Phonon stability boundary and deep elastic strain engineering of lattice thermal conductivity.

Recent studies have reported the experimental discovery that nanoscale specimens of even a natural material, such as diamond, can be deformed elastically to as much as 10% tensile elastic strain at room temperature without the onset of permanent damage or fracture. Computational work combining ab initio calculations and machine learning (ML) algorithms has further demonstrated that the bandgap of diamond can be altered significantly purely by reversible elastic straining. These findings open up unprecedented possibilities for designing materials and devices with extreme physical properties and performance characteristics for a variety of technological applications. However, a general scientific framework to guide the design of engineering materials through such elastic strain engineering (ESE) has not yet been developed. By combining first-principles calculations with ML, we present here a general approach to map out the entire phonon stability boundary in six-dimensional strain space, which can guide the ESE of a material without phase transitions. We focus on ESE of vibrational properties, including harmonic phonon dispersions, nonlinear phonon scattering, and thermal conductivity. While the framework presented here can be applied to any material, we show as an example demonstration that the room-temperature lattice thermal conductivity of diamond can be increased by more than 100% or reduced by more than 95% purely by ESE, without triggering phonon instabilities. Such a framework opens the door for tailoring of thermal-barrier, thermoelectric, and electro-optical properties of materials and devices through the purposeful design of homogeneous or inhomogeneous strains.

Microfluidic study of retention and elimination of abnormal red blood cells by human spleen with implications for sickle cell disease.

The spleen clears altered red blood cells (RBCs) from circulation, contributing to the balance between RBC formation (erythropoiesis) and removal. The splenic RBC retention and elimination occur predominantly in open circulation where RBCs flow through macrophages and inter-endothelial slits (IESs). The mechanisms underlying and interconnecting these processes significantly impact clinical outcomes. In sickle cell disease (SCD), blockage of intrasplenic sickled RBCs is observed in infants splenectomized due to acute splenic sequestration crisis (ASSC). This life-threatening RBC pooling and organ swelling event is plausibly triggered or enhanced by intra-tissular hypoxia. We present an oxygen-mediated spleen-on-a-chip platform for in vitro investigations of the homeostatic balance in the spleen. To demonstrate and validate the benefits of this general microfluidic platform, we focus on SCD and study the effects of hypoxia on splenic RBC retention and elimination. We observe that RBC retention by IESs and RBC-macrophage adhesion are faster in blood samples from SCD patients than those from healthy subjects. This difference is markedly exacerbated under hypoxia. Moreover, the sickled RBCs under hypoxia show distinctly different phagocytosis processes from those non-sickled RBCs under hypoxia or normoxia. We find that reoxygenation significantly alleviates RBC retention at IESs, and leads to rapid unsickling and fragmentation of the ingested sickled RBCs inside macrophages. These results provide unique mechanistic insights into how the spleen maintains its homeostatic balance between splenic RBC retention and elimination, and shed light on how disruptions in this balance could lead to anemia, splenomegaly, and ASSC in SCD and possible clinical manifestations in other hematologic diseases.

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous tracking of the dynamics of T cell infiltration and cytotoxicity within the 3D tumor cultures with a tunable stromal makeup. By recourse to a clinical tumor-infiltrating lymphocyte (TIL) score analyzer, which is based on a clinical data-driven deep learning method, our platform can evaluate the efficacy of each treatment based on the scoring of T cell infiltration patterns. By screening a drug library using this technology, we identified an epigenetic drug (lysine-specific histone demethylase 1 inhibitor, LSD1i) that effectively promoted T cell tumor infiltration and enhanced treatment efficacy in combination with an immune checkpoint inhibitor (anti-PD1) in vivo. We demonstrated an automated system and strategy for screening immunocyte-solid tumor interactions, enabling the discovery of immuno- and combination therapies.

Two-component macrophage model for active phagocytosis with pseudopod formation.

