Colonoscopy-assisted removal of an impaction foreign body at the rectosigmoid junction: A case report.

BACKGROUND: When an anorectal foreign body is found, its composition and shape should be evaluated, and a timely and effective treatment plan should be developed based on the patient's symptoms to avoid serious complications such as intestinal perforation caused by displacement of the foreign body. CASE SUMMARY: A 54-year-old male was admitted to our outpatient clinic on June 3, 2023, due to a rectal foreign body that had been embedded for more than 24 h. The patient reported using a glass electrode tube to assist in the recovery of prolapsed hemorrhoids, however, the electrode tube was inadvertently inserted into the anus and could not be removed by the patient. During hospitalization, the patient underwent surgery, and the foreign body was dragged into the rectum with the aid of colonoscopy. The anus was dilated with a comb-type pulling hook and an anal fistula pulling hook to widen the anus and remove the foreign body, and the local anal symptoms were then relieved with topical drugs. The patient was allowed to eat and drink, and an entire abdominal Computed tomography (CT) and colonoscopy were reviewed 3 d after surgery. CT revealed no foreign body residue and colonoscopy showed no metal or other residues in the colon and rectum, and no apparent intestinal tract damage. CONCLUSION: The timeliness and rationality of the surgical and therapeutic options for this patient were based on a literature review of the clinical signs and conceivable conditions in such cases. The type, material and the potential risks of rectal foreign bodies should be considered.

Characterization of the powdery mildew resistance locus in wheat breeding line Jimai 809 and its breeding application.

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a severe disease that affects the yield and quality of wheat. Popularization of resistant cultivars in production is the preferred strategy to control this disease. In the present study, the Chinese wheat breeding line Jimai 809 showed excellent agronomic performance and high resistance to powdery mildew at the whole growth stage. To dissect the genetic basis for this resistance, Jimai 809 was crossed with the susceptible wheat cultivar Junda 159 to produce segregation populations. Genetic analysis showed that a single dominant gene, temporarily designated PmJM809, conferred the resistance to different Bgt isolates. PmJM809 was then mapped on the chromosome arm 2BL and flanked by the markers CISSR02g-1 and CIT02g-13 with genetic distances 0.4 and 0.8 cM, respectively, corresponding to a physical interval of 704.12-708.24 Mb. PmJM809 differed from the reported Pm genes on chromosome arm 2BL in origin, resistance spectrum, physical position and/or genetic diversity of the mapping interval, also suggesting PmJM809 was located on a complex interval with multiple resistance genes. To analyze and screen the candidate gene(s) of PmJM809, six genes related to disease resistance in the candidate interval were evaluated their expression patterns using an additional set of wheat samples and time-course analysis post-inoculation of the Bgt isolate E09. As a result, four genes were speculated as the key candidate or regulatory genes. Considering its comprehensive agronomic traits and resistance findings, PmJM809 was expected to be a valuable gene resource in wheat disease resistance breeding. To efficiently transfer PmJM809 into different genetic backgrounds, 13 of 19 closely linked markers were confirmed to be suitable for marker-assisted selection. Using these markers, a series of wheat breeding lines with harmonious disease resistance and agronomic performance were selected from the crosses of Jimai 809 and several susceptible cultivars. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01467-8.

Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: To assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). METHODS: In this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. CONCLUSIONS: Generating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.

Evaluation and identification of powdery mildew-resistant genes in 137 wheat relatives.

Powdery mildew is one of the most severe diseases affecting wheat yield and quality and is caused by Blumeria graminis f. sp. tritici (Bgt). Host resistance is the preferred strategy to prevent this disease. However, the narrow genetic basis of common wheat has increased the demand for diversified germplasm resources against powdery mildew. Wheat relatives, especially the secondary gene pool of common wheat, are important gene donors in the genetic improvement of common wheat because of its abundant genetic variation and close kinship with wheat. In this study, a series of 137 wheat relatives, including 53 Triticum monococcum L. (2n = 2x = 14, AA), 6 T. urartu Thumanjan ex Gandilyan (2n = 2x = 14, AA), 9 T. timopheevii Zhuk. (2n = 4x = 28, AAGG), 66 T. aestivum subsp. spelta (2n = 6x = 42, AABBDD), and 3 Aegilops speltoides (2n = 2x = 14, SS) were systematically evaluated for their powdery mildew resistance and composition of Pm genes. Out of 137 (60.58%) accessions, 83 were resistant to Bgt isolate E09 at the seedling stage, and 116 of 137 (84.67%) wheat relatives were resistant to the mixture of Bgt isolates at the adult stage. This indicates that these accessions show a high level of resistance to powdery mildew. Some 31 markers for 23 known Pm genes were used to test these 137 accessions, and, in the results, only Pm2, Pm4, Pm6, Pm58, and Pm68 were detected. Among them, three Pm4 alleles (Pm4a, Pm4b, and Pm4f) were identified in 4 T. subsp. spelta accessions. q-RT PCR further confirmed that Pm4 alleles played a role in disease resistance in these four accessions. The phylogenetic tree showed that the kinship of Pm4 was close to Pm24 and Sr62. This study not only provides reference information and valuable germplasm resources for breeding new wheat varieties with disease resistance but also lays a foundation for enriching the genetic basis of wheat resistance to powdery mildew.

Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation and Computer-aided Diagnosis.

Breast cancer is becoming a significant global health challenge, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods from multi-parametric MRI still have limitations in exploring inter-modality information and focusing task-informative modality/modalities. To address these shortcomings, we propose a Modality-Specific Information Disentanglement (MoSID) framework to extract both inter- and intra-modality attention maps as prior knowledge for guiding tumor segmentation. Specifically, by disentangling modality-specific information, the MoSID framework provides complementary clues for the segmentation task, by generating modality-specific attention maps to guide modality selection and inter-modality evaluation. Our experiments on two 3D breast datasets and one 2D prostate dataset demonstrate that the MoSID framework outperforms other state-of-the-art multi-modality segmentation methods, even in the cases of missing modalities. Based on the segmented lesions, we further train a classifier to predict the patients' response to radiotherapy. The prediction accuracy is comparable to the case of using manually-segmented tumors for treatment outcome prediction, indicating the robustness and effectiveness of the proposed segmentation method. The code is available at https://github.com/Qianqian-Chen/MoSID.

Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy.

Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.

S-Wave Accelerates Optimization-based Photoacoustic Image Reconstruction in vivo.

OBJECTIVE: Photoacoustic imaging has undergone rapid development in recent years. To simulate photoacoustic imaging on a computer, the most popular MATLAB toolbox currently used for the forward projection process is k-Wave. However, k-Wave suffers from significant computation time. Here we propose a straightforward simulation approach based on superposed Wave (s-Wave) to accelerate photoacoustic simulation. METHODS: In this study, we consider the initial pressure distribution as a collection of individual pixels. By obtaining standard sensor data from a single pixel beforehand, we can easily manipulate the phase and amplitude of the sensor data for specific pixels using loop and multiplication operators. The effectiveness of this approach is validated through an optimization-based reconstruction algorithm. RESULTS: The results reveal significantly reduced computation time compared with k-Wave. Particularly in a sparse 3-D configuration, s-Wave exhibits a speed improvement >2000 times compared with k-Wave. In terms of optimization-based image reconstruction, in vivo imaging results reveal that using the s-Wave method yields images highly similar to those obtained using k-Wave, while reducing the reconstruction time by approximately 50 times. CONCLUSION: Proposed here is an accelerated optimization-based algorithm for photoacoustic image reconstruction, using the fast s-Wave forward projection simulation. Our method achieves substantial time savings, particularly in sparse system configurations. Future work will focus on further optimizing the algorithm and expanding its applicability to a broader range of photoacoustic imaging scenarios.

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity.

Active mapping is an important technique for mobile robots to autonomously explore and recognize indoor environments. View planning, as the core of active mapping, determines the quality of the map and the efficiency of exploration. However, most current view-planning methods focus on low-level geometric information like point clouds and neglect the indoor objects that are important for human-robot interaction. We propose a novel View-Planning method for indoor active Sparse Object Mapping (VP-SOM). VP-SOM takes into account for the first time the properties of object clusters in the coexisting human-robot environment. We categorized the views into global views and local views based on the object cluster, to balance the efficiency of exploration and the mapping accuracy. We developed a new view-evaluation function based on objects' information abundance and observation continuity, to select the Next-Best View (NBV). Especially for calculating the uncertainty of the sparse object model, we built the object surface occupancy probability map. Our experimental results demonstrated that our view-planning method can explore the indoor environments and build object maps more accurately, efficiently, and robustly.

Dual-mode sensing chip for photoelectrochemical and electrochromic visual determination of deoxynivalenol mycotoxin.

