Guided and magnetic self-assembly of tunable magnetoceptive gels

Self-assembly of components into complex functional patterns at microscale is common in nature, and used increasingly in numerous disciplines such as optoelectronics, microfabrication, sensors, tissue engineering and computation. Here, we describe the use of stable radicals to guide the self-assembly of magnetically tunable gels, which we call ‘magnetoceptive’ materials at the scale of hundreds of microns to a millimeter, each can be programmed by shape and composition, into heterogeneous complex structures. Using paramagnetism of free radicals as a driving mechanism, complex heterogeneous structures are built in the magnetic field generated by permanent magnets. The overall magnetic signature of final structure is erased via an antioxidant vitamin E, subsequent to guided self-assembly. We demonstrate unique capabilities of radicals and antioxidants in fabrication of soft systems with heterogeneity in material properties, such as porosity, elastic modulus and mass density; then in bottom-up tissue engineering and finally, levitational and selective assembly of microcomponents

Untethered micro-robotic coding of three-dimensional material composition

Complex functional materials with three-dimensional micro- or nano-scale dynamic compositional features are prevalent in nature. However, the generation of three-dimensional functional materials composed of both soft and rigid microstructures, each programmed by shape and composition, is still an unsolved challenge. Herein, we describe a method to code complex materials in three-dimensions with tunable structural, morphological, and chemical features using an untethered magnetic micro-robot remotely controlled by magnetic fields. This strategy allows the micro-robot to be introduced to arbitrary microfluidic environments for remote two- and three-dimensional manipulation. We demonstrate the coding of soft hydrogels, rigid copper bars, polystyrene beads, and silicon chiplets into three-dimensional heterogeneous structures. We also use coded microstructures for bottom-up tissue engineering by generating cell-encapsulating constructs

3D Printed Microfluidic Devices

3D printing has revolutionized the microfabrication prototyping workflow over the past few years. With the recent improvements in 3D printing technologies, highly complex microfluidic devices can be fabricated via single-step, rapid, and cost-effective protocols as a promising alternative to the time consuming, costly and sophisticated traditional cleanroom fabrication. Microfluidic devices have enabled a wide range of biochemical and clinical applications, such as cancer screening, micro-physiological system engineering, high-throughput drug testing, and point-of-care diagnostics. Using 3D printing fabrication technologies, alteration of the design features is significantly easier than traditional fabrication, enabling agile iterative design and facilitating rapid prototyping. This can make microfluidic technology more accessible to researchers in various fields and accelerates innovation in the field of microfluidics. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on novel methodological developments in 3D printing and its use for various biochemical and biomedical applications

ISOLATED PERIORBITAL EDEMA ASSOCIATED WITH NAPROXEN: A CASE REPORT

Hypersensitivity to nonsteroidal anti-inflammatory drugs (NSAIDs), resulting in urticaria and angioedema, is being observedwith increasing frequency partly due to the large size of the exposed (at risk) population. Prevalence rates range from 0.1–0.3%. Facial angioedema constitutes the most common form of clinical presentation, and one-third of the patients show amixed clinical pattern of cutaneous (urticaria and/or angioedema) and respiratory symptoms which include upper respiratorytract edema, rhinorrhea, cough, breathlessness and tearing. But to the best of our knowledge there is no isolated periorbitaledema reported to date due to naproxen in the literature. In this report, a 62-year-old woman who developed reversiblebilateral periorbital edema after naproxen ingestion was presented. The periorbital edema due to NSAIDs was discussed

Analysis of vaginal microbicide film hydration kinetics by quantitative imaging refractometry

We have developed a quantitative imaging refractometry technique, based on holographic phase microscopy, as a tool for investigating microscopic structural changes in water-soluble polymeric materials. Here we apply the approach to analyze the structural degradation of vaginal topical microbicide films due to water uptake. We implemented transmission imaging of 1-mm diameter film samples loaded into a flow chamber with a 1.5×2 mm field of view. After water was flooded into the chamber, interference images were captured and analyzed to obtain high resolution maps of the local refractive index and subsequently the volume fraction and mass density of film material at each spatial location. Here, we compare the hydration dynamics of a panel of films with varying thicknesses and polymer compositions, demonstrating that quantitative imaging refractometry can be an effective tool for evaluating and characterizing the performance of candidate microbicide film designs for anti-HIV drug delivery. © 2014 Rinehart et al

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

High Resolution Köppen-Geiger Climate Zones of Türkiye

The K & ouml;ppen-Geiger (K-G) climate classification is the most commonly used climate classification method in the world, and there are many K-G climate classification studies focusing on T & uuml;rkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K-G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate-based studies to achieve accurate results. In this study, the K-G climate types of T & uuml;rkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in T & uuml;rkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co-occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub-types within the same main climate type in T & uuml;rkiye. In this study, precipitation and temperature values in the CHELSA database were modified by using meteorological station records and K & ouml;ppen-Geiger climate zones map of Turkey was produced.imageTurkish Academy of SciencesThis study is based on the findings of E.T.'s doctoral thesis titled Geographic Information Systems Based Classification of Ecoregions of Tuerkiye, but the thesis has not been published yet. M.Z.OE. thanks to the Turkish Academy of Sciences for their support within the framework of the Outstanding Young Scientist Award Program (TUBA- GEBIP- 2023

Nanotechnology-based electrochemical biosensors for monitoring breast cancer biomarkers

Breast cancer is categorized as the most widespread cancer type among women globally. On-time diagnosis can decrease the mortality rate by making the right decision in the therapy procedure. These features lead to a reduction in medication time and socio-economic burden. The current review article provides a comprehensive assessment for breast cancer diagnosis using nanomaterials and related technologies. Growing use of the nano/biotechnology domain in terms of electrochemical nanobiosensor designing was discussed in detail. In this regard, recent advances in nanomaterial applied for amplified biosensing methodologies were assessed for breast cancer diagnosis by focusing on the advantages and disadvantages of these approaches. We also monitored designing methods, advantages, and the necessity of suitable (nano) materials from a statistical standpoint. The main objective of this review is to classify the applicable biosensors based on breast cancer biomarkers. With numerous nano-sized platforms published for breast cancer diagnosis, this review tried to collect the most suitable methodologies for detecting biomarkers and certain breast cancer cell types

Machine learning-enabled multiplexed microfluidic sensors

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed

Smart-Phone Based Magnetic Levitation for Measuring Densities

Magnetic levitation, which uses a magnetic field to suspend objects in a fluid, is a powerful and versatile technology. We develop a compact magnetic levitation platform compatible with a smart-phone to separate micro-objects and estimate the density of the sample based on its levitation height. A 3D printed attachment is mechanically installed over the existing camera unit of a smart-phone. Micro-objects, which may be either spherical or irregular in shape, are suspended in a paramagnetic medium and loaded in a microcapillary tube which is then inserted between two permanent magnets. The micro-objects are levitated and confined in the microcapillary at an equilibrium height dependent on their volumetric mass densities (causing a buoyancy force toward the edge of the microcapillary) and magnetic susceptibilities (causing a magnetic force toward the center of the microcapillary) relative to the suspending medium. The smart-phone camera captures magnified images of the levitating micro-objects through an additional lens positioned between the sample and the camera lens cover. A custom-developed Android application then analyzes these images to determine the levitation height and estimate the density. Using this platform, we were able to separate microspheres with varying densities and calibrate their levitation heights to known densities to develop a technique for precise and accurate density estimation. We have also characterized the magnetic field, the optical imaging capabilities, and the thermal state over time of this platform