Effect of postbiotic Lactiplantibacillus plantarum LRCC5314 supplemented in powdered milk on type 2 diabetes in mice

Type 2 diabetes (T2D) is a chronic multifactorial disease characterized by a combination of insulin resistance and impaired glucose regulation. The alleviative effects of probiotics on T2D have been widely studied. However, studies on the effects of postbiotics, known as inactivated probiotics, on dairy products are limited. This study aimed to evaluate the effectiveness of postbiotic Lactiplantibacillus plantarum LRCC5314 in milk powder (MP-LRCC5314) in a stress-induced T2D (stress-T2D) mouse model. Compared with probiotic MP-LRCC5314, postbiotic MP-LRCC5314 significantly influenced stress-T2D-related factors. The administration of heat-killed MP-LRCC5314 reduced corticosterone levels, increased short-chain fatty acid production by modulating gut microbiota, and regulated immune response, glucose metabolism, stress-T2D-related biomarkers in the brain, gut, and adipose tissues, as well as glucose and insulin sensitivity. In addition, heat-killed MP-LRCC5314 treatment led to a decrease in pro-inflammatory cytokine levels and an increase in anti-inflammatory cytokine levels. Overall, these findings suggest that adding postbiotic MP-LRCC5314 to milk powder could serve as a potential supplement for stress-T2D mitigation.

Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography

Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techniques can disrupt contrastive learning owing to the subtle differences between other standardized CXRs compared to augmented positive pairs; therefore, additional efforts are required. In this study, we propose intermediate feature approximation (IFA) loss, which improves the performance of contrastive convolutional neural networks by focusing more on positive representations of CXRs without additional augmentations. The IFA loss encourages the feature maps of a query image and its positive pair to resemble each other by maximizing the cosine similarity between the intermediate feature outputs of the original data and the positive pairs. Therefore, we used the InfoNCE loss, which is commonly used loss to address negative representations, and the IFA loss, which addresses positive representations, together to improve the contrastive network. We evaluated the performance of the network using various downstream tasks, including classification, object detection, and a generative adversarial network (GAN) inversion task. The downstream task results demonstrated that IFA loss can improve the performance of effectively overcoming data imbalance and data scarcity; furthermore, it can serve as a perceptual loss encoder for GAN inversion. In addition, we have made our model publicly available to facilitate access and encourage further research and collaboration in the field.

Chip collection of hepatocellular carcinoma based on O2 heterogeneity from patient tissue

Hepatocellular carcinoma frequently recurs after surgery, necessitating personalized clinical approaches based on tumor avatar models. However, location-dependent oxygen concentrations resulting from the dual hepatic vascular supply drive the inherent heterogeneity of the tumor microenvironment, which presents challenges in developing an avatar model. In this study, tissue samples from 12 patients with hepatocellular carcinoma are cultured directly on a chip and separated based on preference of oxygen concentration. Establishing a dual gradient system with drug perfusion perpendicular to the oxygen gradient enables the simultaneous separation of cells and evaluation of drug responsiveness. The results are further cross-validated by implanting the chips into mice at various oxygen levels using a patient-derived xenograft model. Hepatocellular carcinoma cells exposed to hypoxia exhibit invasive and recurrent characteristics that mirror clinical outcomes. This chip provides valuable insights into treatment prognosis by identifying the dominant hepatocellular carcinoma type in each patient, potentially guiding personalized therapeutic interventions.

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

RATIONALE AND OBJECTIVES

The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels.

MATERIALS AND METHODS

Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method.

RESULTS

A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA [-0.4, 0.4]).

CONCLUSION

Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.

CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning

Training deep learning models on medical images heavily depends on experts' expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs). Our contribution is a publicly accessible pretrained model trained with a 4.8-M CXR dataset using self-supervised learning with a contrastive learning and its validation with various kinds of downstream tasks including classification on the 6-class diseases in internal dataset, diseases classification in CheXpert, bone suppression, and nodule generation. When compared to a scratch model, on the 6-class classification test dataset, we achieved 28.5% increase in accuracy. On the CheXpert dataset, we achieved 1.3% increase in mean area under the receiver operating characteristic curve on the full dataset and 11.4% increase only using 1% data in stress test manner. On bone suppression with perceptual loss, we achieved improvement in peak signal to noise ratio from 34.99 to 37.77, structural similarity index measure from 0.976 to 0.977, and root-square-mean error from 4.410 to 3.301 when compared to ImageNet pretrained model. Finally, on nodule generation, we achieved improvement in Fréchet inception distance from 24.06 to 17.07. Our study showed the decent transferability of CheSS weights. CheSS weights can help researchers overcome data imbalance, data shortage, and inaccessibility of medical image datasets. CheSS weight is available at https://github.com/mi2rl/CheSS .

Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies

Hematoxylin and eosin (H&E) staining is the gold standard modality for diagnosis in medicine. However, the dosage ratio of hematoxylin to eosin in H&E staining has not been standardized yet. Additionally, H&E stains fade out at various speeds. Therefore, the staining quality could differ among each image, and stain normalization is a critical preprocessing approach for training deep learning (DL) models, especially in long-term and/or multicenter digital pathology studies. However, conventional methods for stain normalization have some significant drawbacks, such as collapsing in the structure and/or texture of tissue. In addition, conventional methods must require a reference patch or slide. Meanwhile, DL-based methods have a risk of overfitting and/or grid artifacts. We developed a score-based diffusion model of colorization for stain normalization. However, mistransfer, in which the model confuses hematoxylin with eosin, can occur using a score-based diffusion model due to its high diversity nature. To overcome this mistransfer, we propose a stain separation method using sparse non-negative matrix factorization (SNMF), which can decompose pathology slide into Hematoxylin and Eosin to normalize each stain component. Furthermore, inpainting with overlapped moving window patches was used to prevent grid artifacts of whole slide image normalization. Our method can normalize the whole slide pathology images through this stain normalization pipeline with decent performance.

Cross-platform comparison of arbitrary quantum states

As we approach the era of quantum advantage, when quantum computers (QCs) can outperform any classical computer on particular tasks, there remains the difficult challenge of how to validate their performance. While algorithmic success can be easily verified in some instances such as number factoring or oracular algorithms, these approaches only provide pass/fail information of executing specific tasks for a single QC. On the other hand, a comparison between different QCs preparing nominally the same arbitrary circuit provides an insight for generic validation: a quantum computation is only as valid as the agreement between the results produced on different QCs. Such an approach is also at the heart of evaluating metrological standards such as disparate atomic clocks. In this paper, we report a cross-platform QC comparison using randomized and correlated measurements that results in a wealth of information on the QC systems. We execute several quantum circuits on widely different physical QC platforms and analyze the cross-platform state fidelities.

P-530 tRNA fragments in follicle fluid can be explored in Diminished Ovarian Reserve patients as a marker of IVF outcomes

Study question

Can tRNA fragments in follicle fluid be explored for markers that predict blastocyst formation in patients with diminished ovarian reserve?

Summary answer

tRNA-Ser-CGA-4 is up-regulated in the follicle fluid of patients with diminished ovarian reserve (DOR)

What is known already

tRNA fragments are a novel class of small non-coding RNAs that have been shown to play regulatory roles in reproductive biology.

Study design, size, duration

Non-Randomized case control study in patients presenting for IVF treatment at a University affiliated fertility clinic from September 2020- January 2021. Thirty three patients were included: 15 patients with DOR and 18 controls.

Participants/materials, setting, methods

Patients were stimulated by appropriate protocols according to their age, diagnosis and AMH.

At retrieval, non-bloody follicle fluid (FF) was retrieved for after removing the cumulus-oophorus complex. FF was centrifuged at 1600 g x 10 mins and filtered with 80 nm filter for analysis.

