Physiological Information Research Articles

Validation of a sensor system for the measurement of breath ammonia using selected-ion flow-tube mass spectrometry.

The measurement of trace breath gases is of growing interest for its potential to provide non-invasive physiological information in health and disease. While instrumental techniques such as selected-ion flow-tube mass spectrometry (SIFT-MS) can achieve this, these are less suitable for clinical application. Sensitive sensor-based systems for breath ammonia could be more widely deployed, but have proven challenging to develop. This work demonstrates the sequential analytical validation of an electrochemical impedance-based sensor system for the measurement of ammonia in breath using SIFT-MS. Qualitative and relative responses between the two methods were comparable, although there were consistent differences in absolute concentration. When tested in artificial breath ammonia, sensors had a relative impedance sensitivity of 3.43x10-5ppbv-1for each breath in the range of 249 to 1,653 ppbv (r2=0.87,p<0.05). When correlated with SIFT-MS using human breath (n=14), ammonia was detected in the range of 100 to 700 ppbv (r=0.78,p<0.001), demonstrating acceptable sensitivity, reproducibility and dynamic range for clinical application.

Advances in cardiac sounds monitoring enabled by triboelectric sensors

Cardiac sounds sensor as a noninvasive instrument plays a pivotal role in the early diagnosis of various heart disease. In recent years, triboelectric nanogenerators (TENGs) have been employed widely for sensing different physiological information benefiting from abundant materials choices, simplicity of construction and low cost. More importantly, the fast saturated constitutive characteristic of the contact-separation mode (C-S) TENG demonstrates ultrahigh sensitivity in the detection of micromechanical quantities than traditional piezoelectric sensing mechanism. This article presents an overview of the C-S TENG enabled self-powered cardiac sounds monitoring, focusing on the working principle, performance comparison between C-S TENG and piezoelectric generator (PEG), and related applications. The challenges and future research directions of cardiac sounds sensor and C-S TENG toward micro-displacement region have also been proposed.

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

AVENUE: A novel deep fake detection method based on temporal convolution network &amp; rPPG information

In Deep Learning (DL), an adversary creates Deepfakes by manipulating facial features to fool someone. The Deepfakes pose a security threat to anyone's privacy and a primary concern for our society. It can be detected by utilizing the texture and physiological properties of the face, like eye and lip movements; however, such methods are incompetent when Deepfakes are created using recent generative adversarial networks (GAN). Alternatively, remote Photoplethysmography (rPPG) information can be used for Deepfake detection because GANs neglect human physiological information for Deepfake generation. Such detection can be inaccurate when rPPG signals are affected by the noises induced by facial deformation and illumination variations. Furthermore, the exiting Deepfake detections are usually performed using sequential models, and such models fail to process the long sequence of temporal information. These issues are mitigated by our proposed method AVENUE , that is, \(A\) no \(V\) el d \(E\) epfake detectio \(N\) method based on temporal convol \(U\) tion n \(E\) twork &amp; rPPG information. For mitigating the noise issues in the rPPG signals, the proposed method detects and employs relatively stable clips of the input video for Deepfake detection. The stable clips are those clips that are least affected by facial deformations. Also, we use a modified Temporal convolution network to model the long sequence of Deepfake information rather than the sequential architectures. We performed the experimental result on publically available datasets of Deepfake videos. It demonstrates that our proposed method performs better than the existing rPPG-based Deepfake detection methods.

