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Objectives: This research delves into the application of texture analysis in spine computed tomography (CT) scans and its correlation with bone mineral density (BMD), as determined by dual-energy X-ray absorptiometry (DXA). It specifically addresses the discordance between the 2 measurements, suggesting that certain spinal-specific factors may contribute to this discrepancy. Methods: The study involved 405 cases from a single institution collected between May 6, 2012 and June 30, 2021. Each case underwent a spinal CT scan and a DXA scan. BMD values at the lumbar region (T12 to S1) and total hip were recorded. Texture features from axial cuts of T12 to S1 vertebrae were extracted using gray-level co-occurrence matrices, and a regression model was constructed to predict the BMD values. Results: The correlation between CT texture analysis results and BMD from DXA was moderate, with a correlation coefficient ranging between 0.4 and 0.5. This discordance was examined in light of factors unique to the spine region, such as abdominal obesity, aortic calcification, and lumbar degenerative changes, which could potentially affect BMD measurements. Conclusions: Emerging from this study is a novel insight into the discordance between spinal CT texture analysis and DXA-derived BMD measurements, highlighting the unique influence of spinal attributes. This revelation calls into question the exclusive reliance on DXA scans for BMD assessment, particularly in scenarios where DXA scanning may not be feasible or accurate.

Abstract Current soft neural probes are still operated by bulky, rigid electronics mounted to a body, which deteriorate the integrity of the device to biological systems and restrict the free behavior of a subject. We report a soft, conformable neural interface system that can monitor the single-unit activities of neurons with long-term stability. The system implements soft neural probes in the brain, and their subsidiary electronics which are directly printed on the cranial surface. The high-resolution printing of liquid metals forms soft neural probes with a cellular-scale diameter and adaptable lengths. Also, the printing of liquid metal-based circuits and interconnections along the curvature of the cranium enables the conformal integration of electronics to the body, and the cranial circuit delivers neural signals to a smartphone wirelessly. In the in-vivo studies using mice, the system demonstrates long-term recording (33 weeks) of neural activities in arbitrary brain regions. In T-maze behavioral tests, the system shows the behavior-induced activation of neurons in multiple brain regions.

Abstract Purpose Below knee amputation (BKA) is a common surgical procedure for diabetic foot ulcers and necrotizing lower limb fasciitis patients. However, it is a painful procedure and inadequate postoperative analgesia impedes rehabilitation and prolongs hospitalization. An ideal pain management regimen should provide superior analgesia while minimizing opioid consumption and improving rehabilitation. Methods We retrospectively reviewed medical charts of 218 patients who underwent BKA for diabetic foot ulcer or necrotizing lower limb fasciitis at a single center between January 2017 and September 2020. Two groups were analyzed: patients who received dual nerve block (DNB) before surgery (Group I; n = 104), and patients who did not (Group II; n = 93). By the exclusion criteria, 21 patients were excluded. The femoral and sciatic nerves were each blocked separately under ultrasound guidance. This procedure was performed immediately before the operation. Results Group I patients' subjective pain scores were significantly lower than that of Group II at 6, 12, and 24 h after BKA (P

Background: This research explores the application of morphometric texture analysis in chest Computed Tomography (CT) scans for determining Bone Mineral Content (BMC) and its temporal changes, both crucial in diagnosing osteoporosis. The study establishes an innovative approach to osteoporosis screening by leveraging Hounsfield Units (HUs) in CT scans to evaluate BMC, offering a comparison with dual-energy X-ray absorptiometry (DXA)-based BMC. Methods: A total of 806 instances (encompassing 379 individuals) were meticulously compiled from a sole institution, during the period stretching from 6 May 2012 to 30 June 2020. In this detailed analysis, each participant was subjected to a pair of chest CT scans, sequentially pursued by a DXA scan, spread over two years. Focused records of BMC values at the inaugural lumbar vertebra (L1) were secured from both the DXA and CT axial slices across all instances. A meticulous selection process pinpointed the largest trabecular section from the L1 vertebral body, whereupon 45 distinctive texture attributes were harvested utilizing gray-level co-occurrence matrix methodologies. Utilizing these amassed 45 attributes, a regression architecture was devised, aiming to forecast the precise BMC values individually. Moreover, an alternative regression framework was engaged, leveraging 90 distinct features, to gauge the BMC fluctuations observed between the duo of scans administered to each participant. Results: The precision of the cultivated regression frameworks was scrupulously assessed, benchmarking against the correlation coefficient (CC) and the mean absolute deviation (MAE) in comparison to the DXA-established references. The regression apparatus employed for estimating BMC unveiled a CC of 0.754 and an MAE of 1.641 (g), respectively. Conversely, the regression mechanism devoted to discerning the variations in BMC manifested a CC of 0.680, coupled with an MAE of 0.528 (g), respectively. Conclusion: The innovative methodology utilizing morphometric texture analysis in CT HUs offers an indirect, yet promising, approach for osteoporosis screening by providing estimations of BMC and its temporal changes. The estimations demonstrate moderate positive correlations with DXA measures, suggesting a potential alternative in circumstances where DXA scanning is limited.