Main results and the role of chance

There were no differences between the two groups with respect to age, BMI and duration of infertility. The mean age was 35.1 years

As anticipated, the mean AMH, antral follicle count and number of eggs retrieved were statistically lower in the DOR groups compared with controls (<0.05)

The fertilization rate was 61.2% in DOR vs 79.5% in controls

The blastocyst rate was 39.5% in DOR patients and 50.6% in controls

Total RNA yield was lower in DOR patients, as 50% of the DOR samples had undetectable levels (p < 0.001).

Samples were predominantly processed in duplicate. Input into the pipeline was paired end data which showed an overall tRNA fragment count that was higher in the DOR population. Log2FC showed an up-regulation of the tRNA fragments tRNA-Ser-CGA-4, tRNA-Cys-GCA-15, tRNA-Cys-GCA-10 and tRNA-Cys-GCA-19 in the DOR population.

Limitations, reasons for caution

Undetectable levels in follicle fluid of some of the DOR patients introduced selection bias

Larger sample size and broader applicability across various ethnic groups.

Wider implications of the findings

Follicle fluid can be explored to give insight into the signaling pathways of ovarian biology

Non-invasive method of assessing oocyte competence

Useful in explaining the pathophysiology of DOR

Can these findings assist is in the identification of therapeutic targets to delay the progression of age related/non-age related DOR?

Trial registration number

not applicable

An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.

Determining the Degree of Dopaminergic Denervation Based on the Loss of Nigral Hyperintensity on SMWI in Parkinsonism

BACKGROUND AND PURPOSE

Nigrostriatal dopaminergic function in patients with Parkinson disease can be assessed using ¹²³I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropan dopamine transporter (¹²³I-FP-CIT) SPECT, and a good correlation has been demonstrated between nigral status on SWI and dopaminergic denervation on ¹²³I-FP-CIT SPECT. Here, we aim to correlate quantified dopamine transporter attenuation on ¹²³I-FP-CIT SPECT with nigrosome-1 status using susceptibility map-weighted imaging (SMWI).

MATERIALS AND METHODS

Between May 2017 and January 2018, consecutive patients with idiopathic Parkinson disease (n = 109) and control participants (n = 29) who underwent ¹²³I-FP-CIT SPECT with concurrent 3T SWI were included. SMWI was generated from SWI. Two neuroradiologists evaluated nigral hyperintensity from nigrosome-1 on each side of the substantia nigra. Using consensus reading, we compared the ¹²³I-FP-CIT-specific binding ratio according to nigral hyperintensity status and the ¹²³I-FP-CIT specific binding ratio threshold to confirm the loss of nigral hyperintensity was determined using receiver operating characteristic curve analysis.

RESULTS

The concordance rate between SMWI and ¹²³I-FP-CIT SPECT was 65.9%. The ¹²³I-FP-CIT-specific binding ratios in the striatum, caudate nucleus, and putamen were significantly lower when nigral hyperintensity in the ipsilateral substantia nigra was absent than when present (all, P < .001). The ¹²³I-FP-CIT-specific binding ratio threshold values for the determination of nigral hyperintensity loss were 2.56 in the striatum (area under the curve, 0.890), 3.07 in the caudate nucleus (0.830), and 2.36 in the putamen (0.887).

CONCLUSIONS

Nigral hyperintensity on SMWI showed high positive predictive value and low negative predictive value with dopaminergic degeneration on ¹²³I-FP-CIT SPECT. In patients with Parkinson disease, the loss of nigral hyperintensity is prominent in patients with lower striatal specific binding ratios.

Molecular insights into the development of hepatic metastases in colorectal cancer: a metastasis prediction study

OBJECTIVE

Colorectal cancer is presently the third most commonly diagnosed cancer in the United States. In this study, we identified molecular differences between hepatic and non-hepatic metastases in colorectal cancer and evaluated their prognostic significance.

