RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.Comment: UNSURE2023 (Uncertainty for Safe Utilization of Machine Learning in Medical Imaging) at MICCAI2023; Spotligh

SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings

Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions. Although effective, this paradigm is spatially inflexible, scales poorly to higher-resolution images, and lacks direct understanding of object shapes. To address these limitations, some recent works utilized implicit neural representations (INRs) to learn continuous representations for segmentation. However, these methods often directly adopted components designed for 3D shape reconstruction. More importantly, these formulations were also constrained to either point-based or global contexts, lacking contextual understanding or local fine-grained details, respectively--both critical for accurate segmentation. To remedy this, we propose a novel approach, SwIPE (Segmentation with Implicit Patch Embeddings), that leverages the advantages of INRs and predicts shapes at the patch level--rather than at the point level or image level--to enable both accurate local boundary delineation and global shape coherence. Extensive evaluations on two tasks (2D polyp segmentation and 3D abdominal organ segmentation) show that SwIPE significantly improves over recent implicit approaches and outperforms state-of-the-art discrete methods with over 10x fewer parameters. Our method also demonstrates superior data efficiency and improved robustness to data shifts across image resolutions and datasets. Code is available on Github.Comment: Accepted to 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'23

A Spatial-temporal Rainfall Generator for Flood Response Analysis

The Guangdong-Hong Kong-Macao Bay Area, located in the southeast of China, suffers typhoon-related storms and floods. This paper presents a spatial-temporal rainfall generation model for regional flood response analysis, with its parameters easily obtainable from historical point observations. The model generates point rainfall event series at different rainfall stations with variables describing the external structure and a predefined internal profile within an alternating renewal model framework. Spatial correlation of rainfall process between different sites within the study area is considered, and the areal rainfall distribution of each time slot is obtained from multi-point rainfall amounts. The model performs well in the reproduction of regional rainfall statistical characteristics.This research was supported by Science and Technology Plan of Shenzhen (20180428170335970) and the Research Grants Council of the Hong Kong SAR Government (Nos. C6012-15G and 16206217)

The Potential Antipyretic Mechanism of Gardeniae Fructus and Its Heat-Processed Products With Plasma Metabolomics Using Rats With Yeast-Induced Fever

Gardeniae Fructus (GF), prepared GF (GFP), and carbonized GF (GFC) are widely used in China for the treatment of fever. However, the involved antipyretic mechanism has not been fully elucidated. In this study, rectal temperature and pyrogenic cytokines were used to evaluate the antipyretic effect of raw and processed GF in rats with dry-yeast-induced pyrexia. Reverse phase and hydrophilic interaction liquid chromatography and ultra-high-performance liquid chromatography/mass spectrometry were used to acquire the metabolomics profile of GF, GFP, and GFC in rats with pyrexia. The results showed that the rectal temperature of rats treated with GF, GFP, and GFC was suppressed after 6 h (P < 0.05), compared with that observed in pyrexia model rats. The enzyme-linked immunosorbent assay showed that the expression of tumor necrosis factor α and interleukin 6 were suppressed by GF, GFP, and GFC. Moreover, GFC suppressed the expression of interleukin 6 significantly (P < 0.01). Of note, 11, 15, and 25 feature metabolites were identified in the GF, GFP, and GFC groups. Pathway analysis showed that GF mainly regulated the biosynthesis of valine, leucine, and isoleucine. Notably, GFP was involved in glycerophospholipid metabolism, while GFC was linked to glycerophospholipid and sphingolipid metabolism. These results suggested that GF, GFP, and GFC maintained their antipyretic effect despite heat processing. However, heat processing altered endogenous feature metabolites and certain pathways of GF, GFP, and GFC in rats with yeast-induced pyrexia to exert an antipyretic effect

PTH1-34 Alleviates Radiotherapy-Induced Local Bone Loss by Improving Osteoblast and Osteocyte Survival

