased as a child produced utterances that were longer relative to their average utterance length (within-child effect). Contrastively, for errors of omission, the probability of making an error decreased when a child produced utterances that were longer relative to their average utterance length (within-child effect). In English, a child who produced utterances that were, on average, longer than the average utterance length for all children produced more errors of commission and fewer errors of omission (between-children effect). This between-children effect was similar in Spanish for errors of commission but nonsignificant for errors of omission. For both error types, the within-child effects of utterance length were moderated by the use of subordination.

CONCLUSION: The relationship between utterance length and grammaticality is complex and varies by error type, language, and whether the frame of reference is the child’s own language (within-child effect) or the language of other children (between-children effect). Supplemental Material https://doi.org/10.23641/asha.17035916.

PMID:34818505 | DOI:10.1044/2021_JSLHR-20-00464

Prim Care Companion CNS Disord. 2021 Nov 24;23(6):21m02927. doi: 10.4088/PCC.21m02927.

ABSTRACT

Objective: To compare the safety and efficacy of conventional anticoagulants with new oral anticoagulants (NOACs) for management of cerebral venous sinus thrombosis (CVST).

Methods: This was a retrospective, prospective cohort study of patients who presented with CVST to a tertiary stroke center in the Middle East from January 2012 to October 2019. Patients with a diagnosis of CVST were identified, and data were analyzed for demographic characteristics. Specific consideration was given to compare the efficacy and safety of different anticoagulation treatments.

Results: A total of 36 patients were included in the final analysis, with 15 (41%) men and 21 (59%) women and a male to female ratio of 1:1.4. Most of the patients (n = 22, 61%) were Saudi. Their ages ranged between 15 and 82 years (mean ± SD age of 34.22 ± 13.16 years). Headache was the most common feature, present in 22 (61%) of the patients, followed by unilateral weakness in 15 (41%) and cranial nerve palsies in 11 (30%). The most common etiology was prothrombotic state (both hereditary and acquired thrombophilia: n = 16, 45%). Other etiologies were postpartum state/oral contraceptive pill usage in 7 (19%), infections in 7 (19%), and trauma in 3 (8%). Most of the patients (n = 24, 67%) still received conventional anticoagulation (warfarin/low molecular weight heparin), but 9 (25%) of the patients consented to start NOACs. Efficacy (as measured by clinical improvement plus rate of recanalization of previously thrombosed venous sinuses) showed no statistically significant difference, although it proved to be better tolerated, as none of the patients stopped the treatment due to adverse events and risk of major bleeding was significantly low in the NOAC group. Nine patients in the warfarin group stopped medication, while none in the NOAC group did so (P = .034).

Conclusion: NOACs were found to be at least as good as conventional anticoagulation for the management of CVST. However, efficacy was almost similar, a finding that is consistent with most of the published case series and the few recently published prospective studies. Larger prospective and population-based studies are needed to clarify our preliminary results.

PMID:34818472 | DOI:10.4088/PCC.21m02927

Asia Pac J Clin Oncol. 2021 Nov 24. doi: 10.1111/ajco.13639. Online ahead of print.

ABSTRACT

AIM: The effects of diabetes mellitus (DM) on the outcomes of colorectal cancer (CRC) are controversial. This retrospective study evaluated the effects of DM on American Joint Committee on Cancer (AJCC, 7th) Stages II and III CRC patients who received curative surgery.

METHODS: We reviewed the records of CRC patients who were treated from January 2008 to December 2014 and identified the presence of DM and hypertension prior to CRC diagnosis. Cox proportional hazards analyses were used for prognostic factor determination, and survival was analyzed using the Kaplan-Meier method with the log-rank test.

RESULTS: Total of 1066 consecutive eligible patients with stage II/III CRC were enrolled. There were 326 (30.6%) patients diagnosed with DM, and 311 (29.2%) CRC patients had recurrence. Patients with DM did not have a higher recurrence risk (p = 0.183) but had higher mortality (adjusted hazard ratio [aHR] = 1.381; 95% conference interval [CI], 1.069-1.782). In addition, HbA1c (≥7 vs. <7) was not associated with recurrence (p = 0.365). Patients with DM had more hypertension than patients without DM (69.1% vs. 37.6%, p < 0.001). A lower recurrence risk was noted in patients with hypertension (p = 0.002), but the overall survival (OS) did not reach statistical significance (aHR = 0.910; 95% CI, 0.707-1.169).

CONCLUSION: In our study, DM was a poor prognostic factor for survival in curative CRC patients. More studies are required to elucidate the effects that DM and other metabolic disorders, such as hypertension, have on the prognosis of patients with CRC.

PMID:34818458 | DOI:10.1111/ajco.13639

Clin Pharmacol Ther. 2021 Nov 24. doi: 10.1002/cpt.2477. Online ahead of print.

ABSTRACT

Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.

PMID:34818443 | DOI:10.1002/cpt.2477