Macrophage phagocytosis is critical for the immune response, homeostasis regulation, and tissue repair. This intricate process involves complex changes in cell morphology, cytoskeletal reorganization, and various receptor-ligand interactions controlled by mechanical constraints. However, there is a lack of comprehensive theoretical and computational models that investigate the mechanical process of phagocytosis in the context of cytoskeletal rearrangement. To address this issue, we propose a novel coarse-grained mesoscopic model that integrates a fluid-like cell membrane and a cytoskeletal network to study the dynamic phagocytosis process. The growth of actin filaments results in the formation of long and thin pseudopods, and the initial cytoskeleton can be disassembled upon target entry and reconstructed after phagocytosis. Through dynamic changes in the cytoskeleton, our macrophage model achieves active phagocytosis by forming a phagocytic cup utilizing pseudopods in two distinct ways. We have developed a new algorithm for modifying membrane area to prevent membrane rupture and ensure sufficient surface area during phagocytosis. In addition, the bending modulus, shear stiffness, and cortical tension of the macrophage model are investigated through computation of the axial force for the tubular structure and micropipette aspiration. With this model, we simulate active phagocytosis at the cytoskeletal level and investigate the mechanical process during the dynamic interplay between macrophage and target particles.

A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell disease.

Being the largest lymphatic organ in the body, the spleen also constantly controls the quality of red blood cells (RBCs) in circulation through its two major filtration components, namely interendothelial slits (IES) and red pulp macrophages. In contrast to the extensive studies in understanding the filtration function of IES, fewer works investigate how the splenic macrophages retain the aged and diseased RBCs, i.e., RBCs in sickle cell disease (SCD). Herein, we perform a computational study informed by companion experiments to quantify the dynamics of RBCs captured and retained by the macrophages. We first calibrate the parameters in the computational model based on microfluidic experimental measurements for sickle RBCs under normoxia and hypoxia, as those parameters are not available in the literature. Next, we quantify the impact of key factors expected to dictate the RBC retention by the macrophages in the spleen, namely, blood flow conditions, RBC aggregation, hematocrit, RBC morphology, and oxygen levels. Our simulation results show that hypoxic conditions could enhance the adhesion between the sickle RBCs and macrophages. This, in turn, increases the retention of RBCs by as much as four-fold, which could be a possible cause of RBC congestion in the spleen of patients with SCD. Our study on the impact of RBC aggregation illustrates a 'clustering effect', where multiple RBCs in one aggregate can make contact and adhere to the macrophages, leading to a higher retention rate than that resulting from RBC-macrophage pair interactions. Our simulations of sickle RBCs flowing past macrophages for a range of blood flow velocities indicate that the increased blood velocity could quickly attenuate the function of the red pulp macrophages on detaining aged or diseased RBCs, thereby providing a possible rationale for the slow blood flow in the open circulation of the spleen. Furthermore, we quantify the impact of RBC morphology on their tendency to be retained by the macrophages. We find that the sickle and granular-shaped RBCs are more likely to be filtered by macrophages in the spleen. This finding is consistent with the observation of low percentages of these two forms of sickle RBCs in the blood smear of SCD patients. Taken together, our experimental and simulation results aid in our quantitative understanding of the function of splenic macrophages in retaining the diseased RBCs and provide an opportunity to combine such knowledge with the current knowledge of the interaction between IES and traversing RBCs to apprehend the complete filtration function of the spleen in SCD.

Rapid time-lapse 3D oxygen tension measurements within hydrogels using widefield frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) and image segmentation.