Coral-like Ti3C2Tx/PANI Binary Nanocomposite Wearable Enzyme Electrochemical Biosensor for Continuous Monitoring of Human Sweat Glucose

With the continuous advancement of contemporary medical technology, an increasing number of individuals are inclined towards self-monitoring their physiological health information, specifically focusing on monitoring blood glucose levels. However, as an emerging flexible sensing technique, continuous and non-invasive monitoring of glucose in sweat offers a promising alternative to conventional invasive blood tests for measuring blood glucose levels, reducing the risk of infection associated with blood testing. In this study, we fabricated a flexible and wearable electrochemical enzyme sensor based on a two-dimensional Ti3C2Tx MXene nanosheets and coral-like polyaniline (PANI) binary nanocomposite (denoted as Ti3C2Tx/PANI) for continuous, non-invasive, real-time monitoring of sweat glucose. The exceptional conductivity of Ti3C2Tx MXene nanosheets, in conjunction with the mutual doping effect facilitated by coral-like PANI, significantly enhances electrical conductivity and specific surface areas of Ti3C2Tx/PANI. Consequently, the fabricated sensor exhibits remarkable sensitivity (25.16 μA·mM−1·cm−2), a low detection limit of glucose (26 μM), and an extensive detection range (0.05 mM ~ 1.0 mM) in sweat. Due to the dense coral-like structure of Ti3C2Tx/PANI binary nanocomposite, a larger effective area is obtained to offer more active sites for enzyme immobilization and enhancing enzymatic catalytic activity. Moreover, the sensor demonstrates exceptional mechanical performance, enabling a 60° bend in practical applications, thus satisfying the rigorous demands of human sweat detection applications. The results obtained from continuous 60 min in vitro monitoring of sweat glucose levels demonstrate a robust correlation with the data of blood glucose levels collected by a commercial glucose meter. Furthermore, the fabricated Ti3C2Tx/PANI/GOx sensor demonstrated agreement with HPLC findings regarding the actual concentration of added glucose. This study presents an efficient and practical approach for the development of a highly reliable MXene glucose biosensor, enabling stable and long-term monitoring of glucose levels in human sweat.

A Flexible Smart Healthcare Platform Conjugated with Artificial Epidermis Assembled by Three-Dimensionally Conductive MOF Network for Gas and Pressure Sensing

The rising flexible and intelligent electronics greatly facilitate the noninvasive and timely tracking of physiological information in telemedicine healthcare. Meticulously building bionic-sensitive moieties is vital for designing efficient electronic skin with advanced cognitive functionalities to pluralistically capture external stimuli. However, realistic mimesis, both in the skin’s three-dimensional interlocked hierarchical structures and synchronous encoding multistimuli information capacities, remains a challenging yet vital need for simplifying the design of flexible logic circuits. Herein, we construct an artificial epidermal device by in situ growing Cu3(HHTP)2 particles onto the hollow spherical Ti3C2Tx surface, aiming to concurrently emulate the spinous and granular layers of the skin’s epidermis. The bionic Ti3C2Tx@Cu3(HHTP)2 exhibits independent NO2 and pressure response, as well as novel functionalities such as acoustic signature perception and Morse code-encrypted message communication. Ultimately, a wearable alarming system with a mobile application terminal is self-developed by integrating the bimodular senor into flexible printed circuits. This system can assess risk factors related with asthmatic, such as stimulation of external NO2 gas, abnormal expiratory behavior and exertion degrees of fingers, achieving a recognition accuracy of 97.6% as assisted by a machine learning algorithm. Our work provides a feasible routine to develop intelligent multifunctional healthcare equipment for burgeoning transformative telemedicine diagnosis.

Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.

Directly monitoring the dynamic in vivo metabolisms of hyperpolarized 13C-oligopeptides.

Peptides play essential roles in biological phenomena, and, thus, there is a growing interest in detecting in vivo dynamics of peptide metabolisms. Dissolution-dynamic nuclear polarization (d-DNP) is a state-of-the-art technology that can markedly enhance the sensitivity of nuclear magnetic resonance (NMR), providing metabolic and physiological information in vivo. However, the hyperpolarized state exponentially decays back to the thermal equilibrium, depending on the spin-lattice relaxation time (T1). Because of the limitation in T1, peptide-based DNP NMR molecular probes applicable in vivo have been limited to amino acids or dipeptides. Here, we report the direct detection of in vivo metabolic conversions of hyperpolarized 13C-oligopeptides. Structure-based T1 relaxation analysis suggests that the C-terminal [1-13C]Gly-d2 residue affords sufficient T1 for biological uses, even in relatively large oligopeptides, and allowed us to develop 13C-β-casomorphin-5 and 13C-glutathione. It was found that the metabolic response and perfusion of the hyperpolarized 13C-glutathione in the mouse kidney were significantly altered in a model of cisplatin-induced acute kidney injury.

