Exosomes are small membrane vesicles released by a variety of cell types. Exosomes contain genetic materials, such as mRNAs and microRNAs (miRNAs), implying that they may play a pivotal role in ...cell-to-cell communication. Mesenchymal stem cells (MSCs), which potentially differentiate into multiple cell types, can migrate to the tumor sites and have been reported to exert complex effects on tumor progression. To elucidate the role of MSCs within the tumor microenvironment, previous studies have suggested various mechanisms such as immune modulation and secreted factors of MSCs. However, the paracrine effects of MSC-derived exosomes on the tumor microenvironment remain to be explored. The hypothesis of this study was that MSC-derived exosomes might reprogram tumor behavior by transferring their molecular contents. To test this hypothesis, exosomes from MSCs were isolated and characterized. MSC-derived exosomes exhibited different protein and RNA profiles compared with their donor cells and these vesicles could be internalized by breast cancer cells. The results demonstrated that MSC-derived exosomes significantly down-regulated the expression of vascular endothelial growth factor (VEGF) in tumor cells, which lead to inhibition of angiogenesis in vitro and in vivo. Additionally, miR-16, a miRNA known to target VEGF, was enriched in MSC-derived exosomes and it was partially responsible for the anti-angiogenic effect of MSC-derived exosomes. The collective results suggest that MSC-derived exosomes may serve as a significant mediator of cell-to-cell communication within the tumor microenvironment and suppress angiogenesis by transferring anti-angiogenic molecules.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). ...Based on Fourier neural operator, this study proposes VINO (Vehicle–Bridge Interaction Neural Operator) to serve as a surrogate model of bridge structures. VINO learns mappings between structural response fields and damage fields. In this study, vehicle–bridge interaction (VBI)–finite element (FE) data set was established by running parametric FE simulations of the VBI system, considering a random distribution of the structural initial damage field. Subsequently, vehicle‐bridge interaction (VB)–experimental (EXP) dataset was produced by conducting an experimental study under four damage scenarios. After VINO was pretrained by VBI‐FE and fine‐tuned by VBI‐EXP from the bridge at the healthy state, the model achieved the following two improvements. First, forward VINO can predict structural responses from damage field inputs more accurately than the FE model. Second, inverse VINO can determine, localize, and quantify damages in all scenarios, validating the accuracy and efficiency of data‐driven approaches.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Fluorescent molecular rotors (FMRs) can act as viscosity sensors in various media including subcellular organelles and microfluidic channels. In FMRs, the rotation of rotators connected to a ...fluorescent π‐conjugated bridge is suppressed by increasing environmental viscosity, resulting in increasing fluorescence (FL) intensity. In this minireview, we describe recently developed FMRs including push–pull type π‐conjugated chromophores, meso‐phenyl (borondipyrromethene) (BODIPY) derivatives, dioxaborine derivatives, cyanine derivatives, and porphyrin derivatives whose FL mechanism is viscosity‐responsive. In addition, FMR design strategies for addressing various issues (e.g., obtaining high FL contrast, internal FL references, and FL intensity‐contrast trade‐off) and their biological and microfluidic applications are also discussed.
Spinning around: This minireview discusses recently developed fluorescent molecular rotors (FMRs), which act as viscosity sensors in various media including subcellular organelles and microfluidic channels. In addition, the mechanism of viscosity‐responsive fluorescence, design strategy of FMRs for addressing remarkable issues, and their various biological and microfluidic applications, are also discussed.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a ...machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia.
This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation <95% at any point during surgery. Three common machine learning techniques were employed to develop models using the training dataset: gradient-boosting machine (GBM), long short-term memory (LSTM), and transformer. The performances of the models were compared using the area under the receiver operating characteristics curve using randomly assigned internal testing dataset. We also validated the developed models using temporal holdout dataset. Pediatric patient surgery cases between November 2020 and January 2021 were used. The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC).
In total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients' records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients' records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval CI 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P < .001) and the transformer model (0.885, 95% CI, 0.882-0.887, P < .001). In holdout validation, GBM also demonstrated best performance with an AUROC of 0.939 (95% CI 0.936 to 0.941) which was better than LSTM (0.904, 95% CI 0.900 to 0.907, P < .001) and the transformer model (0.929, 95% CI 0.926 to 0.932, P < .001).
Machine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Experimental validation of the change of frequencies during vehicle passage.•Uses appropriate time-frequency signal processing tools.•Separate investigation of free and forced vibration, leading to ...interesting results.•Investigation of vehicle with the same mass but different suspension properties.•The vehicle’s natural frequencies influence the amount of frequency shift in the beam.
