Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

DPP: deep phase prior for parallel imaging with wave encoding.

Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.

Novel method for identifying the heaviest QED atom.

QED atoms are composed of unstructured and point-like lepton pairs bound together by the electromagnetic force. The smallest and heaviest QED atom is formed by a τ+τ- pair. Currently, the only known atoms of this type are the e+e- and µ+e- atoms, which were discovered 64 years ago and remain the sole examples found thus far. We demonstrate that the Jτ (τ+τ- atom with JPC=1--) atom signal can be observed with a significance larger than 5σ including both statistical and systematic uncertainties, via. the process e+e-→X+Y-Ɇ (X,Y=e,µ,π,K, or ρ, and Ɇ is the missing energy due to unobserved neutrinos) with 1.5ab-1 data taken around the τ pair production threshold. The τ lepton mass can be measured with a precision of 1 keV with the same data sample. This is within one year's running time of the proposed super tau-charm facility in China or super charm-tau factory in Russia.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.

Background: The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods: In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results: Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions: These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.

DNase I targeted degradation of neutrophil extracellular traps to reduce the damage on IgAV rat.

BACKGROUND: In the past two years, studies have found a significant increase in neutrophil extracellular traps (NETs) in patients with IgA vasculitis (IgAV), which is correlated with the severity of the disease. NETs have been reported as an intervention target in inflammatory and autoimmune diseases. This study aimed to investigate the effect of targeted degradation of NETs using DNase I in IgAV rat model. METHODS: Twenty-four Sprague-Dawley rats were randomly divided into three groups: the IgAV model group, the DNase I intervention group and the normal control group, with an average of 8 rats in each group. The model group was established by using Indian ink, ovalbumin, and Freund's complete adjuvant. In the intervention group, DNase I was injected through tail vein 3 days before the end of established model. The circulating cell free-DNA (cf-DNA) and myeloperoxidase-DNA (MPO-DNA) were analyzed. The presence of NETs in the kidney, gastric antrum and descending duodenum were detected using multiple fluorescences immunohistochemistry and Western blots. Morphological changes of the tissues were observed. RESULTS: After the intervention of DNase I, there was a significant reduction in cf-DNA and MPO-DNA levels in the intervention group compared to the IgAV model group (all P<0.001). The presence of NETs in renal, gastric, and duodenal tissues of the intervention group exhibited a significant decrease compared to the IgAV model group (P < 0.01). Moreover, the intervention group demonstrated significantly lower levels of renal MPO and citrullinated histone H3 (citH3) protein expression when compared to the IgAV model group (all P < 0.05). The HE staining results of intervention group demonstrated a significant reduction in congestion within glomerular and interstitial capillaries. Moreover, there was a notable improvement in gastric and intestinal mucosa necrosis, congestion and bleeding. Additionally, there was a substantial decrease in inflammatory cells infiltration. CONCLUSION: The degradation of NETs can be targeted by DNase I to mitigate tissue damage in IgAV rat models. Targeted regulation of NETs holds potential as a therapeutic approach for IgAV.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

A meta-analysis of the association between mindfulness and motivation.

Introduction: Mindfulness reflects attention to the present moment in a non-judgmental way and has been linked to individual autonomy and motivation, but conclusions are inconsistent. The purpose of this review was to summarize previous studies to explore the relationship between mindfulness and motivation and its intervention effects. Methods: Literature searches were conducted in five electronic databases. Both correlational studies assessing the association between motivation and mindfulness and experimental studies to verify the effect of intervention were included. Results: Six papers with seven intervention studies and twenty-three papers with twenty-seven correlational studies met the inclusion criteria. Meta-analysis showed that mindfulness was positively correlated with intrinsic motivation (r = 0.28, p < 0.0001) and total motivation (r = 0.37, p < 0.0001) but had no significant correlation with extrinsic motivation (r = 0.01, p = 0.93) or amotivation (r = -0.17, p = 0.14). Effect-size estimates suggested that mindfulness intervention was beneficial to motivation promotion, but the effect was at a low level (g = 0.12). Conclusion: We found consistent support for mindfulness practice relating to motivation promotion, especially on intrinsic motivation development. However, there was still a portion of heterogeneity that could not be explained and needed to be identified in future studies.

K-UNN: k-space interpolation with untrained neural network.

