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Low yields of extracted cell-free DNA (cfDNA) from plasma limit continued development of liquid biopsy in cancer, especially in early-stage cancer diagnostics and cancer screening applications. We investigate a novel liquid-phase-based DNA isolation method that utilizes aqueous two-phase systems to purify and concentrate circulating cfDNA. The PHASIFY MAX and PHASIFY ENRICH kits were compared to a commonly employed solid-phase extraction method on their ability to extract cfDNA from a set of 91 frozen plasma samples from cancer patients. Droplet digital PCR (ddPCR) was used as the downstream diagnostic to detect mutant copies. Compared to the QIAamp Circulating Nucleic Acid (QCNA) kit, the PHASIFY MAX method demonstrated 60% increase in DNA yield and 171% increase in mutant copy recovery, and the PHASIFY ENRICH kit demonstrated a 35% decrease in DNA yield with a 153% increase in mutant copy recovery. A follow-up study with PHASIFY ENRICH resulted in the positive conversion of 9 out of 47 plasma samples previously determined negative with QCNA extraction (all with known positive tissue genotyping). Our results indicate that this novel extraction technique offers higher cfDNA recovery resulting in better sensitivity for detection of cfDNA mutations compared to a commonly used solid-phase extraction method.

Background Laser hemorrhoidoplasty has demonstrated significant therapeutic effectiveness. To diminish postoperative bleeding and enhance overall outcomes, we have additionally adopted suture ligating the feeding vessels. This study aimed to understand the treatment outcomes and any associated complications. Methods This study comprised patients with symptomatic grade II–III hemorrhoids who underwent laser hemorrhoidoplasty with feeding vessel suture ligation and Milligan–Morgan hemorrhoidectomy between 1 September 2020, and 31 August 2022. Surgical-related details, postoperative pain, discomfort after discharge, hemorrhoid recurrence, and any complications were collected from inpatient records, outpatient follow-ups, and telephone interviews. Initially, we will analyze the distinctions between the laser group and the traditional group, followed by an investigation into complications and satisfaction within the laser surgery subgroup. Results The study included 323 patients, with 173 undergoing laser hemorrhoidoplasty (LHP) and 150 undergoing Milligan–Morgan hemorrhoidectomy. Regarding pain assessment, the LHP group exhibited superior performance compared to traditional surgery at postoperative 4 h, before discharge, and during the first and second outpatient visits, with statistically significant differences. Additionally, the LHP group had a lower rate of urinary retention and experienced significantly less pain, with statistically significant differences. Conclusions Laser hemorrhoidoplasty with feeding vessels suture ligation has been shown to reduce postoperative pain and appears to be a promising minimally invasive treatment option for symptomatic grade II and III hemorrhoids.

Purpose In patients with chemotherapy, there is no consensus on the timing of ileostomy closure. Ileostomy reversal could improve the quality of life and minimise the long-term adverse events of delayed closure. In this study, we evaluated the impact of chemotherapy on ileostomy closure and searched for the predictive factors for complications. Methods We retrospectively analysed 212 patients with rectal cancer who underwent ileostomy closure surgery during and without chemotherapy and were consecutively enrolled between 2010 and 2016. As a result of the heterogeneity of the two groups, propensity score matching (PSM) was performed with a 1:1 PSM cohort. Results A total of 162 patients were included in the analysis. The overall stoma closure-related complications (12.4% vs. 11.1%, p  = 1.00) and major complications (2.5% vs. 6.2%, p  = 0.44) were not significantly different between the two groups. Multivariate analysis demonstrated that chronic kidney disease and bevacizumab use are risk factors for major complications. Conclusion Patients with oral or intravenous chemotherapy can safely have ileostomy closure with an adequate time delay from chemotherapy. When patients use bevacizumab, major complications related to ileostomy closure should still be cautioned.