The natural frequencies of coupled vehicle-bridge systems change with vehicle position. These changes are generally attributed to the contribution of the additional mass of the vehicle. However, other mechanical properties of the vehicle influence the evolution of the vehicle-bridge system frequencies, an aspect that has rarely been addressed. The aim of this paper is to further explore how frequencies vary during a vehicle passage and empirically show that the frequency shift depends also on the vehicle-to-bridge frequency ratio. The responses of a scaled model of a vehicle traversing a bridge are measured and analysed. The signals are processed in the time-frequency domain to assess the non-stationary and non-linear nature of the responses. The interpretation is supported with the predictions of a coupled vehicle and bridge numerical model. The results confirm different frequency shifts for vehicles with the same mass but different suspension properties. Furthermore, the direct (sensor on bridge) and indirect (sensor on vehicle) methods of extracting the bridge fundamental frequency are compared. The implications of these findings for indirect or drive-by bridge monitoring techniques are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A field experiment was conducted on a real steel truss bridge with four artificial damage scenarios.•Changes in the identified modal parameters due to the artificial damage were observed.•A damage ...detection approach in an order of feature extraction and discrimination was practicable.•Multiple modal frequencies were effective features sensitive to damage presence and severity.•Multiple MAC values and COMAC values were also effective features if modes are sufficient.
Bridge damage detection has become increasingly important, but few related techniques have been tested in situ on real bridges. For this study, a field experiment was conducted on an actual simply supported steel truss bridge with four artificial damage scenarios applied sequentially. Preliminary results of modal-parameter identification and vibration-based damage detection are then presented. For each scenario, modal frequencies and mode shapes of the bridge were identified with high precision and accuracy using a stabilization diagram-aided multivariate autoregressive analysis of vehicle-excited bridge vibrations. Changes in the identified modal parameters attributable to the artificial damage were observed. For modal frequencies, they decreased as damage causing high stress redistribution was applied, signifying a global stiffness loss. For mode shapes, both symmetric and anti-symmetric ones were distorted when the damage was applied asymmetrically, but no distortion was observed when damage was applied symmetrically. Moreover, a damage detection approach in an order of feature extraction and discrimination was verified to be practicable if the damage-sensitive feature was properly selected. Multiple modal frequencies, specifically the first three and four modal frequencies, were effective features because they were highly sensitive not only to the presence but also to the severity of the artificial damage. Multiple modal assurance criteria (MAC) values and coordinate modal assurance criteria (COMAC) values were also effective features that were sensitive to damage scenarios examined herein if sufficient modes were considered. However, neither a single frequency nor a single MAC value was as effective as multiple ones because each was sensitive to certain specific damage scenarios only. When damping ratios were taken as features, most of their combinations were slightly sensitive to the asymmetric damage, but none of those combinations, neither single nor multiple damping ratios, was sensitive to the symmetric damage.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Membrane distillation (MD) is an emerging thermally-driven technology that poses a lot of promise in desalination, and water and wastewater treatment. Developments in membrane design and the use of ...alternative energy sources have provided much improvement in the viability of MD for different applications. However, fouling of membranes is still one of the major issues that hounds the long-term stability performance of MD. Membrane fouling is the accumulation of unwanted materials on the surface or inside the pores of a membrane that results to a detrimental effect on the overall performance of MD. If not addressed appropriately, it could lead to membrane damage, early membrane replacement or even shutdown of operation. Similar with other membrane separation processes, fouling of MD is still an unresolved problem. Due to differences in membrane structure and design, and operational conditions, the fouling formation mechanism in MD may be different from those of pressure-driven membrane processes. In order to properly address the problem of fouling, there is a need to understand the fouling formation and mechanism happening specifically for MD. This review details the different foulants and fouling mechanisms in the MD process, their possible mitigation and control techniques, and characterization strategies that can be of help in understanding and minimizing the fouling problem. Abbreviations * AFM, atomic force microscopy * APS, Accelerated precipitation softening * AGMD, air gap membrane distillation * BSA, bovine serum albumin * CA, contact angle * CaCl sub(2), calcium chloride * CaCO sub(3), calcium carbonate * CaSO sub(4), calcium sulfate * CFU, colony forming unit * CLSM, confocal laser scanning microscopy * COD, chemical