CATransformer: A Cycle-Aware Transformer for High-Fidelity ECG Generation From PPG.

Electrocardiography (ECG) is the gold standard for monitoring heart function and is crucial for preventing the worsening of cardiovascular diseases (CVDs). However, the inconvenience of ECG acquisition poses challenges for long-term continuous monitoring. Consequently, researchers have explored non-invasive and easily accessible photoplethysmography (PPG) as an alternative, converting it into ECG. Previous studies have focused on peaks or simple mapping to generate ECG, ignoring the inherent periodicity of cardiovascular signals. This results in an inability to accurately extract physiological information during the cycle, thus compromising the generated ECG signals' clinical utility. To this end, we introduce a novel PPG-to-ECG translation model called CATransformer, capable of adaptive modeling based on the cardiac cycle. Specifically, CATransformer automatically extracts the cycle using a cycle-aware module and creates multiple semantic views of the cardiac cycle. It leverages a transformer to capture detailed features within each cycle and the dynamics across cycles. Our method outperforms existing approaches, exhibiting the lowest RMSE across five paired PPG-ECG databases. Additionally, extensive experiments are conducted on four cardiovascular-related tasks to assess the clinical utility of the generated ECG, achieving consistent state-of-the-art performance. Experimental results confirm that CATransformer generates highly faithful ECG signals while preserving their physiological characteristics.

Use of [&lt;sup&gt;18&lt;/sup&gt;F]FDG/PET to access the rosmarinic acid anti-inflammatory effect in a mouse sponge implant model

Positron emission tomography (PET) is one of the most sensitive and effective imaging techniques for detecting very low concentrations of specific radiotracers. It is suitable for analyzing biochemical, metabolic, physiological and functional information at the molecular level in vivo and non-invasively. PET using 2-deoxy-2[18F]-fluoro-D-glucose ([18F]FDG) can be used to identify sites of inflammation and other pathologies and to monitor the efficacy of treatment. This technique can be used to study the effect of rosmarinic acid (RA) in various preclinical models. RA is a natural compound with promising antioxidant and anti-inflammatory effects. Therefore, the aim of this study is to investigate the anti-inflammatory potential of RA and the efficacy of PET/[18F]FDG in localizing inflammation and assessing response to treatment. A sponge implant model in mice was used for this purpose. All experiments were performed with male BALB/c mice aged 6 to 9 weeks and weighing 18 to 22 g, with 5 animals per group (10 animals in total throughout the study). Methods: The mice were implanted with sponge-like polyurethane discs to induce local inflammation. The animals were divided into two experimental groups: those treated with a vehicle and those treated with RA. PET scans with [18F]FDG were performed to evaluate the inflammatory process. Results: The results obtained with [18F]FDG showed a decrease in inflammatory cell infiltrates in the sponge after treatment with RA. Conclusion: The use of [18F]FDG demonstrated its efficacy in quantitatively evaluating the inflammatory process in the mouse subcutaneous sponge model as well as the anti-inflammatory effect of RA.