Understanding the influence of oxygen tension on cellular functions and behaviors is crucial for investigating various physiological and pathological conditions. In vitro cell culture models, particularly those based on hydrogel extracellular matrices, have been developed to study cellular responses in specific oxygen microenvironments. However, accurately characterizing oxygen tension variations with great spatiotemporal resolutions, especially in three dimensions, remains challenging. This paper presents an approach for rapid time-lapse 3D oxygen tension measurements in hydrogels using a widely available inverted fluorescence microscope. Oxygen-sensitive fluorescent microbeads and widefield frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) are utilized for oxygen tension estimation. To incorporate the third dimension, a motorized sample stage is implanted that enables automated image acquisition in the vertical direction. A machine learning algorithm based on K-means clustering is employed for microbead position identification. Using an upside-down microfluidic device, 3D oxygen gradients are generated within a hydrogel sample, and z-stack images are acquired using the FD-FLIM system. Analyses of the acquired images, involving microbead position identification, lifetime calculation, and oxygen tension conversion, are then performed offline. The results demonstrate the functionality of the developed approach for rapid time-lapse 3D oxygen tension measurements in hydrogels. Furthermore, the 3D oxygen tension adjacent to a tumor spheroid within a hydrogel during media exchange is characterized. The results further confirm that the 3D spatiotemporal oxygen tension profiles can be successfully measured quantitatively using the established setup and analysis process and that the approach may have great potential for investigating cellular activities within oxygen microenvironments.

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro methods based on traditional microfluidic platforms have been developed to mimic physiological conditions. However, existing methods lack the capability to provide accurate assessment of these flow fields, particularly in vessels with complex geometries. Conventional approaches to quantify flow fields rely either on analyzing only visual images or on enforcing underlying physics without considering visualization data, which could compromise accuracy of predictions. Here, we present artificial-intelligence velocimetry (AIV) to quantify velocity and stress fields of blood flow by integrating the imaging data with underlying physics using physics-informed neural networks. We demonstrate the capability of AIV by quantifying hemodynamics in microchannels designed to mimic saccular-shaped microaneurysms (microaneurysm-on-a-chip, or MAOAC), which signify common manifestations of diabetic retinopathy, a leading cause of vision loss from blood-vessel damage in the retina in diabetic patients. We show that AIV can, without any a priori knowledge of the inlet and outlet boundary conditions, infer the two-dimensional (2D) flow fields from a sequence of 2D images of blood flow in MAOAC, but also can infer three-dimensional (3D) flow fields using only 2D images, thanks to the encoded physics laws. AIV provides a unique paradigm that seamlessly integrates images, experimental data, and underlying physics using neural networks to automatically analyze experimental data and infer key hemodynamic indicators that assess vascular injury.

Characterization of the strain-rate-dependent mechanical response of single cell-cell junctions.

Cell-cell adhesions are often subjected to mechanical strains of different rates and magnitudes in normal tissue function. However, the rate-dependent mechanical behavior of individual cell-cell adhesions has not been fully characterized due to the lack of proper experimental techniques and therefore remains elusive. This is particularly true under large strain conditions, which may potentially lead to cell-cell adhesion dissociation and ultimately tissue fracture. In this study, we designed and fabricated a single-cell adhesion micro tensile tester (SCAµTT) using two-photon polymerization and performed displacement-controlled tensile tests of individual pairs of adherent epithelial cells with a mature cell-cell adhesion. Straining the cytoskeleton-cell adhesion complex system reveals a passive shear-thinning viscoelastic behavior and a rate-dependent active stress-relaxation mechanism mediated by cytoskeleton growth. Under low strain rates, stress relaxation mediated by the cytoskeleton can effectively relax junctional stress buildup and prevent adhesion bond rupture. Cadherin bond dissociation also exhibits rate-dependent strengthening, in which increased strain rate results in elevated stress levels at which cadherin bonds fail. This bond dissociation becomes a synchronized catastrophic event that leads to junction fracture at high strain rates. Even at high strain rates, a single cell-cell junction displays a remarkable tensile strength to sustain a strain as much as 200% before complete junction rupture. Collectively, the platform and the biophysical understandings in this study are expected to build a foundation for the mechanistic investigation of the adaptive viscoelasticity of the cell-cell junction.

Diphosphine Ligand-Enabled Nickel-Catalyzed Chelate-Assisted Inner-Selective Migratory Hydroarylation of Alkenes.