The successful development of a dual-mode sensing chip for deoxynivalenol (DON) detection using photoelectrochemical (PEC) and electrochromic visualization techniques is reported. By laser etching technology, different functional areas, including the photoanode, the cathode, and the electrochromic area, are fabricated on indium tin oxide (ITO) glass. Then, these three areas are further respectively modified with PEC active materials, platinum nanoparticles, and Prussian blue. Under light illumination, photocurrents generate between the photoanode and the cathode due to the separation of photo-induced electrons and holes in the TiO2/3DNGH material. Meanwhile, the photo-induced electrons are transferred to Prussian blue on the visualization area, which will be reduced to colorless Prussian white. The binding of DON molecules and aptamers can promote electron transfer and reduce the recombination of electrons and holes, allowing for simultaneous quantitative detection of DON using either the photocurrent or color change. The sensor chip has a broad detection range of DON concentrations of 1 fg⋅mL-1 to 100 pg⋅mL-1 in the PEC mode with the limit of detection of 0.37 fg⋅mL-1, and 1 to 250 ng⋅mL-1 in the visualization mode with the limit of detection of 0.51 ng⋅mL-1. This portable dual-mode sensor chip can be used in both laboratory and field settings without the need for specialized instruments, making it a powerful tool for ensuring food safety. At the same time, the analysis of the standard addition method of the actual sample by using the sensor chip shows that, in the PEC mode, the recoveries of the dual-mode aptasensor chip were 91.3 to 99.0% with RSD values of 1.73~2.55%, and in visualization mode, the recoveries of the dual-mode aptasensor chip were 99.2 to 102.0% with RSD values of 1.00~6.21%, which indicate good accuracy and reproducibility.

Uncertainty-aware refinement framework for ovarian tumor segmentation in CECT volume.

BACKGROUND: Ovarian cancer is a highly lethal gynecological disease. Accurate and automated segmentation of ovarian tumors in contrast-enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground-background, all of which contribute to high predictive uncertainty for a segmentation model. PURPOSE: To tackle these challenges, we propose an uncertainty-aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECT images. METHODS: To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertain regions. RESULTS: Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17  mm and 2.57  mm, all of which are significantly better than that of the other state-of-the-art models. And results of visual comparison shows that the compared methods have more mis-segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm3 , indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31 mm), and ASSD (2.32 mm), robustly establishing its superior performance. CONCLUSIONS: Experimental results demonstrate that the framework significantly outperforms the compared state-of-the-art methods, with decreased under- or over-segmentation and better small tumor identification. It has the potential for clinical application.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.

A double-quenching paperclip ECL biosensing platform for ultrasensitive detection of antibiotic resistance genes (mecA) based on Ti3C2 MXene-Au NPs as a coreactant accelerator.

The global spread of environmental biological pollutants, such as antibiotic-resistant bacteria and their antibiotic resistance genes (ARGs), has emerged as a critical public health concern. It is imperative to address this pressing issue due to its potential implications for public health. Herein, a DNA paperclip probe with double-quenching function of target cyclic cleavage was proposed, and an electrochemiluminescence (ECL) biosensing platform was constructed using Ti3C2 MXene in-situ reduction growth of Au NPs (TCM-Au) as a coreactant accelerator, and applied to the sensitive detection of ARGs. Thanks to the excellent catalytic performance, large surface area and Au-S affinity of TCM-Au, the ECL performance of CdS QDs have been significantly improved. By cleverly utilizing the negative charge of the paperclip nucleic acid probe and its modification group, double-quenching of the ECL signal was achieved. This innovative approach, combined with target cyclic amplification, facilitated specific and sensitive detection of the mecA gene. This biosensing platform manifested highly selective and sensitive determination of mecA genes in the range of 10 fM to 100 nM and a low detection limit of 2.7 fM. The credible detectability and anti-interference were demonstrated in Yangtze river and Aeration tank outlet, indicating its promising application toward pollution monitoring of ARGs.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches.

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification.

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.

Photoacoustic digital brain and deep-learning-assisted image reconstruction.

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals' distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull's acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

A novel cosmetic approach for partial matricectomy in treating ingrown toenails.

BACKGROUND: Toenails play a great part in protecting toes and peripheral soft tissues, simultaneously playing a cosmetic role. The ideal treatment should result in a functional and aesthetic outcome. OBJECTIVE: To describe a novel, aesthetic and minimally invasive method to treat ingrown toenail. METHODS: We retrospectively analyzed 436 lesions of 395 ingrown toes in 353 patients with a mean age of 26.0 ± 13.4 (range 10-55) from June 2014 to March 2020 in our department. A novel cosmetic approach for partial matricectomy in treating ingrown toenails was undergone. The average follow-up time was 27.5 ± 2.8 months. The average period prior to work resumption, recurrence rate, and infection rate were measured. Mean pain Visual Analogue Scale (VAS) and Mean satisfaction VAS were used to evaluate the foot appearance. RESULTS: The average period prior work resumption was 2.2 ± 2.1 days (range, 0-7 days). The recurrence rate was 1.6% (7 lesions in 6 patients) at more than 2 years of follow-up. There was no critical complication except infection (0.46%). Mean pain VAS reduced from a preoperative score of 7.7 ± 1.5 points (range, 6-10 points) to a postoperative 3-day score of 2.2 ± 1.0 points (range, 1-4 points; p < 0.001) while Mean satisfaction VAS improved from 1.5 ± 1.3 points (range, 0-3 points) to 9.2 ± 0.6 points (range, 8-10 points; p < 0.001). CONCLUSION: Our proposed approach is minimally invasive relative to conventional methods, which can achieve comparable efficacy to treat ingrown toenails with granulation tissue. Therefore, it can serve as another option to treat this specific type of ingrown toenails.