Abstract Recently, personalized medical diagnostics and treatments have received significant interest due to the rising demand for reliable, rapid, and cost‐effective healthcare services. In the recent development of wearable devices, ways to engineer devices to extend their reliability, minimize the risk of infection, and expand the scope of application have been focused on improving conventional clinical procedures. With the increasing interest in personalized healthcare, wearable devices have received substantial interest because they monitor each physiological parameter for specific clinical interest. These unique bio‐parameters of individuals can be recorded from different target organs for the analysis of various functions, ranging from simple bodily movement to clinical treatment. In this review, the recent advances in sensors for recording unique bio‐signals from the human body are discussed. Based on each unique bio‐signals, a comprehensive analysis of wearable platforms for clinical diagnosis is provided, including subjects such as the selection of materials, device design structure, and application strategies for therapeutic significance. Furthermore, the challenges and future potential direction of modern bio‐sensor devices are discussed extensively.

Abstract Electrical stimulation as a therapeutic approach is widely applicable in terms of target tissues or target effects. This method can be an alternative to conventional therapies for patients who are resistant to drugs or are ineligible for surgical operations. In addition, as researchers have actively studied how to adjust the parameters for electrical stimulation in order to improve effectiveness, many patients have already received treatments with electrical stimulation. With respect to devices for electrical stimulation, recent studies are focused on developing reliability for safe and long‐term operations. From the point of view of engineers, a comprehensive understanding of how electrical stimulation modulates the biological system is essential to develop advanced strategies that provide effective therapeutic results. Herein, the fundamental mechanisms for delivering electrical stimulation on biological tissues are reviewed along with the requirements that need to be qualified by the electrodes. Furthermore, the latest advances in electrical stimulation devices are discussed followed by an introduction of representative applications of therapeutic electrical stimulation.

Objective: This study aimed to develop a novel method for opportunistically screening osteoporosis by measuring bone mineral density (BMD) from CT images. We addressed the limitations of commercially available software and introduced texture analysis using Hounsfield units (HU) as an alternative approach. Methods: A total of 458 samples (296 patients) were selected from a dataset of 1320 cases (782 patients) between 1 March 2013, and 30 August 2022. BMD measurements were obtained from the ilium, femoral neck, intertrochanteric region of both femurs, and L1–L5 and sacrum spine body. The region of interest (ROI) for each patient’s CT scan was defined as the maximum trabecular area of the spine body, ilium, femoral neck, and femur intertrochanter. Using gray-level co-occurrence matrices, we extracted 45 texture features from each ROI. Linear regression analysis was employed to predict BMD, and the top five influential texture features were identified. Results: The linear regression (LR) model yielded correlation coefficients (R-squared values) for total lumbar BMD, total lumbar BMC, total femur BMD, total femur BMC, femur neck BMD, femur neck BMC, femur intertrochanter BMD, and femur intertrochanter BMC as follows: 0.643, 0.667, 0.63, 0.635, 0.631, 0.636, 0.68, and 0.68, respectively. Among the 45 texture features considered, the top five influential factors for BMD prediction were Entropy, autocorrelate_32, autocorrelate_32_volume, autocorrelate_64, and autocorrelate_64_volume.

Background: This study aimed to validate the application of CT texture analysis in estimating Bone Mineral Density (BMD) in patients with Type 2 Diabetes (T2D) and comparing it with the results of dual-energy X-ray absorptiometry (DXA) in a normative cohort. Methods: We analyzed a total of 510 cases (145 T2D patients and 365 normal patients) from a single institution. DXA-derived BMD and CT texture analysis-estimated BMD were compared for each participant. Additionally, we investigated the correlation among 45 different texture features within each group. Results: The correlation between CT texture analysis-estimated BMD and DXA-derived BMD in T2D patients was consistently high (0.94 or above), whether measured at L1 BMD, L1 BMC, total hip BMD, or total hip BMC. In contrast, the normative cohort showed a modest correlation, ranging from 0.66 to 0.75. Among the 45 texture features, significant differences were found in the Contrast V 64 and Contrast V 128 features in the normal group. Conclusion: In essence, our study emphasizes that the clinical assessment of bone health, particularly in T2D patients, should not merely rely on traditional measures, such as DXA BMD. Rather, it may be beneficial to incorporate other diagnostic tools, such as CT texture analysis, to better comprehend the complex interplay between various factors impacting bone health.