Electret mechano-sensor array integrated with tribopotential-modulated thin film transistors for precise spatiotemporal pressure perception

Spatiotemporal pressure perception is a key function of electronic skins. Electret mechano-sensors, as active sensors for E-skins, have garnered significant interest, with outstanding sensing performance in dynamic pressure measurement. However, their high output impedance poses a challenge in accurately and simply measuring low-frequency or static pressure distribution. Herein, we proposed an electret mechano-sensor array, in which each sensing cell is based on an electret sensor integrated with an IGZO TFT. The high input impedance of the TFT facilitates the sensor signal readout and enables the measurement of static pressure. The super-linear amplification feature of the TFTs improves the sensitivity and linearity of the sensors. An IGZO TFT layer with the lower electrodes of the electret sensor was fabricated through microfabrication process and assembled with a pre-charged electret film to achieve a mechano-sensor array with a high density. The fabricated TFT-integrated electret mechano-sensor array has achieved pattern detection and spatial tactile perception. Moreover, the sensor array can record spatiotemporal static pressure and arterial pulse waves simultaneously, exhibiting high resolution in detecting subtle changes and potentially offering various physiological information regarding the cardiovascular system. This work demonstrated that the proposed mechano-sensor array has capabilities for broad-scale pressure distribution detection in healthcare monitoring and human-computer interaction.

Miniaturized photoacoustic microscope for multi-segmental spinal cord imaging in freely moving mice.

Long-term and non-narcotic hemodynamic imaging is indispensable for observing factual physiological information of the spinal cord. Unfortunately, achieving label-free, high-resolution, and widefield spinal cord imaging for mice under freely moving conditions is challenging. In this study, we developed a miniaturized photoacoustic microscope along with a corresponding photoacoustic spinal window to realize high-resolution, multi-segmental hemodynamic imaging of the spinal cord for freely moving mice. The microscope has an outer size of 32 mm × 23 mm × 10 mm, a weight of 5.8 g, and a 4.4 µm lateral resolution within an effective field of view (FOV) of 2.6 mm × 1.8 mm. To eliminate the off-focus phenomena during spinal imaging, the microscope is equipped with a miniature motor to adapt the focal plane. Besides, the microscope is slidable along a customized rail on the window to expand the FOV. We evaluated the stability of the microscope and analyzed vascular images of the spinal cord under various physiological states. The results suggest that the microscope is capable of performing stable, multi-segmental spinal cord imaging in freely moving mice, offering new insights into spinal cord hemodynamics and neurovascular coupling research.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine.

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Growth pattern and turnover of carbon and nitrogen measured by stable isotope ratios in the hair of sika deer (Cervus nippon).

Hair is known to preserve diet history and other physiological information during its growth period and is often used in chemical analyses. However, the growth patterns and turnover of hair vary according to the animal species or habitat, so understanding these patterns in the target animal is necessary for interpreting the results of hair analyses. In this study, we aimed to elucidate the growth pattern and dietary information of winter coat hair in captive sika deer (Cervus nippon). Experiments involving hair-staining and shaving were conducted to elucidate the growth pattern of sika deer hair. A diet-switching experiment was conducted to ascertain what dietary information is reflected in the carbon and nitrogen stable isotope values of the deer winter coat. Hair samples collected from each body site (head, shoulder, back, and hip) were analyzed using an elemental analyser interfaced with an isotope-ratio mass spectrometer. The winter coat grows from early September to early November, and then stops after that. During the growth period of the winter coat, the hair of the shoulder and back grew at a constant rate. The carbon and nitrogen stable isotope values of hair reflected the deer's feeding history during hair growth, but there seemed to be a time lag in the hip hair. The results suggest that the guard hair of the shoulder is suitable for hair analysis in sika deer. The obtained information on growth patterns and isotopic change of hair from captive sika deer according to diet be useful for interpreting the results of future analyses using hair samples of wild deer.

Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

Relationship between psychologican and physiological responses to broadband water sounds

This study aimed to investigate the effects of reproducing an ultrasonic sound above 20 kHz on the subjective impressions of water sounds using psychological and physiological information obtained by the semantic differential method and electroencephalography (EEG), respectively. The results indicated that the ultrasonic component affected the subjective impression of the water sounds. In addition, regarding the relationship between psychological and physiological aspects, a moderate correlation was confirmed between the EEG change rate and subjective impressions. However, no differences in characteristics were found between with and without the ultrasound component, suggesting that ultrasound does not directly affect the relationship between subjective impressions and EEG energy at the current stage. Furthermore, the correlations calculated for the left and right channels in the occipital region differed significantly, which suggests functional asymmetry for sound perception between the right and left hemispheres.