The precise control of the regioselectivity in the transition metal-catalyzed migratory hydrofunctionalization of alkenes remains a big challenge. With a transient ketimine directing group, the nickel-catalyzed migratory ß-selective hydroarylation and hydroalkenylation of alkenyl ketones has been realized with aryl boronic acids using alkyl halide as the mild hydride source for the first time. The key to this success is the use of a diphosphine ligand, which is capable of the generation of a Ni(II)-H species in the presence of alkyl bromide, and enabling the efficient migratory insertion of alkene into Ni(II)-H species and the sequent rapid chain walking process. The present approach diminishes organosilanes reductant, tolerates a wide array of complex functionalities with excellent regioselective control. Moreover, this catalytic system could also be applied to the migratory hydroarylation of alkenyl azahetereoarenes, thus providing a general approach for the preparation of 1,2-aryl heteroaryl motifs with wide potential applications in pharmaceutical discovery.

In silico and in vitro study of the adhesion dynamics of erythrophagocytosis in sickle cell disease.

Erythrophagocytosis occurring in the spleen is a critical process for removing senescent and diseased red blood cells (RBCs) from the microcirculation. Although some progress has been made in understanding how the biological signaling pathways mediate the phagocytic processes, the role of the biophysical interaction between RBCs and macrophages, particularly under pathological conditions such as sickle cell disease, has not been adequately studied. Here, we combine computational simulations with microfluidic experiments to quantify RBC-macrophage adhesion dynamics under flow conditions comparable to those in the red pulp of the spleen. We also investigate the RBC-macrophage interaction under normoxic and hypoxic conditions. First, we calibrate key model parameters in the adhesion model using microfluidic experiments for normal and sickle RBCs under normoxia and hypoxia. We then study the adhesion dynamics between the RBC and the macrophage. Our simulation illustrates three typical adhesion states, each characterized by a distinct dynamic motion of the RBCs, namely firm adhesion, flipping adhesion, and no adhesion (either due to no contact with macrophages or detachment from the macrophages). We also track the number of bonds formed when RBCs and macrophages are in contact, as well as the contact area between the two interacting cells, providing mechanistic explanations for the three adhesion states observed in the simulations and microfluidic experiments. Furthermore, we quantify, for the first time to our knowledge, the adhesive forces between RBCs (normal and sickle) and macrophages under different oxygenated conditions. Our results show that the adhesive forces between normal cells and macrophages under normoxia are in the range of 33-58 pN and 53-92 pN for sickle cells under normoxia and 155-170 pN for sickle cells under hypoxia. Taken together, our microfluidic and simulation results improve our understanding of the biophysical interaction between RBCs and macrophages in sickle cell disease and provide a solid foundation for investigating the filtration function of the splenic macrophages under physiological and pathological conditions.

The Application Value of Contrast-Enhanced Ultrasonography in Assessing the Efficacy of Ultrasound-Guided Lauromacrogol Injections in Cesarean Scar Pregnancies.