Cancer radiotherapy is often complicated by a spectrum of changes in the neighboring bone from mild osteopenia to osteoradionecrosis. We previously reported that parathyroid hormone (PTH, 1–34), an anabolic agent for osteoporosis, reversed bone structural deterioration caused by multiple microcomputed tomography (microCT) scans in adolescent rats. To simulate clinical radiotherapy for cancer patients and to search for remedies, we focally irradiated the tibial metaphyseal region of adult rats with a newly available small animal radiation research platform (SARRP) and treated these rats with intermittent injections of PTH1–34. Using a unique 3D image registration method that we recently developed, we traced the local changes of the same trabecular bone before and after treatments, and observed that, while radiation caused a loss of small trabecular elements leading to significant decreases in bone mass and strength, PTH1–34 preserved all trabecular elements in irradiated bone with remarkable increases in bone mass and strength. Histomorphometry demonstrated that SARRP radiation severely reduced osteoblast number and activity, which were impressively reversed by PTH treatment. In contrast, suppressing bone resorption by alendronate failed to rescue radiation-induced bone loss and to block the rescue effect of PTH1–34. Furthermore, histological analyses revealed that PTH1–34 protected osteoblasts and osteocytes from radiation-induced apoptosis and attenuated radiation-induced bone marrow adiposity. Taken together, our data strongly support a robust radioprotective effect of PTH on trabecular bone integrity through preserving bone formation and shed light on further investigations of an anabolic therapy for radiation-induced bone damage

Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model—An ORS spine section initiative

Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle-punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross-study comparisons. Fleiss\u27s multi-rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, k-means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity

Identification and functional analysis of a chondrocyte-specific internal promoter of the type III collagen gene

Type III collagen is not found in normal growth cartilage. However, we demonstrated previously that embryonic chick chondrocytes contain an unusual transcript derived from the type III collagen gene that initiate at an internal site within intron 23. Use of this internal start site results in replacement of exons 1-23 with a chondrocyte-specific exon (exon 23A) and a change in the translational reading frame; thus the alternative transcript does not encode type III collagen. We have isolated a genomic DNA fragment containing exon 23A by screening a chicken genomic DNA library. The sequences preceding exon 23A did not contain any recognizable promoter elements, in that there was no TATA box or CCAAT box. Thus we initially performed RNase protection and primer extension assays, to confirm that no additional sequences were present at the 5\sp\prime end of the alternative transcript. We subsequently demonstrated that a DNA fragment consisting of 518 bp of intron 23 and 41 bp of exon 23A is highly active in chondrocytes, but appears to be repressed in fibroblasts. Thus we have isolated an internal promoter of the type III collagen gene that appears to be responsible for production of the 4 kb alternative transcript. Analysis of 5\sp\prime end deletion mutants of the internal promoter suggested that the sequences between nucleotides $-$145 and $-$64 are responsible for determining the chondrocyte-preferential activity of this promoter. We subsequently used linker substitution mutagenesis and electrophoretic mobility shift assays to demonstrate that the transcription factor AP1 binds to a site at $-$122 to $-$116 that is responsible in part for the high level of activity of this promoter in chondrocytes, and also contributes to the low activity of the promoter in skin fibroblasts. Furthermore, we have identified an element between $-$73 and $-$56 that is responsible for repression of transcriptional activity in fibroblasts. One transcription factor that binds this site is similar or identical to the previously identified factor LBP-1

Impacts of COVID-19 on Informal Workers and National Policies in China

The impact of the epidemic on China's economy is huge. By analyzing the impact of the epidemic on the informal economy and consulting the measures taken by local governments in Sichuan to restore the stall economy in the post epidemic period, this paper classifies and refines the measures made by urban governments of different sizes of cities, puts forward relevant laws, and puts forward opinions and forecasts on the future trend of the stall economy and stall economy in the post epidemic period. Due to the impact of the epidemic, people's awareness of self-protection has increased, local governments have also strengthened prevention and control, and the business of vendors has been seriously affected. The Sichuan case shows that the government's encouragement is an important guarantee for the rapid recovery of the stall economy. At the same time, reasonable control is a necessary means to prevent the recurrence of the epidemic. For the future trend of the stall, it is a trend to set up permitted-vending-places (shudaoqu). Selecting an address according to the nature of the commodity is the guarantee of sales