Microbial single-cell applications under anoxic conditions.

The field of microbiology traditionally focuses on studying microorganisms at the population level. Nevertheless, the application of single-cell level methods, including microfluidics and imaging techniques, has revealed heterogeneity within populations, making these methods essential to understand cellular activities and interactions at a higher resolution. Moreover, single-cell sorting has opened new avenues for isolating cells of interest from microbial populations or complex microbial communities. These isolated cells can be further interrogated in downstream single-cell "omics" analyses, providing physiological and functional information. However, applying these methods to study anaerobic microorganisms under in situ conditions remains challenging due to their sensitivity to oxygen. Here, we review the existing methodologies for the analysis of viable anaerobic microorganisms at the single-cell level, including live-imaging, cell sorting, and microfluidics (lab-on-chip) applications, and we address the challenges involved in their anoxic operation. Additionally, we discuss the development of non-destructive imaging techniques tailored for anaerobes, such as oxygen-independent fluorescent probes and alternative approaches.

Commentary: Environmental RNA and the assessment of organismal function in the field

Environmental RNA (eRNA) is an emerging technique with significant potential for the assessment of organismal function in field settings. It has the advantage of being non-invasive, facilitating insight into the physiological status of an organism without complications associated with processes such as capture, handling, and transportation from the field to the laboratory. It is hypothesised that eRNA approaches will be especially valuable for assessing sublethal stress of species living in environmental settings undergoing change and could therefore be integral for examining population health and for testing hypotheses regarding organismal physiology developed from laboratory studies. However, the successful application of eRNA approaches requires further data regarding the stability and persistence of eRNA in natural substrates; established and validated relationships between molecular biomarkers and the physiological processes they participate in; and an understanding of the contributions of different epithelia in direct contact with the environment (skin, gill, gut) to the eRNA transcriptome. The utility of microRNA as a component of the eRNA pool should be an area of specific future research focus. Ultimately, eRNA has the potential to provide fundamental physiological information regarding the responses of organisms in their natural settings and could increase the sensitivity and acuity of biomonitoring efforts.

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing.

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

Microbacterium rhizophilus sp. nov., an indole acetic acid-producing actinobacterium isolated from rhizosphere soil.

A novel gram-stain-positive, short rod, aerobic, non-motile and non-spore-forming actinobacterial strain, designated GXG1230T was isolated from the rhizosphere soil of a coastal mangrove forest in Beihai city, Guangxi Zhuang Autonomous Region, PR China. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain GXG1230T was affiliated with the genus Microbacterium. Additionally, it demonstrated a high degree of similarity to Microbacterium paludicola US15T (97.9%) and Microbacterium marinilacus YM11-607T (97.3%). Chemotaxonomic characteristics showed that the whole-cell sugars were glucose, xylose, rhamnose and galactose. Menaquinones MK-11 and MK-12 were detected as respiratory quinones. Lysine was found in the peptidoglycan hydrolysate and the polar lipids were diphosphatidylglycerol, one phospholipid and two unidentified glycolipid. The major fatty acids were anteiso-C15:0, iso-C16:0 and anteiso-C17:0. The strain GXG1230T exhibited a genomic DNA G + C content of 71.7%. Furthermore, the average nucleotide identity values of GXG1230T with the reference strains were 75.4% and 81.9%, respectively, while the digital DNA-DNA hybridization values were 20.1% and 25.0%. Based on physiological, chemotaxonomic and phylogenetic information, strain GXG1230T is considered to represent a novel species of the genus Microbacterium, for which the name Microbacterium rhizophilus sp.nov is proposed, with GXG1230T (= MCCC 1K09302T = KCTC 59252T) as the type strain.