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module. This implies that the unrolled network output may not align with the regularization models. Furthermore, there are few established theories that guarantee global convergence and robustness (regularity) of unrolled networks under practical assumptions. To address this gap, we propose a safeguarded methodology for network unrolling. Specifically, for parallel MR imaging, we unroll a zeroth-order algorithm, where the network module serves as a regularizer itself, allowing the network output to be covered by a regularization model. Additionally, inspired by deep equilibrium models, we conduct the unrolled network before backpropagation to converge to a fixed point and then demonstrate that it can tightly approximate the actual MR image. We also prove that the proposed network is robust against noisy interferences if the measurement data contain noise. Finally, numerical experiments indicate that the proposed network consistently outperforms state-of-the-art MRI reconstruction methods, including traditional regularization and unrolled deep learning techniques.

Neutrophil extracellular traps involved in the pathogenesis of IgA vasculitis: Confirmed in two IgAV rat models.

BACKGROUND: Neutrophil extracellular traps (NETs) have been found to play a role in the development of autoimmune diseases. In the past two years, studies have demonstrated a significantly increase of NETs in skin tissues during the early stages of IgAV, indicating their involvement in disease activity among children with IgAV. However, the presence of NETs in IgAV animal models has not yet been reported. The objective of this study is to investigate whether NETs are involved in the pathogenesis of IgA vasculitis (IgAV) rats. METHODS: Twenty-four SD rats were randomly divided into three groups: the ovalbumin group, the gliadin group, and the control group. The IgAV rat models were established administering Indian ink with ovalbumin (ovalbumin group) or gliadin (gliadin group) with Freund's complete adjuvant. The cell-free DNA (cf-DNA) was quantified by using dsDNA quantification kit, while the levels of Immunoglobulins, complement C3 and myeloperoxidase-DNA (MPO-DNA) in serum were tested using enzyme linked immunosorbent assay (ELISA). The IgA, complement C3 and NETs in tissues were detected through multiple immunofluorescences. RESULTS: Both the ovalbumin group and gliadin group showed IgA and C3 deposition in various tissues, including the glomerular mesangial region, skin, and digestive tract, while the control group showed no such deposition. The levels of circulatory cf-DNA and MPO-DNA, which are components of NETs, were significantly elevated in both ovalbumin and gliadin groups compared with the control group. Furthermore, the presence of NETs were found in gastrointestinal and renal tissues of the ovalbumin and gliadin groups, but not in the control group. CONCLUSIONS: IgAV model rat can be established through the combination of ovalbumin and gliadin with Indian ink and Freund's complete adjuvant. This study provides the first confirmation that NETs are involved in the pathogenesis of IgAV rat.

Hypobaric Hypoxia Aggravates Renal Injury by Inducing the Formation of Neutrophil Extracellular Traps through the NF-κB Signaling Pathway.

OBJECTIVE: The hypersensitivity of the kidney makes it susceptible to hypoxia injury. The involvement of neutrophil extracellular traps (NETs) in renal injury resulting from hypobaric hypoxia (HH) has not been reported. In this study, we aimed to investigate the expression of NETs in renal injury induced by HH and the possible underlying mechanism. METHODS: A total of 24 SD male rats were divided into three groups (n=8 each): normal control group, hypoxia group and hypoxia+pyrrolidine dithiocarbamate (PDTC) group. Rats in hypoxia group and hypoxia+PDTC group were placed in animal chambers with HH which was caused by simulating the altitude at 7000 meters (oxygen partial pressure about 6.9 kPa) for 7 days. PDTC was administered at a dose of 100 mg/kg intraperitoneally once daily for 7 days. Pathological changes of the rat renal tissues were observed under a light microscope; the levels of serum creatinine (SCr), blood urea nitrogen (BUN), cell-free DNA (cf-DNA) and reactive oxygen species (ROS) were measured; the expression levels of myeloperoxidase (MPO), citrullinated histone H3 (cit-H3), B-cell lymphoma 2 (Bcl-2), Bax, nuclear factor kappa B (NF-κB) p65 and phospho-NF-κB p65 (p-NF-κB p65) in rat renal tissues were detected by qRT-qPCR and Western blotting; the localization of NF-κB p65 expression in rat renal tissues was observed by immunofluorescence staining and the expression changes of NETs in rat renal tissues were detected by multiplex fluorescence immunohistochemical staining. RESULTS: After hypoxia, the expression of NF-κB protein in renal tissues was significantly increased, the levels of SCr, BUN, cf-DNA and ROS in serum were significantly increased, the formation of NETs in renal tissues was significantly increased, and a large number of tubular dilatation and lymphocyte infiltration were observed in renal tissues. When PDTC was used to inhibit NF-κB activation, NETs formation in renal tissue was significantly decreased, the expression level of Bcl-2 in renal tissues was significantly increased, the expression level of Bax was significantly decreased, and renal injury was significantly alleviated. CONCLUSION: HH induces the formation of NETs through the NF-κB signaling pathway, and it promotes apoptosis and aggravates renal injury by decreasing Bcl-2 and increasing Bax expression.