Hierarchical Organ-Aware Total-Body Standard-Dose PET Reconstruction From Low-Dose PET and CT Images.

Positron emission tomography (PET) is an important functional imaging technology in early disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably increases the exposure risk to patients. To reduce dosage, a lower dose tracer is often used and injected into patients. However, this often leads to low-quality PET images. In this article, we propose a learning-based method to reconstruct total-body standard-dose PET (SPET) images from low-dose PET (LPET) images and corresponding total-body computed tomography (CT) images. Different from previous works focusing only on a certain part of human body, our framework can hierarchically reconstruct total-body SPET images, considering varying shapes and intensity distributions of different body parts. Specifically, we first use one global total-body network to coarsely reconstruct total-body SPET images. Then, four local networks are designed to finely reconstruct head-neck, thorax, abdomen-pelvic, and leg parts of human body. Moreover, to enhance each local network learning for the respective local body part, we design an organ-aware network with a residual organ-aware dynamic convolution (RO-DC) module by dynamically adapting organ masks as additional inputs. Extensive experiments on 65 samples collected from uEXPLORER PET/CT system demonstrate that our hierarchical framework can consistently improve the performance of all body parts, especially for total-body PET images with PSNR of 30.6 dB, outperforming the state-of-the-art methods in SPET image reconstruction.

Antimicrobial Substances and Mechanisms of Lactobacillus rhamnosus against Gardnerella vaginalis.

Bacterial vaginosis (BV) is a common vaginal disease associated with abnormal changes in the vaginal microbiome. Our previous study found that Lactobacillus rhamnosus has a good therapeutic effect on bacterial vaginosis by inhibiting the most prominent bacterium associated with BV, Gardnerella vaginalis. In this study, we show that acetic acid and lactic acid are the main substances in the cell-free supernatant (CFS) of L. rhamnosus that inhibit the growth of G. vaginalis. Further study on the mechanism showed that acetic acid and lactic acid alter the morphology of the G. vaginalis cells, eventually causing the cells to shrink or burst, resulting in exudation of their intracellular contents. In addition, these two organic acids also dissipate the membrane potential of bacterial cells, affecting their synthesis of ATP. A reduced activity of the Na+/K+-ATPase leads to abnormal ATP metabolism, and ultimately inhibits the growth and reproduction of G. vaginalis. Our study provides valuable information for the widespread application of L. rhamnosus in the treatment of bacterial vaginosis.

INFRARED THERMOGRAPHY-BASED BODY-SURFACE THERMAL INHOMOGENEITY MONITORING TO ASSESS THE SEVERITY OF HYPOPERFUSION IN CRITICALLY ILL PATIENTS.

ABSTRACT: Background: Uneven body-surface thermal distribution is a manifestation of hypoperfusion and can be quantified by infrared thermography. Our aim was to investigate whether body-surface thermal inhomogeneity could accurately evaluate the severity of patients at risk of hypoperfusion. Methods: This was a prospective cohort study in which infrared thermography images were taken from unilateral legs of critically ill patients at high risk of hypoperfusion in a cardiac surgical intensive care unit. For each patient, five body-surface thermal inhomogeneity parameters, including standard deviation (SD), kurtosis, skewness, entropy, and low-temperature area rate (LTAR), were calculated. Demographic, clinical, and thermal characteristics of deceased and living patients were compared. The risk of mortality and capillary refill time (CRT) were chosen as the primary outcome and benchmarking parameter for hypoperfusion, respectively. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy. Results: Three hundred seventy-three patients were included, and 55 (14.7%) died during hospital stay. Of inhomogeneity parameters, SD (0.738) and LTAR (0.768) had similar AUROC to CRT (0.757) for assessing mortality risk. Besides, there was a tendency for LTAR (1%-3%-7%) and SD (0.81°C-0.88°C-0.94°C) to increase in normotensive, hypotensive, and shock patients. These thermal parameters are associated with CRT, lactate, and blood pressure. The AUROC of a combined prediction incorporating three thermal inhomogeneity parameters (SD, kurtosis, and entropy) was considerably higher at 0.866. Conclusions: Body-surface thermal inhomogeneity provided a noninvasive and accurate assessment of the severity of critically ill patients at high risk of hypoperfusion.