OBJECTIVE: To investigate the application value of contrast-enhanced ultrasonography (CEUS) in ultrasound-guided lauromacrogol injections in patients with cesarean scar pregnancies (CSP). METHODS: A total of 31 patients diagnosed with CSP, who underwent an ultrasound-guided lauromacrogol injection + curettage in our hospital between February 2019 and December 2020 and had a complete recovery confirmed by a postoperative ultrasound review and serum ß-human chorionic gonadotropin (ß-hCG) assay, were enrolled as the study subjects. According to the volume of intraoperative blood loss and the duration of postoperative vaginal bleeding, the patients were divided into two groups, with 19 in the significantly effective group (Group A) and 12 in the effective group (Group B). The recorded clinical data, including age, duration of amenorrhea, number of pregnancies, number of deliveries, time since last cesarean delivery, number of cesarean deliveries, and preoperative serum ß-hCG levels, were retrospectively analyzed. The morphological indicators in CEUS before the lauromacrogol injection, as well as immediately and 12-24 hours after the injection, were compared between the groups. RESULTS: In Group A, the post-injection CEUS showed no enhancement, single strip enhancement, and sparse punctate enhancement, while in Group B, it showed a more irregular ring and local patch enhancement. In addition, the number of cases where the CEUS showed no enhancement 12-24 hours after the injection was more than that of the immediate CEUS after the injection. In Group A, four (21.1%) cases showed a single strip-like blood flow on the immediate postoperative CEUS, four (21.1%) cases showed a sparsely dotted blood flow on the immediate postoperative CEUS, and three cases turned into no enhancement 12-24 hours after the injection. A total of four cases in Group B showed that the contrast enhancement range 12-24 hours after the injection was reduced compared with that of the immediate contrast after the injection. CONCLUSION: Contrast-enhanced ultrasonography can guide the location selection of the lauromacrogol injection in patients with CSP, and its postoperative morphological indicators can adequately predict the therapeutic effect after curettage and guide clinical management.

Metallization of diamond.

Experimental discovery of ultralarge elastic deformation in nanoscale diamond and machine learning of its electronic and phonon structures have created opportunities to address new scientific questions. Can diamond, with an ultrawide bandgap of 5.6 eV, be completely metallized, solely under mechanical strain without phonon instability, so that its electronic bandgap fully vanishes? Through first-principles calculations, finite-element simulations validated by experiments, and neural network learning, we show here that metallization/demetallization as well as indirect-to-direct bandgap transitions can be achieved reversibly in diamond below threshold strain levels for phonon instability. We identify the pathway to metallization within six-dimensional strain space for different sample geometries. We also explore phonon-instability conditions that promote phase transition to graphite. These findings offer opportunities for tailoring properties of diamond via strain engineering for electronic, photonic, and quantum applications.

Extraction of mechanical properties of materials through deep learning from instrumented indentation.

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress-strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

Ligand-Enabled NiII -Catalyzed Hydroxylarylation of Alkenes with Molecular Oxygen.

The use of molecular oxygen as the terminal oxidant in transition metal catalyzed oxidative process is an appealing and challenging task in organic synthetic chemistry. Here, we report a Ni-catalyzed hydroxylarylation of unactivated alkenes enabled by a ß-diketone ligand with high efficiency and excellent regioselectivity employing molecular oxygen as the oxidant and hydroxyl source. This reaction features mild conditions, broad substrate scope and incredible heterocycle compatibility, providing a variety of ß-hydroxylamides, γ-hydroxylamides, ß-aminoalcohols, γ-aminoalcohols, and 1,3-diols in high yields. The synthetic value of this methodology was demonstrated by the efficient synthesis of two bioactive compounds, (±)-3'-methoxyl citreochlorol and tea catechin metabolites M4.

Microfluidic Size Exclusion Chromatography (µSEC) for Extracellular Vesicles and Plasma Protein Separation.

Extracellular vesicles (EVs) are recognized as next generation diagnostic biomarkers due to their disease-specific biomolecular cargoes and importance in cell-cell communications. A major bottleneck in EV sample preparation is the inefficient and laborious isolation of nanoscale EVs (≈50-200 nm) from endogenous proteins in biological samples. Herein, a unique microfluidic platform is reported for EV-protein fractionation based on the principle of size exclusion chromatography (SEC). Using a novel rapid (≈20 min) replica molding technique, a fritless microfluidic SEC device (µSEC) is fabricated using thiol-ene polymer (UV glue NOA81, Young's modulus ≈1 GPa) for high pressure (up to 6 bar) sample processing. Controlled on-chip nanoliter sample plug injection (600 nL) using a modified T-junction injector is first demonstrated with rapid flow switching response time (<1.5 s). Device performance is validated using fluorescent nanoparticles (50 nm), albumin, and breast cancer cells (MCF-7)-derived EVs. As a proof-of-concept for clinical applications, EVs are directly isolated from undiluted human platelet-poor plasma using µSEC and show distinct elution profiles between EVs and proteins based on nanoparticle particle analysis (NTA), Western blot and flow cytometry analysis. Overall, the optically transparent µSEC can be readily automated and integrated with EV detection assays for EVs manufacturing and clinical diagnostics.