Next-generation viral nanoparticles for targeted delivery of therapeutics: Fundamentals, methods, biomedical applications, and challenges.

INTRODUCTION: Viral nanoparticles (VNPs) are virus-based nanocarriers that have been studied extensively and intensively for biomedical applications. However, their clinical translation is relatively low compared to the predominating lipid-based nanoparticles. Therefore, this article describes the fundamentals, challenges, and solutions of the VNP-based platform, which will leverage the development of next-generation VNPs. AREAS COVERED: Different types of VNPs and their biomedical applications are reviewed comprehensively. Strategies and approaches for cargo loading and targeted delivery of VNPs are examined thoroughly. The latest developments in controlled release of cargoes from VNPs and their mechanisms are highlighted too. The challenges faced by VNPs in biomedical applications are identified, and solutions are provided to overcome them. EXPERT OPINION: In the development of next-generation VNPs for gene therapy, bioimaging and therapeutic deliveries, focus must be given to reduce their immunogenicity, and increase their stability in the circulatory system. Modular virus-like particles (VLPs) which are produced separately from their cargoes or ligands before all the components are coupled can speed up clinical trials and commercialization. In addition, removal of contaminants from VNPs, cargo delivery across the blood brain barrier (BBB), and targeting of VNPs to organelles intracellularly are challenges that will preoccupy researchers in this decade.

[Effects of Long-term Sod Cultivation on Chinese Hickory Plantation Soil Fungal Community and Enzyme Activities].

A long-term field experiment was conducted at a Chinese hickory (Carya cathayensis) plantation from 2011 to 2021, with the purpose of researching the effects of long-term sod cultivation on hickory plantation soil fungal communities and enzyme activities and providing experience for ecological management in other plantations. Sod cultivation included oilseed rape (Brassica chinensis, BR), Chinese milk vetch (Astragalus sinicus, AS), and oilseed rape+Chinese milk vetch (BA), and clear tillage (CT) served as a contrast. The soil fertility, fungal community composition and diversity, and soil enzyme activities were determined. The results showed that:① long-term sod cultivation significantly increased soil nutrient contents and availability, and pH increased variably from different sod cultivation treatments (P<0.05). ②The soil fungal community composition was changed by long-term sod cultivation. The relative abundance of Ascomycota, which utilized the readily decomposed organic matter, was increased, whereas the relative abundance of Basidiomycota, which degraded stubborn organic matter, decreased. Long-term sod cultivation shifted the soil dominant genera, as BR and BA increased the relative abundance of somemycorrhizal fungi that could form mutually beneficial structures with dominant plant genera after sod cultivation,whereas AS increased the relative abundance of saprophytic fungi that could decompose the remains of dead plants and animals. The soil fertility factors including pH, available nitrogen, microbial biomass nitrogen, and water-soluble organic carbon were revealed to have a significant influence on the soil fungal composition (P<0.05). ③ Moreover, long-term sod cultivation stimulated the activities of soil enzymes involved in the carbon and nitrogen cycle. Apart from BA, sod cultivation treatments decreased the activities of alkaline phosphatase, which was involved in the soil P turnover. The correlation analysis demonstrated that the correlations between activities of enzymes decomposing carbon and nitrogen and soil fertility were significant (P<0.05 or P<0.01). The activities of phosphatase were positively correlated with soil microbial biomass carbon and nitrogen. Long-term sod cultivation could improve soil nutrient content and availability, optimized soil fungal community structure, and promoted soil nutrient turnover enzyme activities.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

BACKGROUND: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data. PURPOSE: To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free. METHODS: Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively. RESULTS: In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. CONCLUSIONS: The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

Comparisons of facial emotion recognition in different social contexts among patients with schizophrenia, major depressive disorder and bipolar disorder.

BACKGROUND: Previous studies have found that patients with schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) all have facial emotion recognition deficits, but the differences and similarities of these deficits in the three groups of patients under different social interaction situations are not clear. The present study aims to compare the ability of facial emotion recognition in three different conversation situations from a cross-diagnostic perspective. METHODS: Thirty-three participants with SCZ, 35 participants with MDD, and 30 participants with BD were recruited, along with 31 healthy controls. A computer-based task was given to assess the ability of Facial Emotion Categorization (FEC) under three different conversational situations (praise, blame, and inquiry). RESULTS: In the "praise" situation, patients with SCZ, MDD and BD were all slower to recognize anger emotion than the healthy controls. In all three clinical groups, patients with SCZ recognized angry faces faster than those with MDD and BD on a continuum from happy faces to angry faces in the "inquiry" situation, while no significant difference was found in the latter two groups. In addition, no significant defect was found in the percentage and threshold of angry face recognition in all three patient groups. CONCLUSIONS: Our findings indicate that patients with SCZ, MDD, and BD share both common and distinct deficits in facial emotion recognition during social interactions, which may be beneficial for early screening and precise intervention for these mental disorders.

