Speed-enhanced scattering compensation method with sub-Nyquist sampling.

A rapid feedback-based scattering compensation method is particularly important for guiding light precisely within turbid tissues, especially the dynamic tissues. However, the huge number of measurements that come from the underutilization of the signal frequency channel greatly limits the modulation speed. This paper introduces a rapid compensation method with the sub-Nyquist sampling which improves the channel utilization and the speed of wavefront shaping. The number of measurements is reduced to ∼1500 with 32 × 32 freedom, and the PBR of the focus reaches ∼200. The system performances are demonstrated by focusing the light through brain slices of different thicknesses.

Tophi and carotid atherosclerosis in gout patients: Role of insulin resistance.

BACKGROUND AND AIM: Gout and cardiovascular disease are closely related, but the mechanism linking them is still unknown. Gout may affect the insulin signaling pathway inducing insulin resistance (IR). The study aims to evaluate the association between tophi and carotid atherosclerosis, considering the potential role of IR. METHODS AND RESULTS: A total of 595 patients with gout aged 18 to 80 were enrolled in this study. Carotid intima-media thickness, plaques and tophi were evaluated by B-mode ultrasonography. IR was assessed by the HOMA index (hepatic IR) and Gutt index (peripheral IR). Multivariable logistic regression and interaction analysis were used to examine the association between tophi and IR and its impact on carotid atherosclerosis. Among these participants, the average age was 55.4 (±12.54) years, and 94.6 % were male. Tophi were associated with increased odds of carotid atherosclerosis and burden after adjustment for confounders (P < 0.05). Tophi and IR synergically interacted for inducing carotid atherosclerosis. The interaction between peripheral IR with tophi was more pronounced than hepatic IR with tophi. CONCLUSIONS: Tophi were independently associated with carotid atherosclerosis risk. IR mediated a significant amount of the effect of tophi on the development of carotid atherosclerosis. Peripheral IR probably plays a more important role than hepatic IR does.

High-axial-resolution speckle-free holographic reconstruction via cylindrical quadratic phase method and temporal focusing.

Holographic techniques enable precise laser manipulation, but suffer from two considerable limitations: speckle and deterioration of axial distribution. Here, we propose a cylindrical quadratic phase (CQP) method with temporal focusing (TF) to generate speckle-free holographic illumination with high axial resolution. TF-CQP utilizes a superposed cylindrical phase as the initial guess to iteratively optimize phase hologram, realizing speckle-free holographic reconstruction on the target focal plane and eliminating secondary focus on the defocused planes. TF-CQP further disperses defocused beams symmetrically by a blazed grating, placed conjugate to the focal plane, which enhances axial confinement. Simulation and experimental results show that TF-CQP reconstructs speckle-free illumination with arbitrary shapes and <10 µm axial resolution. Compared to TF-GS (Gerchberg-Saxton algorithm), widely used in holographic optogenetics, TF-CQP shows increased uniformity of 200% and improved modulation efficiency of 32.33% for parallel holographic illumination, as well as a 10% increment in axial resolution.

External stenting for saphenous vein grafts in coronary artery bypass grafting: A meta-analysis.

OBJECTIVES: Autologous saphenous vein grafts (SVGs) are the most commonly used bypass conduits in coronary artery bypass grafting (CABG) with multivessel coronary artery disease. Although external support devices for SVGs have shown promising outcomes, the overall efficacy and safety remains controversial. We aimed to evaluate external stenting for SVGs in CABG versus non-stented SVGs. METHODS: MEDLINE, EMBASE, Cochrane Library and clinicaltrails.gov were searched for randomized controlled trials (RCTs) to evaluate external-stented SVGs versus non-stented SVGs in CABG up to 31 August 2022. The risk ratio and mean difference with 95% confidence interval were analysed. The primary efficacy outcomes included intimal hyperplasia area and thickness. The secondary efficacy outcomes were graft failure (≥50% stenosis) and lumen diameter uniformity. RESULTS: We pooled 438 patients from three RCTs. The external stented SVGs group showed significant reductions in intimal hyperplasia area (MD: -0.78, p < 0.001, I2 = 0%) and thickness (MD: -0.06, p < 0.001, I2 = 0%) compared to the non-stented SVGs group. Meanwhile, external support devices improved lumen uniformity with Fitzgibbon I classification (risk ratio (RR):1.1595, p = 0.05, I2 = 0%). SVG failure rates were not increased in the external stented SVGs group during the short follow-up period (RR: 1.14, p = 0.38, I2 = 0%). Furthermore, the incidences of mortality and major cardiac and cerebrovascular events were consistent with previous reports. CONCLUSIONS: External support devices for SVGs significantly reduced the intimal hyperplasia area and thickness, and improved the lumen uniformity, assessed with the Fitzgibbon I classification. Meanwhile, they did not increase the overall SVG failure rate.

