Predicting vasovagal reactions to needles with anticipatory facial temperature profiles.

Around one-third of adults are scared of needles, which can result in adverse emotional and physical responses such as dizziness and fainting (e.g. vasovagal reactions; VVR) and consequently, avoidance of healthcare, treatments, and immunizations. Unfortunately, most people are not aware of vasovagal reactions until they escalate, at which time it is too late to intervene. This study aims to investigate whether facial temperature profiles measured in the waiting room, prior to a blood donation, can be used to classify who will and will not experience VVR during the donation. Average temperature profiles from six facial regions were extracted from pre-donation recordings of 193 blood donors, and machine learning was used to classify whether a donor would experience low or high levels of VVR during the donation. An XGBoost classifier was able to classify vasovagal groups from an adverse reaction during a blood donation based on this early facial temperature data, with a sensitivity of 0.87, specificity of 0.84, F1 score of 0.86, and PR-AUC of 0.93. Temperature fluctuations in the area under the nose, chin and forehead have the highest predictive value. This study is the first to demonstrate that it is possible to classify vasovagal responses during a blood donation using temperature profiles.

Eleven quick tips for running an interdisciplinary short course for new graduate students.

Quantitative reasoning and techniques are increasingly ubiquitous across the life sciences. However, new graduate researchers with a biology background are often not equipped with the skills that are required to utilize such techniques correctly and efficiently. In parallel, there are increasing numbers of engineers, mathematicians, and physical scientists interested in studying problems in biology with only basic knowledge of this field. Students from such varied backgrounds can struggle to engage proactively together to tackle problems in biology. There is therefore a need to establish bridges between those disciplines. It is our proposal that the beginning of graduate school is the appropriate time to initiate those bridges through an interdisciplinary short course. We have instigated an intensive 10-day course that brought together new graduate students in the life sciences from across departments within the National University of Singapore. The course aimed at introducing biological problems as well as some of the quantitative approaches commonly used when tackling those problems. We have run the course for three years with over 100 students attending. Building on this experience, we share 11 quick tips on how to run such an effective, interdisciplinary short course for new graduate students in the biosciences.

Image classification of unlabeled malaria parasites in red blood cells.

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Macromolecular crowding amplifies adipogenesis of human bone marrow-derived mesenchymal stem cells by enhancing the pro-adipogenic microenvironment.

The microenvironment plays a vital role in both the maintenance of stem cells in their undifferentiated state (niche) and their differentiation after homing into new locations outside this niche. Contrary to conventional in-vitro culture practices, the in-vivo stem cell microenvironment is physiologically crowded. We demonstrate here that re-introducing macromolecular crowding (MMC) at biologically relevant fractional volume occupancy during chemically induced adipogenesis substantially enhances the adipogenic differentiation response of human bone marrow-derived mesenchymal stem cells (MSCs). Both early and late adipogenic markers were significantly up-regulated and cells accumulated 25-40% more lipid content under MMC relative to standard induction cocktails. MMC significantly enhanced deposition of extracellular matrix (ECM), notably collagen IV and perlecan, a heparan sulfate proteoglycan. As a novel observation, MMC also increased the presence of matrix metalloproteinase -2 in the deposited ECM, which was concomitant with geometrical ECM remodeling typical of adipogenesis. This suggested a microenvironment that was richer in both matrix components and associated ligands and was conducive to adipocyte maturation. This assumption was confirmed by seeding undifferentiated MSCs on decellularized ECM deposited by adipogenically differentiated MSCs, Adipo-ECM. On Adipo-ECM generated under crowding, MSCs differentiated much faster under a classical differentiation protocol. This was evidenced throughout the induction time course, by a significant up-regulation of both early and late adipogenic markers and a 60% higher lipid content on MMC-generated Adipo-ECM in comparison to standard induction on tissue culture plastic. This suggests that MMC helps build and endow the nascent microenvironment with adipogenic cues. Therefore, MMC initiates a positive feedback loop between cells and their microenvironment as soon as progenitor cells are empowered to build and shape it, and, in turn, are informed by it to respond by attaining a stable differentiated phenotype if so induced. This work sheds new light on the utility of MMC to tune the microenvironment to augment the generation of adipose tissue from differentiating human MSCs.