Learning of speckle statistics for in vivo and noninvasive characterization of cutaneous wound regions using laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) provides a noninvasive and cost effective solution for in vivo monitoring of blood flow. So far, most of the researches consider changes in speckle pattern (i.e. correlation time of speckle intensity fluctuation), account for relative change in blood flow during abnormal conditions. This paper introduces an application of LSCI for monitoring wound progression and characterization of cutaneous wound regions on mice model. Speckle images are captured on a tumor wound region at mice leg in periodic interval. Initially, raw speckle images are converted to their corresponding contrast images. Functional characterization begins with first segmenting the affected area using k-means clustering, taking wavelet energies in a local region as feature set. In the next stage, different regions in wound bed are clustered based on progressive and non-progressive nature of tissue properties. Changes in contrast due to heterogeneity in tissue structure and functionality are modeled using LSCI speckle statistics. Final characterization is achieved through supervised learning of these speckle statistics using support vector machine. On cross evaluation with mice model experiment, the proposed approach classifies the progressive and non-progressive wound regions with an average sensitivity of 96.18%, 97.62% and average specificity of 97.24%, 96.42% respectively. The clinical information yield with this approach is validated with the conventional immunohistochemistry result of wound to justify the ability of LSCI for in vivo, noninvasive and periodic assessment of wounds.

Exploring the resistome and virulome in major sequence types of Acinetobacter baumannii genomes: Correlations with genome divergence and sequence types.

The increasing global prevalence of antimicrobial resistance in Acinetobacter baumannii has led to concerns regarding the effectiveness of infection treatment. Moreover, the critical role of virulence factor genes in A. baumannii's pathogenesis and its propensity to cause severe disease is of particular importance. Comparative genomics, including multi-locus sequence typing (MLST), enhances our understanding of A. baumannii epidemiology. While there is substantial documentation on A. baumannii, a comprehensive study of the antibiotic-resistant mechanisms and the virulence factors contributing to pathogenesis, and their correlation with Sequence Types (STs) remains incompletely elucidated. In this study, we aim to explore the relationship between antimicrobial resistance genes, virulence factor genes, and STs using genomic data from 223 publicly available A. baumannii strains. The core phylogeny analysis revealed five predominant STs in A. baumannii genomes, linked to their geographical sources of isolation. Furthermore, the resistome and virulome of A. baumannii followed an evolutionary pattern consistent with their pan-genome evolution. Among the major STs, we observed significant variations in resistant genes against "aminoglycoside" and "sulphonamide" antibiotics, highlighting the role of genotypic variations in determining resistance profiles. Furthermore, the presence of virulence factor genes, particularly exotoxin and nutritional / metabolic factor genes, played a crucial role in distinguishing the major STs, suggesting a potential link between genetic makeup and pathogenicity. Understanding these associations can provide valuable insights into A. baumannii's virulence potential and clinical outcomes, enabling the development of effective strategies to combat infections caused by this opportunistic pathogen.

MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data.

Novel methodologies are now essential for identification of antibiotic resistant pathogens in order to resist them. Here, we are presenting a model, MACI (Machine learning-based Antibiotic resistance gene-specific drug Class Identification) that can take metagenomic fragments as input and predict the drug class of antibiotic resistant genes. In our study, we trained a model using the Comprehensive Antibiotic Resistance Database, containing 5138 representative sequences across 134 drug classes. Among these classes, 23 dominated, contributing 85% of the sequence data. The model achieved an average precision of 0.8389 ± 0.0747 and recall of 0.8197 ± 0.0782 for these 23 drug classes. Additionally, it exhibited higher performance (precision and recall: 0.8817 ± 0.0540 and 0.8620 ± 0.0493) for predicting multidrug resistant classes compared to single drug resistant categories (0.7923 ± 0.0669 and 0.7737 ± 0.0794). The model also showed promising results when tested on an independent data. We then analysed these 23 drug classes to identify class-specific overlapping nucleotide patterns. Five significant drug classes, viz. "Carbapenem; cephalosporin; penam", "cephalosporin", "cephamycin", "cephalosporin; monobactam; penam; penem", and "fluoroquinolone" were identified, and their patterns aligned with the functional domains of antibiotic resistance genes. These class-specific patterns play a pivotal role in rapidly identifying drug classes with antibiotic resistance genes. Further analysis revealed that bacterial species containing these five drug classes are associated with well-known multidrug resistance properties.

Spike protein mutational landscape in India during the complete lockdown phase: Could Muller's ratchet be a future game-changer for COVID-19?