Speckle suppression in arbitrary parallel holographic illumination by the spatial frequency regaining method.

This Letter proposes a spatial frequency regaining method for parallel holographic illumination (SFR-PHI) to suppress speckle noise in phase-only computer-generated holography (CGH). Based on the accurate calculation of the beam bandwidth, this method uses the bandwidth-limited quadratic initial phase and weighted constraint iteration to generate the optimized phase hologram, which can provide the accurate spatial frequency of multiple illumination patterns. The results show that SFR-PHI performs superiorly in speckle suppression for generating dozens of illumination patterns in parallel and with arbitrary shapes and numbers. Compared with other speckle-suppression methods, it exhibits significant advantages in terms of accuracy and modulation efficiency.

Flexible waveguide integrated thermo-optic switch based on TiO2 platform.

Mechanically flexible photonic devices are critical components of novel bio-integrated optoelectronic and high-end wearable systems, in which thermo-optic switches (TOSs) as optical signal control devices are crucial. In this paper, flexible titanium oxide (TiO2) TOSs based on a Mach-Zehnder interferometer (MZI) structure were demonstrated around 1310 nm for, it is believed, the first time. The insertion loss of flexible passive TiO2 2 × 2 multi-mode interferometers (MMIs) is -3.1 dB per MMI. The demonstrated flexible TOS achieves power consumption (Pπ) of 0.83 mW, compared with its rigid counterpart, for which Pπ is decreased by a factor of 18. The proposed device could withstand 100 consecutive bending operations without noticeable degradation in TOS performance, indicating excellent mechanical stability. These results provide a new perspective for designing and fabricating flexible TOSs for flexible optoelectronic systems in future emerging applications.

Engineered exosome-mediated messenger RNA and single-chain variable fragment delivery for human chimeric antigen receptor T-cell engineering.

BACKGROUND AIMS: Most current chimeric antigen receptor (CAR) T cells are generated by viral transduction, which induces persistent expression of CARs and may cause serious undesirable effects. Messenger RNA (mRNA)-based approaches in manufacturing CAR T cells are being developed to overcome these challenges. However, the most common method of delivering mRNA to T cells is electroporation, which can be toxic to cells. METHODS: The authors designed and engineered an exosome delivery platform using the bacteriophage MS2 system in combination with the highly expressed protein lysosome-associated membrane protein 2 isoform B on exosomes. RESULTS: The authors' delivery platform achieved specific loading and delivery of mRNA into target cells and achieved expression of specific proteins, and anti-CD3/CD28 single-chain variable fragments (scFvs) expressed outside the exosomal membrane effectively activated primary T cells in a similar way to commercial magnetic beads. CONCLUSIONS: The delivery of CAR mRNA and anti-CD3/CD28 scFvs via designed exosomes can be used for ex vivo production of CAR T cells with cancer cell killing capacity. The authors' results indicate the potential applications of the engineered exosome delivery platform for direct conversion of primary T cells to CAR T cells while providing a novel strategy for producing CAR T cells in vivo.

Association between serum free fatty acid levels and tophus in patients with gout: a cross-sectional study.

OBJECTIVES: To explore the relationship between serum free fatty acid (FFA) and tophus in gout patients, and to investigate whether FFA increases the risk of tophus deposition by lowering urine pH. METHODS: A total of 595 patients with gout aged 18 to 80 were enrolled between June 2018 and August 2021. The subjects were divided into four groups according to FFA. Logistic regression was used to analyse the association between serum FFA and tophus. Receiver operating curves (ROC) were plotted to explore the predictive value of FFA on the occurrence of tophus. RESULTS: Accompanying the increase of FFA levels, the prevalence of tophus in groups Q3 and Q4 was significantly higher than in groups Q1 and Q2 (33.6%, 36.5% vs. 6.3%, 19.6%, p<0.001). According to the Spearman correlation, serum FFA levels were positively correlated with tophus while negatively with urine pH (p<0.001). FFA had a significant interaction with urine pH on tophus risk. Multivariate logistic regression showed that participants in Q2-Q4 had a higher OR of tophus than those in Q1 (OR were 2.770, 5.878 and 7.958 in Q2-Q4, respectively). ROC showed the best cut-off value of serum FFA level in predicting the onset of tophus was 0.46 mmol/L. Serum FFA had a great discriminant ability to predict tophus. CONCLUSIONS: High FFA levels are independently associated with tophus risk and FFA may promote tophi deposition by lowering urine pH. Serum FFA levels have a great screening value to identify tophus.

Ultrafast 3D histological imaging based on a minutes-time scale tissue clearing and multidirectional selective plane illumination microscopy.