Genomic analysis and biological characterization of a novel Schitoviridae phage infecting Vibrio alginolyticus.

Vibrio alginolyticus is a Gram-negative bacterium commonly associated with mackerel poisoning. A bacteriophage that specifically targets and lyses this bacterium could be employed as a biocontrol agent for treating the bacterial infection or improving the shelf-life of mackerel products. However, only a few well-characterized V. alginolyticus phages have been reported in the literature. In this study, a novel lytic phage, named ΦImVa-1, specifically infecting V. alginolyticus strain ATCC 17749, was isolated from Indian mackerel. The phage has a short latent period of 15 min and a burst size of approximately 66 particles per infected bacterium. ΦImVa-1 remained stable for 2 h at a wide temperature (27-75 °C) and within a pH range of 5 to 10. Transmission electron microscopy revealed that ΦImVa-1 has an icosahedral head of approximately 60 nm in diameter with a short tail, resembling those in the Schitoviridae family. High throughput sequencing and bioinformatics analysis elucidated that ΦImVa-1 has a linear dsDNA genome of 77,479 base pairs (bp), with a G + C content of ~ 38.72% and 110 predicted gene coding regions (106 open reading frames and four tRNAs). The genome contains an extremely large virion-associated RNA polymerase gene and two smaller non-virion-associated RNA polymerase genes, which are hallmarks of schitoviruses. No antibiotic genes were found in the ΦImVa-1 genome. This is the first paper describing the biological properties, morphology, and the complete genome of a V. alginolyticus-infecting schitovirus. When raw mackerel fish flesh slices were treated with ΦImVa-1, the pathogen loads reduced significantly, demonstrating the potential of the phage as a biocontrol agent for V. alginolyticus strain ATCC 17749 in the food. KEY POINTS: • A novel schitovirus infecting Vibrio alginolyticus ATCC 17749 was isolated from Indian mackerel. • The complete genome of the phage was determined, analyzed, and compared with other phages. • The phage is heat stable making it a potential biocontrol agent in extreme environments.

Study on the time-point distribution characteristics of the occurrence of laryngopharyngeal reflux / 中华耳鼻咽喉头颈外科杂志

Objective: To investigate the characteristics of the time-point distribution of the occurrence of laryngopharyngeal reflux (LPR) by 24-hour multichannel intraluminal impedance-pH monitoring (24 h MII-pH) and to provide guidance for the development of individualized anti-reflux strategies for LPR patients. Methods: We conducted a retrospective analysis of 24 h MII-pH data from 408 patients [339 males and 69 females, aged 23-84 (55.08±11.08) years] attending the Department of Otorhinolaryngology Head and Neck Surgery at the Sixth Medical Center of the PLA General Hospital from January 2013 to March 2020. The number of gas acid/weak-acid reflux, mixed gas-liquid acid/weak-acid reflux, liquid acid/weak-acid reflux and alkaline reflux events at different time points were recorded and statistically analyzed through SPSS 26.0 software. Results: A total of 408 patients were included. Based on the 24 h MII-pH, the total positive rate of LPR was 77.45% (316/408). The type of positive gaseous weak-acid reflux was significantly higher than the remaining types of LPR (χ2=297.12,P<0.001). Except the gaseous weak-acid reflux, the occurrence of the remaining types of LPR showed a tendency to increase after meals, especially after dinner. Liquid acid reflux events occurred mainly between after dinner and the following morning, and 47.11% (57/121) of them occurred within 3 h after dinner. There was a significant positive association between Reflux Symptom Index scores and gaseous weak-acid reflux(r=0.127,P<0.01), liquid acid reflux(r=0.205,P<0.01) and liquid weak-acid reflux(r=0.103,P<0.05)events. Conclusions: With the exception of gaseous weak-acid reflux events, the occurrence of the remaining types of LPR events has a tendency to increase after meals, especially after dinner. Gaseous weak-acid reflux events accounts for the largest proportion of all types of LPR events, but the pathogenic mechanisms of gaseous weak-acid reflux are needed to further investigate.

SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging.

Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.

BACKGROUND: With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis. METHODS: Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. RESULTS: A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets. CONCLUSION: We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.