The dire need of effective preventive measures and treatment approaches against SARS-CoV-2 virus, causing COVID-19 pandemic, calls for an in-depth understanding of its evolutionary dynamics with attention to specific geographic locations, since lockdown and social distancing to prevent the virus spread could lead to distinct localized dynamics of virus evolution within and between countries owing to different environmental and host-specific selection pressures. To decipher any correlation between SARS-CoV-2 evolution and its epidemiology in India, we studied the mutational diversity of spike glycoprotein, the key player for the attachment, fusion and entry of virus to the host cell. For this, we analyzed the sequences of 630 Indian isolates as available in GISAID database till June 07, 2020 (during the time-period before the start of Unlock 1.0 in India on and from June 08, 2020), and detected the spike protein variants to emerge from two major ancestors - Wuhan-Hu-1/2019 and its D614G variant. Average stability of the docked spike protein - host receptor (S-R) complexes for these variants correlated strongly (R2 = 0.96) with the fatality rates across Indian states. However, while more than half of the variants were found unique to India, 67% of all variants showed lower stability of S-R complex than the respective ancestral variants, indicating a possible fitness loss in recently emerged variants, despite a continuous increase in mutation rate. These results conform to the sharply declining fatality rate countrywide (>7-fold during April 11 - June 28, 2020). Altogether, while we propose the potential of S-R complex stability to track disease severity, we urge an immediate need to explore if SARS-CoV-2 is approaching mutational meltdown in India.

Non-invasive determination of murine placental and foetal functional parameters with multispectral optoacoustic tomography.

Despite the importance of placental function in embryonic development, it remains poorly understood and challenging to characterize, primarily due to the lack of non-invasive imaging tools capable of monitoring placental and foetal oxygenation and perfusion parameters during pregnancy. We developed an optoacoustic tomography approach for real-time imaging through entire ~4 cm cross-sections of pregnant mice. Functional changes in both maternal and embryo regions were studied at different gestation days when subjected to an oxygen breathing challenge and perfusion with indocyanine green. Structural phenotyping of the cross-sectional scans highlighted different internal organs, whereas multi-wavelength acquisitions enabled non-invasive label-free spectroscopic assessment of blood-oxygenation parameters in foeto-placental regions, rendering a strong correlation with the amount of oxygen administered. Likewise, the placental function in protecting the embryo from extrinsically administered agents was substantiated. The proposed methodology may potentially further serve as a probing mechanism to appraise embryo development during pregnancy in the clinical setting.

Probabilistic graphical modeling of speckle statistics in laser speckle contrast imaging for noninvasive and label-free retinal angiography.

This paper introduces a noninvasive and label-free approach for retinal angiography using Laser speckle contrast imaging (LSCI). Retinal vessel structure is segmented using a Hidden Markov Random Field (HMRF) based model. Prior to that, k-means clustering is used to obtain initial parameter set and labels for HMRF. Final parameter set for HMRF is estimated using expectation-maximization (EM) algorithm and final labeling is achieved using maximum aposteriori (MAP) algorithm. Clique energy for HMRF is computed from eigenvalue analysis of structure tensor for each pixel. This helps to get connectivity in the direction of strongest tangents in its neighborhood, facilitating the tracking of fine vessels in retinal vascular network. Quantitative evaluation shows an average vessel segmentation accuracy of 96.41% in normal condition with substantial improvement in tracking capability of fine vessels. Changes in blood flow can be tracked and observed at segmented output; particularly applicable for the study of different pathological conditions.

Review of laser speckle-based analysis in medical imaging.

Speckle pattern forms when a rough object is illuminated with coherent light (laser) and the backscattered radiation is imaged on a screen. The pattern changes over time due to movement in the object. Such time-integrate speckle pattern can be statistically analyzed to reveal the flow profile. For higher velocity the speckle contrast gets reduced. This theory can be utilized for tissue perfusion in capillaries of human skin tissue and cerebral blood flow mapping in rodents. Early, the technique was suffered from low resolution and computational intricacies for real-time monitoring purpose. However, modern engineering has made it feasible for real-time monitoring in microcirculation imaging with improved resolution. This review illustrates several modifications over classical technique done by many researchers. Recent advances in speckle contrast methods gain major interest, leading towards practical implementation of this technique. The review also brings out the scopes of laser speckle-based analysis in various medical applications.

Development of cardiac prescreening device for rural population using ultralow-power embedded system.

The invention is inspired by the desire to understand the opportunities and expectations of developing economies in terms of healthcare. The designed system is a point-of-care (POC) device that can deliver heart-care services to the rural population and bridge the rural-urban divide in healthcare delivery. The product design incorporates several innovations including the effective use of adaptive and multiresolution signal-processing techniques for acquisition, denoising, segmentation, and characterization of the heart sounds (HS) and murmurs using an ultralow-power embedded Mixed Signal Processor. The device is able to provide indicative diagnosis of cardiac conditions and classify a subject into either normal, abnormal, ischemic, or valvular abnormalities category. Preliminary results demonstrated by the prototype confirm the applicability of the device as a prescreening tool that can be used by paramedics in rural outreach programs. Feedback from medical professionals also shows that such a device is helpful in early detection of common congenital heart diseases. This letter aims to determine a framework for utilization of automated HS analysis system for community healthcare and healthcare inclusion.