Conventional histopathological examinations are time-consuming and labor-intensive, and are insufficient to depict 3D pathological features intuitively. Here we report an ultrafast 3D histological imaging scheme based on optimized selective plane illumination microscopy (mSPIM), a minutes-time scale clearing method (FOCM), and a deep learning-based image enhancement algorithm (SRACNet) to realize histological preparation and imaging of clinical tissues. Our scheme enables 1-minute clearing and fast imaging (up to 900 mm2/min) of 200 µm-thick mouse kidney slices at micron-level resolution. With hematoxylin and eosin analog, we demonstrated the detailed 3D morphological connections between glomeruli and the surrounding tubules, which is difficult to identify in conventional 2D histology. Further, by the preliminary verification on human kidney tissues, this study will provide new, to the best of our knowledge, feasible histological solutions and inspirations in future 3D digital pathology.

Genetically encoded sensors enable micro- and nano-scopic decoding of transmission in healthy and diseased brains.

Neural communication orchestrates a variety of behaviors, yet despite impressive effort, delineating transmission properties of neuromodulatory communication remains a daunting task due to limitations of available monitoring tools. Recently developed genetically encoded neurotransmitter sensors, when combined with superresolution and deconvolution microscopic techniques, enable the first micro- and nano-scopic visualization of neuromodulatory transmission. Here we introduce this image analysis method by presenting its biophysical foundation, practical solutions, biological validation, and broad applicability. The presentation illustrates how the method resolves fundamental synaptic properties of neuromodulatory transmission, and the new data unveil unexpected fine control and precision of rodent and human neuromodulation. The findings raise the prospect of rapid advances in the understanding of neuromodulatory transmission essential for resolving the physiology or pathogenesis of various behaviors and diseases.

Organizational principles of amygdalar input-output neuronal circuits.

The amygdala, one of the most studied brain structures, integrates brain-wide heterogeneous inputs and governs multidimensional outputs to control diverse behaviors central to survival, yet how amygdalar input-output neuronal circuits are organized remains unclear. Using a simplified cell-type- and projection-specific retrograde transsynaptic tracing technique, we scrutinized brain-wide afferent inputs of four major output neuronal groups in the amygdalar basolateral complex (BLA) that project to the bed nucleus of the stria terminals (BNST), ventral hippocampus (vHPC), medial prefrontal cortex (mPFC) and nucleus accumbens (NAc), respectively. Brain-wide input-output quantitative analysis unveils that BLA efferent neurons receive a diverse array of afferents with varied input weights and predominant contextual representation. Notably, the afferents received by BNST-, vHPC-, mPFC- and NAc-projecting BLA neurons exhibit virtually identical origins and input weights. These results indicate that the organization of amygdalar BLA input-output neuronal circuits follows the input-dependent and output-independent principles, ideal for integrating brain-wide diverse afferent stimuli to control parallel efferent actions. The data provide the objective basis for improving the virtual reality exposure therapy for anxiety disorders and validate the simplified cell-type- and projection-specific retrograde transsynaptic tracing method.

A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.

Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.

Ultrafast optical clearing method for three-dimensional imaging with cellular resolution.

Optical clearing is a versatile approach to improve imaging quality and depth of optical microscopy by reducing scattered light. However, conventional optical clearing methods are restricted in the efficiency-first applications due to unsatisfied time consumption, irreversible tissue deformation, and fluorescence quenching. Here, we developed an ultrafast optical clearing method (FOCM) with simple protocols and common reagents to overcome these limitations. The results show that FOCM can rapidly clarify 300-µm-thick brain slices within 2 min. Besides, the tissue linear expansion can be well controlled by only a 2.12% increase, meanwhile the fluorescence signals of GFP can be preserved up to 86% even after 11 d. By using FOCM, we successfully built the detailed 3D nerve cells model and showed the connection between neuron, astrocyte, and blood vessel. When applied to 3D imaging analysis, we found that the foot shock and morphine stimulation induced distinct c-fos pattern in the paraventricular nucleus of the hypothalamus (PVH). Therefore, FOCM has the potential to be a widely used sample mounting media for biological optical imaging.

Adaptive optics for structured illumination microscopy based on deep learning.

Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from optical aberrations, leading to a serious deterioration in resolution. Therefore, it is necessary to reconstruct structured illumination patterns with high quality and efficiency in deep tissue imaging. Here we demonstrate an adaptive optics (AO) correction method based on deep learning in wide-field SIM imaging system. The mapping between the coefficients of the first 15 Zernike modes and their corresponding distorted patterns is established to train the convolution neural network (CNN). The results show that the optimized CNN can predict the aberration phase within ~10.1 ms with a personal computer. The correlation index between the aberration phases and their corresponding predicted aberration phase is up to 0.9986. This method is highly robust and effective for patterns with various spatial densities and illumination conditions and able to effectively correct the imaging distortion caused by optical aberration in SIM system.