MATERIALS AND METHODS

We downloaded primary data from the NCBI Gene Expression Omnibus (GSE6988, GSE62321, GSE50760, and GSE28722). To identify the molecular differences, we used the Significance Analysis of Microarray method. We selected nine prognostic genes (SYTL2, PTPLAD1, CDS1, RNF138, PIGR, WDR78, MYO7B, TSPAN3, and ATP5F1) with hepatic metastasis prediction score in colorectal cancer (hereafter referred to as LASSO Score). We confirmed the prognostic significance of the LASSO Score by using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristic curve at 1-5 years.

RESULTS

Survival analysis revealed that a high LASSO Score is associated with a poor prognosis in colorectal cancer patients with hepatic metastases (p = 0). Analysis of C-indices and AUC values from the receiver operating characteristic curve further supported this prediction by the LASSO Score. Multivariate analysis confirmed the prognostic significance of the LASSO Score (p = 1.13e-06).

CONCLUSIONS

This study reveals the biological mechanisms underlying hepatic metastases in colorectal cancer and will help in developing targeted therapies for colorectal cancer.

Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics

BACKGROUND AND PURPOSE

Human papillomavirus is a prognostic marker for oropharyngeal squamous cell carcinoma. We aimed to determine the value of CT-based radiomics for predicting the human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

MATERIALS AND METHODS

Eighty-six patients with oropharyngeal squamous cell carcinoma were retrospectively collected and grouped into training (n = 61) and test (n = 25) sets. For human papillomavirus status and overall survival prediction, radiomics features were selected via a random forest-based algorithm and Cox regression analysis, respectively. Relevant features were used to build multivariate Cox regression models and calculate the radiomics score. Human papillomavirus status and overall survival prediction were assessed via the area under the curve and concordance index, respectively. The models were validated in the test and The Cancer Imaging Archive cohorts (n = 78).

RESULTS

For prediction of human papillomavirus status, radiomics features yielded areas under the curve of 0.865, 0.747, and 0.834 in the training, test, and validation sets, respectively. In the univariate Cox regression, the human papillomavirus status (positive: hazard ratio, 0.257; 95% CI, 0.09-0.7; P = .008), T-stage (≥III: hazard ratio, 3.66; 95% CI, 1.34-9.99; P = .011), and radiomics score (high-risk: hazard ratio, 3.72; 95% CI, 1.21-11.46; P = .022) were associated with overall survival. The addition of the radiomics score to the clinical Cox model increased the concordance index from 0.702 to 0.733 (P = .01). Validation yielded concordance indices of 0.866 and 0.720.

CONCLUSIONS

CT-based radiomics may be useful in predicting human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

Benchmarking an 11-qubit quantum computer

The field of quantum computing has grown from concept to demonstration devices over the past 20 years. Universal quantum computing offers efficiency in approaching problems of scientific and commercial interest, such as factoring large numbers, searching databases, simulating intractable models from quantum physics, and optimizing complex cost functions. Here, we present an 11-qubit fully-connected, programmable quantum computer in a trapped ion system composed of 13 ¹⁷¹Yb⁺ ions. We demonstrate average single-qubit gate fidelities of 99.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, average two-qubit-gate fidelities of 97.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, and SPAM errors of 0.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%. To illustrate the capabilities of this universal platform and provide a basis for comparison with similarly-sized devices, we compile the Bernstein-Vazirani and Hidden Shift algorithms into our native gates and execute them on the hardware with average success rates of 78\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% and 35\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, respectively. These algorithms serve as excellent benchmarks for any type of quantum hardware, and show that our system outperforms all other currently available hardware.

The growing complexity of quantum computing devices makes presents challenges for benchmarking their performance as previous, exhaustive approaches become infeasible. Here the authors characterise the quality of their 11-qubit device by successfully computing two quantum algorithms.