Biosensors for Klebsiella pneumoniae with Molecularly Imprinted Polymer (MIP) Technique.

Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 105 CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R2 value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL.

Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN.

Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.

Bone Mineral Density Screening System Using CMOS-Sensor X-ray Detector.

This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy X-ray absorptiometry (DEXA). An indirect X-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller X-ray apparatus is used as an X-ray source to capture two energy level X-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981.

Optical-Based Foot Plantar Pressure Measurement System for Potential Application in Human Postural Control Measurement and Person Identification.

Plantar pressure, the pressure exerted between the sole and the supporting surface, has great potentialities in various research fields, including footwear design, biometrics, gait analysis and the assessment of patients with diabetes. This research designs an optical-based foot plantar pressure measurement system aimed for human postural control and person identification. The proposed system consists of digital cameras installed underneath an acrylic plate covered by glossy white paper and mounted with LED strips along the side of the plate. When the light is emitted from the LED stripes, it deflects the digital cameras due to the pressure exerted between the glossy white paper and the acrylic plate. In this way, the cameras generate color-coded plantar pressure images of the subject standing on the acrylic-top platform. Our proposed system performs personal identification and postural control by extracting static and dynamic features from the generated plantar pressure images. Plantar pressure images were collected from 90 individuals (40 males, 50 females) to develop and evaluate the proposed system. In posture balance evaluation, we propose the use of a posture balance index that contains both magnitude and directional information about human posture balance control. For person identification, the experimental results show that our proposed system can achieve promising results, showing an area under the receiver operating characteristic (ROC) curve of 0.98515 (98.515%), an equal error rate (EER) of 5.8687%, and efficiency of 98.515%.

Noninvasive Portable Hemoglobin Concentration Monitoring System Using Optical Sensor for Anemia Disease.

Anemia is a condition in which red blood cells are not able to carry adequate oxygen to the body's tissues, and is widely found in nearly a quarter of the world population. The typical method to screen for the iron-deficiency anemia, which is the major anemia found in the world, is to implement a blood test called a complete blood count (CBC). However, even though this test gives a highly accurate result, it requires an invasive blood drawing and lab analyzing which could potentially cause physical pain, high risk of infection and take a long time to analyze. Therefore, this research presents an alternative method using an optical technique to measure hemoglobin concentration, which is the common indicator for diagnosing anemia. The light absorbance of the oxyhemoglobin at the wavelength of 660 nm and the deoxyhemoglobin at the wavelength of 880 nm were measured using the MAX30100 sensor. These wavelengths of light are obtained from red and infrared (IR) LEDs. The concept is based on the different absorption coefficients of blood at different electromagnetic wavelengths. This fact is used to indirectly calculate the hemoglobin concentration of blood through the modified Beer-Lambert law. Moreover, the result has been further converted to absolute hemoglobin concentration using a calibration curve derived from the cyanmethemoglobin test, which is the regular method for hemoglobin determination. Besides, the android application was also provided which can wirelessly record or monitor the data. The experiment shows that an accuracy of 90.9% can be achieved by our proposed noninvasive method. Therefore, the noninvasive portable hemoglobin concentration monitoring by the optical sensor has an acceptable result when compared with the invasive method, with less pain and lower risk of infection, as well as shorter processing time.

Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics.

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

Real-time multianalyte biosensors based on interference-free multichannel monolithic quartz crystal microbalance.

In this work, we design, fabricate and characterize a new interference-free multichannel monolithic quartz crystal microbalance (MQCM) platform for bio-sensing applications. Firstly, interference due to thickness-shear vibration mode coupling between channels in MQCM array is effectively suppressed by interposing a polydimethylsiloxane wall between adjacent QCM electrodes on a quartz substrate to form inverted-mesa-like structure. In addition, the electrical coupling due to the electrical impedance of solution is diminished by extending the flow path between them with an extended-design flow channel. The electrical testing results show that individual QCM signal is unaffected by those of adjacent channels under liquid loading, signifying the achievement of interference-free MQCM. The MQCM is applied for multi-analyte biosensing of IgG and HSA. The anti-IgG and anti-HSA are separately immobilized on two adjacent QCM electrodes, which are subsequently blocked with BSA to avoid unspecific binding. The MQCM biosensors are tested with single- and double-analyte solutions under continuous flow of buffer. The IgG and HSA QCM sensors only show frequency shift responses to their corresponding analytes and there are very small cross frequency shifts due to remnant unspecific binding. Moreover, MQCM sensors show approximately linear frequency shift response with analyte concentration. Therefore, the developed MQCM platform is promising for real-time interference-free label-free detection and quantification of multiple bio-analytes.

Use of computational fluid dynamics nasal airflow measurement to design septoplasty: a pilot study.

Deviation in the nasal septum that obstructs airflow is a source of discomfort to patients. Areas of nasal malformation then, need to be identified before performing surgery. In the present study, the authors introduce the computational fluid dynamic (CFD) technique to predict regions of limited airflow based on CT scan reconstruction of the nasal cavity. The present study proposes to use CFD to identify regions of obstructed airflow and design a surgical procedure to correct them. The authors report three cases with obstructed nasal airflow together with CFD measurements before and after the surgery. Results indicate that CFD is useful to verify the areas of airflow abnormality and conform with the results obtained using other methods.

Penalized-likelihood reconstruction for metal artifact reduction in cone-beam CT.

CT reconstruction from metal-embedded data usually produces streak artifacts that reduce the quality of the reconstructed images. In this paper, we propose a new technique for metal artifact reduction in cone-beam CT based on statistical reconstruction. First, the metal objects are segmented in the reconstructed images and then reprojected to obtain the measurement data of the metal objects using cone-beam reconstruction. The original measurement data in the metal area are corrected through cubic interpolation. y, the corrected measurement data are reconstructed with the penalized likelihood using the modified convex algorithm. The simulation results show that the reconstructed images of the metal object using the proposed metal artifact reduction technique are superior to conventional filtered backprojection reconstruction.

Invariant surface alignment in the presence of affine and some nonlinear transformations.

In this paper, we introduce a non-iterative geometric-based method to align 3D brain surfaces into standard coordinate system, which is based on a novel set of surface landmarks (e.g., inflection and/or zero torsion points residing on parabolic contours), which are intrinsic and are computed from the differential geometry of the surface. This is in contrast to existing methods that depend on anatomical landmarks that require expert intervention to locate--a very hard task. The landmarks are local and are preserved under affine transformations. To reduce the sensitivity of the landmarks to noise, we use a B-Spline surface representation that smoothes out the surface prior to the computation of the landmarks. The alignment is achieved by establishing correspondences between the landmarks after a sorting of the landmarks based on derived absolute invariants (volumes confined between parallelepipeds spanned by sets of the landmark point quadruplets). The method is tested for intra- and inter-brain alignments while entertaining affine, cubic and fourth-order polynomial nonlinear transformations using distance mapping as well as comparison with an expert alignment, and promising results are obtained. When comparing our automatic alignment with that of an expert we arrived at complete agreement for the more difficult case of partial alignment of sectional slab materials of five rats with an atlas (a whole brain of rat). This perfect alignment was only based on the surface structure for our procedure, whereas it was based on the staining and the external and internal structures for the expert.