Improved spatial resolution using focal modulation microscopy with a Tai Chi aperture.

Focal modulation microscopy (FMM) has gained significant interest in biological imaging. However, the spatial resolution and penetration depth limit the imaging quality of FMM due to the strong scattering background. Here, we introduce FMM with a Tai Chi aperture (TCFMM) based on diffraction theory to improve the spatial resolution. The results show that the transverse resolution is improved by 61.60% and 41.37% in two orthogonal directions, and the axial resolution is improved by 29.67%, compared with confocal microscopy (CM). The signal background ratio (SBR) of TCFMM is increased by 23.26% compared with CM and remains nearly the same compared with FMM using D-shape apertures (DFMM). These improvements in spatial resolution and SBR indicate that TCFMM has potential in deep tissue imaging.

Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration.

In this Letter, we propose a deep learning method with prior knowledge of potential aberration to enhance the fluorescence microscopy without additional hardware. The proposed method could effectively reduce noise and improve the peak signal-to-noise ratio of the acquired images at high speed. The enhancement performance and generalization of this method is demonstrated on three commercial fluorescence microscopes. This work provides a computational alternative to overcome the degradation induced by the biological specimen, and it has the potential to be further applied in biological applications.

Trends and influencing factors of plasma folate levels in Chinese women at mid-pregnancy, late pregnancy and lactation periods.

Folate status for women during early pregnancy has been investigated, but data for women during mid-pregnancy, late pregnancy or lactation are sparse or lacking. Between May and July 2014, we conducted a cross-sectional study in 1211 pregnant and lactating women from three representative regions in China. Approximately 135 women were enrolled in each stratum by physiological periods (mid-pregnancy, late pregnancy or lactation) and regions (south, central or north). Plasma folate concentrations were measured by microbiological assay. The adjusted medians of folate concentration decreased from 28·8 (interquartile range (IQR) 19·9, 38·2) nmol/l in mid-pregnancy to 18·6 (IQR 13·2, 26·4) nmol/l in late pregnancy, and to 17·0 (IQR 12·3, 22·5) nmol/l in lactation (Pfor trend < 0·001). Overall, lower folate concentrations were more likely to be observed in women residing in the northern region, with younger age, higher pre-pregnancy BMI, lower education or multiparity, and in lactating women who had undergone a Caesarean delivery or who were breastfeeding exclusively. In total, 380 (31·4 %) women had a suboptimal folate status (folate concentration <13·5 nmol/l). Women in late pregnancy and lactating, residing in the northern region, having multiparity and low education level had a higher risk of suboptimal folate status, while those with older age had a lower risk. In conclusion, maternal plasma folate concentrations decreased as pregnancy progressed, and were influenced by geographic region and maternal socio-demographic characteristics. Future studies are warranted to assess the necessity of folic acid supplementation during later pregnancy and lactation especially for women at a higher risk of folate depletion.

Epithelial cell -derived microvesicles: A safe delivery platform of CRISPR/Cas9 conferring synergistic anti-tumor effect with sorafenib.

Safe and efficient intracellular delivery of CRISPR/Cas9 is a key step for effective therapeutic genome editing in a wide range of diseases. This remains challenging due to multiple drawbacks of the currently available vehicles. Here we report that epithelial cell -derived microvesicles (MVs) function as safe and natural carriers for efficient delivery of CRISPR/Cas9 to treat cancer. In our study, compared to epithelial cell -derived MVs, cancer -derived MVs were quickly absorbed intracellularly by recipient cancer cells in vitro and showed selective accumulation in tumors of HepG2 xenografts in vivo, due to their cancer cell tropism dependent targeting. Surprisingly, synergistic anti-tumor effect of sgIQ 1.1 loaded Cas9MVs/HEK293 + sorafenib was better than sgIQ 1.1 + Cas9MVs/HepG2 + sorafenib in vitro. In addition, qPCR results showed that miR-21 and miR-181a expression were upregulated in HepG2 cells treated with cancer cell -derived MVs that might support the cancer progression. Further, treatment of HepG2 xenografts with sgIQ 1.1 loaded Cas9MVs/HEK293 showed enhanced anti-cancer effect than sgIQ 1.1 + Cas9MVs/HepG2. Therefore, we conclude that normal cells -derived MVs can act as better and safe natural delivery systems for cancer therapeutics in the future.

Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection: erratum.

An erratum is presented to correct the sizes of feature maps of Fig. 1(a) in [Opt. Express27, 33504 (2019)10.1364/OE.27.033504].

Wavefront reconstruction based on deep transfer learning for microscopy.

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.