The Impact of Pain on Mobility in Patients with Cancer.

Objectives: Provide an overview of how pain impacts mobility in patients with cancer.

Methods: A literature search was conducted in PubMed and on Google Scholar using search terms, cancer pain with mobility, acute and chronic pain syndromes, enhanced recovery after surgery, nursing care, and rehabilitation. Peer-reviewed research studies, review articles, and pain guidelines and position papers were reviewed to provide an overview on cancer pain, its impact on mobility, and the nurse's role in managing pain and optimizing mobility and functional outcomes.

Neuronal sensorimotor integration guiding salt concentration navigation in .

Animals navigate their environment by manipulating their movements and adjusting their trajectory which requires a sophisticated integration of sensory data with their current motor status. Here, we utilize the nematode to explore the neural mechanisms of processing the sensory and motor information for navigation. We developed a microfluidic device which allows animals to freely move their heads while receiving temporal NaCl stimuli.

Intelligent sort-timing prediction for image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed.

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization.

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels.

Clinical Updates in Mucositis-Related Symptom Management.

Objective: To provide an overview with the most up-to-date evidence on the management of cancer-treatment related mucositis.

Data Sources: Peer-reviewed articles, textbooks, and the internet.

Conclusion: A multitude of new and innovative treatments for the management of mucositis exist.

Deep imaging flow cytometry.

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets.

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases.

Development of a health behavior scale for older adults living alone receiving public assistance.

Background: To reduce health disparities, prevention of non-communicable diseases (NCD) by performing desirable health behavior in older adults living alone with low socioeconomic status is an essential strategy in public health. Self-perception of personal power and practical skills for daily health are key elements of desirable health behavior. However, methods for measuring these concepts have not been established.

All-dielectric chiral-field-enhanced Raman optical activity.

Raman optical activity (ROA) is effective for studying the conformational structure and behavior of chiral molecules in aqueous solutions and is advantageous over X-ray crystallography and nuclear magnetic resonance spectroscopy in sample preparation and cost performance. However, ROA signals are inherently minuscule; 3-5 orders of magnitude weaker than spontaneous Raman scattering due to the weak chiral light-matter interaction. Localized surface plasmon resonance on metallic nanoparticles has been employed to enhance ROA signals, but suffers from detrimental spectral artifacts due to its photothermal heat generation and inability to efficiently transfer and enhance optical chirality from the far field to the near field.

Morphological Indicator for Directed Evolution of with a High Heavy Metal Removal Efficiency.

In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them.

Intelligent Platelet Morphometry.

Technological advances in image-based platelet analysis or platelet morphometry are critical for a better understanding of the structure and function of platelets in biological research as well as for the development of better clinical strategies in medical practice. Recently, the advent of high-throughput optical imaging and deep learning has boosted platelet morphometry to the next level by providing a new set of capabilities beyond what is achievable with traditional platelet morphometry, shedding light on the unexplored domain of platelet analysis. This Opinion article introduces emerging opportunities in 'intelligent' platelet morphometry, which are expected to pave the way for a new class of diagnostics, pharmacometrics, and therapeutics.

AI on a chip.

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning.

Sequentially addressable dielectrophoretic array for high-throughput sorting of large-volume biological compartments.

Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem.

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology.

Bright Yellowish-Red Pigment Based on Hematite/Alumina Composites with a Unique Porous Disk-like Structure.

Inspired by a bacteriogenic, iron-based oxide material and a traditional Japanese red pigment, a bright yellowish-red pigment was prepared by heating an Al-containing iron oxyhydroxide precursor. The obtained red pigment had a unique porous disk-like structure, comprising Al-substituted hematite particles and crystalline alumina nanoparticles. Although these disk-like structures loosely gathered to form an aggregate in powder, they can be easily dispersed into a single, disk-like structure by simple ultrasonic irradiation.

Author Correction: A practical guide to intelligent image-activated cell sorting.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A practical guide to intelligent image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing).

Associations between caries experience, dietary habits, and metabolic syndrome in Japanese adults.

Dental caries could be a risk factor for metabolic syndrome (MetS); however, there is limited evidence of such a relationship in the literature. This cross-sectional study investigated the relationships among dental caries experience, dietary habits, and MetS in Japanese adults. A total of 937 participants aged 40-74 years underwent a health check, including dental examination.

Association between eating behavior and poor glycemic control in Japanese adults.

This study investigated the relationship between eating behavior and poor glycemic control in 5,479 Japanese adults with hemoglobin A1c (HbA1c) <6.5% who participated in health checks. Respondents to a 2013 baseline survey of eating behavior, including skipping breakfast and how quickly they consumed food were followed up until 2017.