Small interfering RNAs (siRNAs) therapeutically induce RNA interference (RNAi) of disease-causing genes, but they also silence hundreds of seed-matched off-targets as behaving similar to microRNAs (miRNAs). miRNAs control the pathophysiology of tumors, wherein their accessible binding sites can be sequenced by Argonaute crosslinking immunoprecipitation (AGO CLIP). Herein, based on AGO CLIP, we develop potent anticancer siRNAs utilizing miRNA-like activity (mi/siRNAs). The mi/siRNAs contain seed sequences (positions 2–7) of tumor-suppressive miRNAs while maintaining perfect sequence complementarity to the AGO-accessible tumor target sites. Initially, host miRNA interactions with human papillomavirus 18 (HPV18) were identified in cervical cancer by AGO CLIP, revealing tumor-suppressive activity of miR-1/206 and miR-218. Based on the AGO-miRNA binding sites, mi/siRNAs were designed to target E6 and E7 (E6/E7) transcript with seed sequences of miR-1/206 (206/E7) and miR-218 (218/E7). Synergistic anticancer activity of 206/E7 and 218/E7 was functionally validated and confirmed via RNA sequencing and in vivo xenograft models (206/E7). Other mi/siRNA sequences were additionally designed for cervical, ovarian, and breast cancer, and available as an online tool (http://ago.korea.ac.kr/misiRNA); some of the mi/siRNAs were validated for their augmented anticancer activity (206/EphA2 and 206/Her2). mi/siRNAs could coordinate miRNA-like activity with robust siRNA function, demonstrating the potential of AGO CLIP analysis for RNAi therapeutics.

Currently both central and local governments that are aware of the importance of conserving rivers, are making continuous efforts to solve the problem of non-point pollution sources (NPS) flowing into rivers. This study established a reactive web service app with a mobile application, to accommodate the need for systematic and scientific investigation and management of NPS. The developed investigation and management techniques incorporate all the PC-based administrative features, thereby improving development efficiency. A prototype of mobile GIS platform for a survey, inspection, and input was proposed, in which data related to pollution source locations and attributes can be effectively collected, stored, updated, and adjusted. To demonstrate, we conducted a quantitative analysis of river pollution source using GPS and GIS to show further details of the framework, which provides geographic information required to develop management technique of NPS flowing into rivers and related parameters in the form of a map. Furthermore, based on the interpolated maps and various environmental factors, the prediction of NPS as well as other parameters can be done. The research will not only improve the efficiency of investigation and management of NPS but also contribute to establishing a management policy, which covers quick decision-making and reaction to inflow accidents.

The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

Accurate and frequent update of surface water has been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from background based on a threshold value. Generally threshold is a fixed value but can be challenging in case of shades, hills, forest and urban areas. In such case, determination of threshold is done manually which is subjective and areal variation. In order to widen the difference between water and background with easier threshold selection, a combination of multiple water indices can be used. This could widen the gap between classes and the threshold sensitivity can be minimized. In this study, we summed Normalized Difference Water Index (NDWI), Modified NDWI, Water Ratio Index and Normalized Difference Vegetation Index to form a new raster and explore the efficiency of thresholding compared to individual indices on Landsat 8 Operational Land Imager (OLI) image of Nepal. The combined index showed much better separation of water with background and can be further used for automated binary classification of surface water. The process could be very useful in mapping surface water accurately.

New sets of satellite sensors are frequently being added to the constellation of remote sensing satellites. These new sets offer improved specification to collect imagery on-demand over specific locations and for specific purposes. The Korea Multi-Purpose Satellite (KOMPSAT) series of satellites is a multi-purposed satellite system developed by Korea Aerospace Research Institute (KARI). The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution multi-spectral imagery with infrared and high resolution electro-optical bands for geographical information systems applications in environmental, agricultural and oceanographic sciences as well as natural disasters. In this study, land cover classification of multispectral data was performed using four supervised classification methods: Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM), using a KOMPSAT-3A multi-spectral imagery with 2.2 m spatial resolution. The study area for this study was selected from southwestern region of South Korea, around Buan city. The training data for supervised classification was carefully selected by visual interpretation of KOMPSAT-3A imagery and field investigation. After classification, the results were then analyzed for the validation of classification accuracy by comparison with those of field investigation. For the validation, we calculated the User’s Accuracy (UA), Producer’s Accuracy (PA), Overall Accuracy (OA) and Kappa statistics from the error matrix to check the classification accuracy for each class obtained individually from different methods. Finally, the comparative analysis was done for the study area for various results of land cover classification using a KOMPSAT-3A multi-spectral imagery.

Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size.

Currently, the settlement of soft ground is measured using instruments operated by on-site workers. However, this method is expensive and inefficient in terms of data consistency, costeffectiveness, and utility. On the other hand, surveying using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is used in various fields. However, studies on its utility in soft ground are insufficient. Therefore, in this study, we examined the optimal method for creating digital elevation models (DEMs) for estimating the settlement of soft ground using UAV LiDAR survey data. This method involved selecting a coastal construction site as the study area and acquiring data through UAV LiDAR surveying. The acquired data were used to create DEMs through preprocessing and postprocessing. Settlement measurements obtained from onsite instruments and settlement estimates derived from DEMs created using various interpolation methods and grid sizes were compared and analyzed. Additionally, the utility of the created time-series DEMs in the settlement estimation of soft ground was evaluated. We proposed the optimal method for creating DEMs for estimating the settlement of soft ground and suggested methods to utilize the proposed time-series DEMs. Our research results show that the use of UAV LiDAR survey data can lead to the economical and efficient settlement estimation of soft ground. [ABSTRACT FROM AUTHOR]