oxygen demand * DCMD, direct contact membrane distillation * DLVO, Derjaguin-Landau-Verwey-Overbeek * EDS, energy dispersive X-ray spectroscopy * EPS, extracellular polymeric substances * FeCl sub(3), ferric chloride * FlFFF, flow field-flow fractionation * FTIR, Fourier-transform infrared spectroscopy * HA, humic acid * HCl, hydrochloric acid * HPSEC, high pressure size exclusion chromatography * LC-OCD, liquid chromatography-organic carbon detection * LEP, liquid entry pressure * LGMD, liquid gap membrane distillation * LSI, Langelier saturation index * MB, methylene blue * MD, membrane distillation * MDBR, membrane distillation bioreactor * MEF, multi-effect distillation * MEMD, multi-effect membrane distillation * MF, microfiltration * MGMD, material gap membrane distillation * MMBF, macromolecular or biofouling * MSF, multistage flash * MWT, magnetic water treatment * NaCl, sodium chloride * NaOH, sodium hydroxide * Na sub(2)SO sub(4), sodium sulfate * NF, nanofiltration * NOM, natural organic matters * OMW, olive mill wastewater * PACl, poly-aluminum chloride * PAM, polypropylene acid ammonium * PP, polypropylene * PSD, pore size distribution * PTFE, polytetrafluoroethylene * PVDF, polyvinylidene fluoride * RCW, recirculating cooling water * RO, reverse osmosis * SEM, scanning electron microscopy * SGMD, sweeping gas membrane distillation * SI, saturation index * TCM, traditional Chinese medicine * TDS, total dissolved solids * TEM, transmission electron microscopy * TOC, total organic carbon * TPC, temperature polarization coefficient * UF, ultrafiltration * UTDR, ultrasonic time-domain reflectometry * VMD, vacuum membrane distillation * V-MEMD, vacuum multi-effect membrane distillation * XRD, X-ray diffraction
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Walnut anthracnose caused by Colletotrichum gloeosporioides is a deleterious disease that severely affects the production of walnut (Juglans regia L.). The aim of this study was to assess the ...antifungal and growth promotion activities of Bacillus velezensis CE 100 as an alternative to chemical use in walnut production. The crude enzyme from B. velezensis CE 100 exhibited chitinase, protease, and β-l,3-glucanase activity and degraded the cell wall of C. gloeosporioides, causing the inhibition of spore germination and mycelial growth by 99.3% and 33.6% at 100 µL/mL, respectively. The field application of B. velezensis CE 100 culture broth resulted in a 1.3-fold and 6.9-fold decrease in anthracnose disease severity compared to the conventional and control groups, respectively. Moreover, B. velezensis CE 100 produced indole-3-acetic acid (up to 1.4 µg/mL) and exhibited the potential for ammonium production and phosphate solubilization to enhance the availability of essential nutrients. Thus, field inoculation of B. velezensis CE 100 improved walnut root development, increased nutrient uptake, enhanced chlorophyll content, and consequently improved total biomass by 1.5-fold and 2.0-fold compared to the conventional and control groups, respectively. These results demonstrate that B. velezensis CE 100 is an effective biocontrol agent against anthracnose disease and a potential plant growth-promoting bacteria in walnut tree production.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This study proposes the new condition monitoring concept of using features in the measured rotation, or 'pitch' signal, of a crossing vehicle as an indicator of the presence of foundation scour in a ...bridge. The concept is explored through two-dimensional vehicle-bridge interaction modelling, with a reduction in stiffness under a pier used to represent the effects of scour. A train consisting of three 10-degree-of-freedom carriages cross the model on a profiled train track, each train varying slightly in terms of mass and velocity. An analysis of the pitch of the train carriages can clearly identify when scour is present. The concept is further tested in a scaled laboratory experiment consisting of a tractor-trailer crossing a four-span simply supported bridge on piers. The foundation support is represented by four springs under each pier, which can be replaced with springs of a reduced stiffness to mimic the effect of scour. The laboratory model also consistently shows a divergence in vehicle pitch between healthy and scoured bridge states.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory ...hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.2 mg/dL), mild-to-moderate (1.2-3.0 mg/dL), and severe (> 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland-Altman analysis showed a bias of 0.20 (95% limit of agreement, LoA: - 2.59 to 3.00) g/dL, 0.98 (95% LoA: - 1.38 to 3.35) g/dL, and 1.23 (95% LoA: - 1.16 to 3.63) g/dL for the normal, mild-to-moderate, and severe groups, respectively. The four-quadrant plot showed reliable trending ability in all groups (concordance rate > 92%). The rates of possible missed transfusion (SpHb > 7.0 g/dL for Hb < 7.0 g/dL) were higher in the hyperbilirubinemia groups (2%, 7%, and 12% for the normal, mild-to-moderate, and severe group, respectively. all P < 0.001). The possible over-transfusion rate was less than 1% in all groups. In conclusion, the use of SpHb in liver transplantation recipients with preoperative hyperbilirubinemia requires caution due to the positive bias and high risk of missed transfusion. However, the reliable trending ability indicated its potential use in clinical settings.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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