The multi-sensor fusion scheme has become more and more popular these days with its great potential to estimate reliable navigation information for the modern development in automated driving system (ADS) and mobile mapping systems (MMS). Since these systems are combined with numerous navigation sensors, thus their geometric relationship should be precisely known. This study focuses on practical aspects when calibrating LiDAR-IMU mounting parameters (lever-arms and bore-sight angles) in land-based MMS. This calibration model is based on expressing the mounting parameters within the direct georeferencing equation for each epoch time and conditioning a set of INS/GNSS and LiDAR navigation solutions to lie on it. There is no need for a required information about the planar features in the calibration field as part of the unknowns. Such conditions are only benefitable in the residential area where the presence of sufficient planes in form of building is abundant. We present an approach for recovery the mounting parameters by conditioning the high-definition (HD) point cloud map-based LiDAR information and INS/GNSS navigation solutions through the least-squares solutions. The presented results and discussion mainly focus on practical examples with data from land-based MMS. Preliminary results indicate that correct calibration parameters are not only capable to improve the performance of point cloud georeferencing but also dramatically provide reliable performance evaluation of navigation estimation. Moreover, these findings show that the studied method is not only applicable in the featureless environment but also in its practicality to the self-driving applications.

In recent years, a lot of researchers have been trying the development of efficient ways to create HD maps with centimeter-level precision. Mobile mapping system (MMS) produce 3D HD point cloud map of the surrounding by integrating navigation (i.e., direct georeferencing or DG process) and high-resolution imaging sensor data. Unfortunately, in partially environments, the provided accuracy of the GNSS system degrades dramatically. In order to constraint the drift and correct the georeferenced point cloud map, ground control points (GCPs) are placed along the road. Moreover, there are approaches which use laser-based point cloud registration techniques to construct the point cloud map. However, all promising mapping techniques which currently use the laser as the core sensor for mapping the high-definition point cloud map may not be promised to construct the point cloud map in partially or unfriendly environments. As a literature review and result, a suitable approach for creating the promising point cloud map is to combine the INS/GNSS navigation solution, LiDAR matching techniques, and GCPs. Thus, this study introduces the HD point cloud map generation method that can potentially help researchers create personalized and globalized HD point cloud maps and develop new HD point cloud map generation methodologies.

In response to the rapid development of autonomous vehicles and the increasing demand for HD maps, the conventional mapping processes following HD maps guidelines require significant manpower and time resources. Therefore, we propose flexible procedures and methods for HD maps creation, aiming to reduce cost expenditure by employing diverse source of ground control point, sensor data collection, and mapping algorithm. This approach accelerates the production speed and capability of HD maps. In this study, we select Taiwan's National Highway No. 8 as the trial field for the elastic HD map construction method, and equipped with autonomous vehicle-grade GNSS, IMU, and LiDAR systems.We align the constructed-map data to the global coordinate system, in order to realize the concept of control point cloud map. To assess the assistance and correction capabilities of HD maps in autonomous vehicle navigation systems, we conduct accuracy evaluation through both direct and indirect methods, and analyse the strengths and weaknesses of each approach. The analysis result demonstrates that the elastic method-built HD maps not only meet the mapping accuracy requirements specified in the HD maps verification and validation guidelines, but also assist autonomous vehicles in realizing positioning, navigation, and timing with “where in lane” level (0.5 meter) accuracy.

To meet the autopilot demand of autonomous vehicle, higher automation level accompanies with higher consideration of safety factor to improve navigation accuracy. Moreover, it shall be stable under diverse environment, e.g., semi-open sky, urban, traffic jam, etc, where conventional navigation methods, the Inertial Measurement Unit (IMU) and global Navigation Satellite System (GNSS), might be limited. Thus, auxiliary sensor, the light detection and ranging (LiDAR), is applied to provide additional information to assist navigation under GNSS challenging environment, and fulfil Simultaneous Localization and Mapping (SLAM). To initially align the LiDAR point cloud, initial pose is generated by Extended Kalman Filter (EKF) through Loosely Coupled (LC) scheme, assisting with motion constraints, including Zero Velocity Update (ZUPT), Non-Holonomic Constraints (NHC), and Zero Integrated Heading Rate (ZIHR) function. With point cloud after initial alignment, registration method applied in this research is point to distribution based-Normal Distribution Transform (P2D-NDT), with scan to dynamic map matching. However, pure LiDAR-SLAM estimated solution remains faults in each measurement, which will propagate through computation and leads to false navigation outcome. Therefore, this paper proposed Fault Detection, Isolation, and Exclusion (FDIE) scheme to exclude the faults in each step of LiDAR-SLAM process. The final estimated solution is compared to robust reference data, the results turn out that convention navigation method work well under stable GNSS signal environment, while significant accuracy enhancement is achieved with NDT and FDE under large initial pose offset, such as GNSS signal blocked area.

