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In recent years, neural networks (NNs) have been embraced by several scientific and engineering disciplines for diverse modeling and inferencing applications. The importance of quantifying the confidence in NN predictions has escalated due to the increasing adoption of these decision models. Nevertheless, conventional NN do not furnish uncertainty estimates associated with their predictions and are therefore ill-calibrated. Uncertainty quantification techniques offer probability distributions or CIs to represent the uncertainty associated with NN predictions, instead of solely presenting the point predictions/estimates. Once the uncertainty in NN is quantified, it is crucial to leverage this information to modify training objectives and improve the accuracy and reliability of the corresponding decision models. This work presents a novel framework to utilize the knowledge of input and output uncertainties in NN to guide querying process in the context of Active Learning. We also derive the lower and upper bounds for label complexity. The efficacy of the proposed framework is established by conducting experiments across classification and regression tasks. 

Ovarian cancer survival depends strongly on the time of diagnosis. Detection at stage 1 must be the goal of liquid biopsies for ovarian cancer detection. We report the development and validation of graphene-based optical nanobiosensors (G-NBSs) that quantify the activities of a panel of proteases, which were selected to provide a crowd response that is specific for ovarian cancer. These G-NBSs consist of few-layer explosion graphene featuring a hydrophilic coating, which is linked to fluorescently labeled highly selective consensus sequences for the proteases of interest, as well as a fluorescent dye. The panel of G-NBSs showed statistically significant differences in protease activities when comparing localized (early-stage) ovarian cancer with both metastatic (late-stage) and healthy control groups. A hierarchical framework integrated with active learning (AL) as a prediction and analysis tool for early-stage detection of ovarian cancer was implemented, which obtained an overall accuracy score of 94.5%, with both a sensitivity and specificity of 0.94. 

This study investigates the potential use of circulating extracellular vesicles’ (EVs) DNA and protein content as biomarkers for traumatic brain injury (TBI) in a mouse model. Despite an overall decrease in EVs count during the acute phase, there was an increased presence of exosomes (CD63+ EVs) during acute and an increase in microvesicles derived from microglia/macrophages (CD11b+ EVs) and astrocytes (ACSA-2+ EVs) in post-acute TBI phases, respectively. Notably, mtDNA exhibited an immediate elevation post-injury. Neuronal (NFL) and microglial (Iba1) markers increased in the acute, while the astrocyte marker (GFAP) increased in post-acute TBI phases. Novel protein biomarkers (SAA, Hp, VWF, CFD, CBG) specific to different TBI phases were also identified. Biostatistical modeling and machine learning identified mtDNA and SAA as decisive markers for TBI detection. These findings emphasize the importance of profiling EVs’ content and their dynamic release as an innovative diagnostic approach for TBI in liquid biopsies 

Cellular senescence is a state of exiting the cell cycle, resisting apoptosis, and changing phenotype. Senescent cells (SCs) can be identified by large, distorted morphology and irreversible inability to replicate. In early development, senescence has beneficial roles like tissue patterning and wound healing, where SCs are cleared by the immune system. However, there is a steep rise in SC number as organisms age. The issue with SC accumulation stems from the loss of cellular function, alterations of the microenvironment, and secretions of pro‐inflammatory molecules, consisting of cytokines, chemokines, matrix metalloproteinases (MMPs), interleukins, and extracellular matrix (ECM)‐associated molecules. This secreted cocktail is referred to as the senescence‐associated secretory phenotype (SASP), a hallmark of cellular senescence. The SASP promotes inflammation and displays a bystander effect where paracrine signaling turns proliferating cells into senescent states. To alleviate age‐associated diseases, researchers have developed novel methods and techniques to selectively eliminate SCs in aged individuals. Although studies demonstrated that selectively killing SCs improves age‐related disorders, there are drawbacks to SC removal. Considering favorable aspects of senescence in the body, this paper reviews recent advancements in elimination strategies and potential rejuvenation targets of senescence to bring researchers in the field up to date. 

Increased expression of the human telomere reverse transcriptase (hTERT) in tumors promotes tumor cell survival and diminishes the survival of patients. Cytosine-to-thymine (C-to-T) transition mutations (C250T or C228T) in the hTERT promoter create binding sites for transcription factors, which enhance transcription. The G-rich strand of the hTERT promoter can form G-quadruplex structures, whereas the C-rich strand can form an i-motif in which multiple cytosine residues are protonated. We considered the possibility that i-motif formation might promote cytosine deamination to uracil and C-to-T mutations. We computationally probed the accessibility of cytosine residues in an i-motif to attack by water. We experimentally examined regions of the C-rich strand to form i-motifs using pH-dependent UV and CD spectra. We then incubated the C-rich strand with and without the G-rich complementary strand DNA under various conditions, followed by deep sequencing. Surprisingly, deamination rates did not vary substantially across the 46 cytosines examined, and the two mutation hotspots were not deamination hotspots. The appearance of mutational hotspots in tumors is more likely the result of the selection of sequences with increased promoter binding affinity and hTERT expression. 

Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, as- sessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-res- olution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-res- olution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data recon- structions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were inves- tigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, in- crease the CNR in nearly all instances, and increase the SNR in small samples. 

For over two centuries, clinicians have hypothesized that cancer developed preferentially at the sites of repeated damage, indicating that cancer is basically “continued healing.” Tumor cells can develop over time into other more malignant types in different environments. Interestingly, indefinite growth correlates with the depletion of a modular, early rigidity sensor, whereas restoring these sensors in tumor cells blocks tumor growth on soft surfaces and metastases. Importantly, normal and tumor cells from many different tissues exhibit transformed growth without the early rigidity sensor. When sensors are restored in tumor cells by replenishing depleted mechanosensory proteins that are often cytoskeletal, cells revert to normal rigidity-dependent growth. Surprisingly, transformed growth cells are sensitive to mechanical stretching or ultrasound which will cause apoptosis of transformed growth cells (Mechanoptosis). Mechanoptosis is driven by calcium entry through mechanosensitive Piezo1 channels that activate a calcium-induced calpain response commonly found in tumor cells. Since tumor cells from many different tissues are in a transformed growth state that is, characterized by increased growth, an altered cytoskeleton and mechanoptosis, it is possible to inhibit growth of many different tumors by mechanical activity and potentially by cytoskeletal inhibitors. 

The advancement of biomedicine in a socioeconomically sustainable manner while achieving efficient patient-care is imperative to the health and well-being of society. Magnetic systems consisting of iron based nanosized components have gained prominence among researchers in a multitude of biomedical applications. This review focuses on recent trends in the areas of diagnostic imaging and drug delivery that have benefited from iron-incorporated nanosystems, especially in cancer treatment, diagnosis and wound care applications. Discussion on imaging will emphasise on developments in MRI technology and hyperthermia based diagnosis, while advanced material synthesis and targeted, triggered transport will be the focus for drug delivery. Insights onto the challenges in transforming these technologies into day-to-day applications will also be explored with perceptions onto potential for patient-centred healthcare.