Mechanical fatigue of human red blood cells.

Fatigue arising from cyclic straining is a key factor in the degradation of properties of engineered materials and structures. Fatigue can also induce damage and fracture in natural biomaterials, such as bone, and in synthetic biomaterials used in implant devices. However, the mechanisms by which mechanical fatigue leads to deterioration of physical properties and contributes to the onset and progression of pathological states in biological cells have hitherto not been systematically explored. Here we present a general method that employs amplitude-modulated electrodeformation and microfluidics for characterizing mechanical fatigue in single biological cells. This method is capable of subjecting cells to static loads for prolonged periods of time or to large numbers of controlled mechanical fatigue cycles. We apply the method to measure the systematic changes in morphological and biomechanical characteristics of healthy human red blood cells (RBCs) and their membrane mechanical properties. Under constant amplitude cyclic tensile deformation, RBCs progressively lose their ability to stretch with increasing fatigue cycles. Our results further indicate that loss of deformability of RBCs during cyclic deformation is much faster than that under static deformation at the same maximum load over the same accumulated loading time. Such fatigue-induced deformability loss is more pronounced at higher amplitudes of cyclic deformation. These results uniquely establish the important role of mechanical fatigue in influencing physical properties of biological cells. They further provide insights into the accumulated membrane damage during blood circulation, paving the way for further investigations of the eventual failure of RBCs causing hemolysis in various hemolytic pathologies.

Deep elastic strain engineering of bandgap through machine learning.

Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties. Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV. Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor. A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies.

Computational modeling of biomechanics and biorheology of heated red blood cells.

Because of their compromised deformability, heat denatured erythrocytes have been used as labeled probes to visualize spleen tissue or to assess the ability of the spleen to retain stiff red blood cells (RBCs) for over three decades, e.g., see Looareesuwan et al. N. Engl. J. Med. (1987). Despite their good accessibility, it is still an open question how heated RBCs compare to certain diseased RBCs in terms of their biomechanical and biorheological responses, which may undermine their effective usage and even lead to misleading experimental observations. To help answering this question, we perform a systematic computational study of the hemorheological properties of heated RBCs with several physiologically relevant static and hemodynamic settings, including optical-tweezers test, relaxation of prestretched RBCs, RBC traversal through a capillary-like channel and a spleen-like slit, and a viscometric rheology test. We show that our in silico RBC models agree well with existing experiments. Moreover, under static tests, heated RBCs exhibit deformability deterioration comparable to certain disease-impaired RBCs such as those in malaria. For RBC traversal under confinement (through microchannel or slit), heated RBCs show prolonged transit time or retention depending on the level of confinement and heating procedure, suggesting that carefully heat-treated RBCs may be useful for studying splenic- or vaso-occlusion in vascular pathologies. For the rheology test, we expand the existing bulk viscosity data of heated RBCs to a wider range of shear rates (1-1000 s-1) to represent most pathophysiological conditions in macro- or microcirculation. Although heated RBC suspension shows elevated viscosity comparable to certain diseased RBC suspensions under relatively high shear rates (100-1000 s-1), they underestimate the elevated viscosity (e.g., in sickle cell anemia) at low shear rates (<10 s-1). Our work provides mechanistic rationale for selective usage of heated RBC as a potentially useful model for studying the abnormal traversal dynamics and hemorheology in certain blood disorders.

Serum Bioavailable, Rather Than Total, 25-hydroxyvitamin D Levels Are Associated With Hepatocellular Carcinoma Survival.