Unilateral ureteral obstruction (UUO) is a well-characterized hydronephrosis model exhibiting interstitial inflammatory-cell infiltration and tubular dilatation followed by tubulointerstitial fibrosis of the obstructed kidney. Our recent report indicates that rapamycin is effective for 50% of transplant recipients with chronic allograft nephropathy. In this study, we investigate the effect of rapamycin on UUO-induced renal fibrosis. UUO or sham-operated rats were randomly assigned to rapamycin or vehicle and were killed on days 7 and 14 after UUO or sham operation. Rapamycin decreased cross-sectional and gross-morphology changes in the obstructed kidney significantly. Rapamycin markedly blunted the increase in weight of the obstructed kidney, obstructed kidney length, and the obstructed/non-obstructed kidney weight ratio (by 74.6, 42.8, and 61.6% on day 14, respectively, all P<0.01). The scores for tubular dilatation, interstitial volume, interstitial collagen deposition, and α-smooth muscle actin (α-SMA) after UUO were significantly reduced by rapamycin. Rapamycin also decreased the number of infiltrative anti-ED1-positive cells and the gene expression of transforming growth factor (TGF)-β1 (84.8 and 80.2% on day 7) after UUO (both P<0.01). By double immunostaining and Western analysis, rapamycin blocked the TGF-β1-induced loss of E-cadherin expression and de novo increase of the expression of α-SMA in a dose-dependent manner. In conclusion, rapamycin significantly attenuated tubulointerstitial damage in a UUO-induced rat model of renal fibrosis, suggesting that rapamycin may have the potential to delay the progression of tubulointerstitial renal fibrosis.

The paper-based immunoassay for point-of-care diagnostics is widely used due to its low cost and portability over traditional lab-based assays. Lateral-flow immunoassay (LFA) is the most well-established paper-based assay since it is rapid and easy to use. However, the disadvantage of LFA is its lack of sensitivity in some cases where a large sample volume is required, limiting its use as a diagnostic tool. To improve the sensitivity of LFA, we previously reported on the concentration of analytes into one of the two bulk phases of an aqueous two-phase system (ATPS) prior to detection. In this study, we preserved the advantages of LFA while significantly improving upon our previous proof-of-concept studies by employing a novel approach of concentrating gold nanoparticles, a common LFA colorimetric indicator. By conjugating specific antibodies and polymers to the surfaces of the particles, these gold nanoprobes (GNPs) were able to capture target proteins in the sample and subsequently be concentrated within 10 min at the interface of an ATPS solution comprised of polyethylene glycol, potassium phosphate, and phosphate-buffered saline. These GNPs were then extracted and applied directly to LFA. By combining this prior ATPS interface extraction with LFA, the detection limit of LFA for a model protein was improved by 100-fold from 1 ng/μL to 0.01 ng/μL. Additionally, we examined the behavior of the ATPS system in fetal bovine serum and synthetic urine to more closely approach real-world applications. Despite using more complex matrices, ATPS interface extraction still improved the detection limit by 100-fold within 15 to 25 min, demonstrating the system's potential to be applied to patient samples.

Autonomous driving relies on high accuracy point vector map, which was generated by the point cloud map, and pre-provides the vehicle preliminary road environment information. Lidar Odometry and Mapping (LOAM) has always been a promising research topic in the field of robotics, environment sensing, and currently autonomous driving. However, in certain urban environments like basement parking lot, tunnels, highways, or other similar settings, the geometric features are not clearly discernible. As a result, algorithms resembling the LOAM framework may encounter difficulties in accurately mapping these areas. The paper utilized relative low-cost LiDAR expecting to propose a state-of-the-art point cloud mapping/update scheme. We compared the GNSS-challenge area with straight line and loop area separately, simultaneously considered the DG, ICP, NDT matching algorithm for the low-cost mapping/update strategy. With the realistic experiment conduction, our result evaluated by point to point corresponding mean error and standard error. For the straight line environment, ICP has the fastest convergence in empirical cumulative distribution under 0.4 meters. For the loop scenario, point-to-point ICP still has the fastest convergence in empirical cumulative distribution under 0.22 meters. Yet both of them still suffer from the fault matching.