BACKGROUND AND AIMS: Free and bioavailable 25-hydroxyvitamin D (25OHD) are emerging measurements of vitamin D status. It remains unclear whether circulating free or bioavailable 25OHD are relevant to hepatocellular carcinoma (HCC) prognosis. Our aim was to test the hypothesis that bioavailable 25OHD may be a better serum biomarker of vitamin D status than total 25OHD on the association with HCC survival. APPROACH AND RESULTS: We included 1,031 newly diagnosed, previously untreated patients with HCC from the Guangdong Liver Cancer Cohort enrolled between September 2013 and April 2017. Serum total 25OHD levels were measured using an electrochemiluminescence immunoassay. Serum-free 25OHD levels were measured using a two-step enzyme-linked immunosorbent assay. Bioavailable 25OHD levels were calculated from measured free 25OHD and albumin using a previously validated equation. Primary outcomes were liver cancer-specific (LCSS) and overall survival (OS). Cox proportional hazards models were performed to calculate the multivariable hazard ratios (HRs) and 95% confidence intervals (CIs). During a median follow-up of 726 days, 430 patients had deceased, including 393 deaths from HCC. In multivariable analyses, higher bioavailable 25OHD levels were significantly associated with better survival, independent of nonclinical and clinical prognostic factors including serum C-reactive protein, Barcelona Clinic Liver Cancer stage, and cancer treatment. The multivariable-adjusted HRs in the highest versus lowest quartile of bioavailable 25OHD levels were 0.69 (95% CI: 0.51, 0.93; P for trend = 0.014) for LCSS and 0.71 (95% CI: 0.53, 0.94; P for trend = 0.013) for OS. In contrast, neither total nor free 25OHD levels were associated with LCSS or OS. CONCLUSIONS: Higher bioavailable, rather than total, 25OHD levels were independently associated with improved survival in a population-based HCC cohort, suggesting a potential utility of bioavailable 25OHD in HCC prognosis.

Effects of probiotic supplementation on serum trimethylamine-N-oxide level and gut microbiota composition in young males: a double-blinded randomized controlled trial.

PURPOSE: To explore whether probiotic supplementation could attenuate serum trimethylamine-N-oxide (TMAO) level and impact the intestinal microbiome composition. DESIGN: Forty healthy males (20-25 years old) were randomized into the probiotic group (1.32 × 1011 CFU live bacteria including strains of Lactobacillus acidophilus, Lactobacillus rhamnosus GG, Bifidobacterium animalis, and Bifidobacterium longum daily) or the control group for 4 weeks. All participants underwent a phosphatidylcholine challenge test (PCCT) before and after the intervention. Serum TMAO and its precursors (TMA, choline and betaine) were measured by UPLC-MS/MS. The faecal microbiome was analyzed by 16S rRNA sequencing. RESULTS: Serum TMAO and its precursors were markedly increased after the PCCT. No statistical differences were observed in the probiotic and the control group in area under the curve (AUC) (14.79 ± 0.97 µmol/L 8 h vs. 19.17 ± 2.55 µmol/L 8 h, P = 0.106) and the pre- to post-intervention AUC alterations (∆AUC) (- 6.33 ± 2.00 µmol/L 8 h vs. - 0.73 ± 3.04 µmol/L 8 h, P = 0.131) of TMAO; however, higher proportion of participants in probiotic group showed their TMAO decrease after the intervention (78.9% vs. 45.0%, P = 0.029). The abundance of Faecalibacterium prausnitzii (P = 0.043) and Prevotella (P = 0.001) in the probiotic group was significantly increased after the intervention but without obvious differences in α- and ß-diversity. CONCLUSIONS: The current probiotic supplementation resulted in detectable change of intestinal microbiome composition but failed to attenuate the serum TMAO elevation after PCCT. CLINICALTRIALS. GOV IDENTIFIER: NCT03292978. CLINICALTRIALS.GOV WEBSITE: https://clinicaltrials.gov/ct2/show/NCT03292978 .