SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization.

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased performance variance, resulting in significantly distinct summaries even when instruction prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-BasedCalibration (SPeC) pipeline that employs soft prompts to lower variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively regulates variance across different LLMs, providing a more consistent and reliable approach to summarizing critical medical information.

Analysis of coexisting pathogens in nasopharyngeal swabs from COVID-19.

Background: The impact of COVID-19 on the world is still ongoing, and it is currently under regular management. Although most infected people have flu-like symptoms and can cure themselves, coexisting pathogens in COVID-19 patients should not be taken lightly. The present study sought to investigate the coexisting pathogens in SARS-CoV-2 infected patients and identify the variety and abundance of dangerous microbes to guide treatment strategies with a better understanding of the untested factors. Methods: We extracted total DNA and RNA in COVID-19 patient specimens from nasopharyngeal swabs to construct a metagenomic library and utilize Next Generation Sequencing (NGS) to discover chief bacteria, fungi, and viruses in the body of patients. High-throughput sequencing data from Illumina Hiseq 4000 were analyzed using Krona taxonomic methodology for species diversity. Results: We studied 56 samples to detect SARS-CoV-2 and other pathogens and analyzed the species diversity and community composition of these samples after sequencing. Our results showed some threatening pathogens such as Mycoplasma pneumoniae, Klebsiella pneumoniae, Streptococcus pneumoniae, and some previously reported pathogens. SARS-CoV-2 combined with bacterial infection is more common. The results of heat map analysis showed that the abundance of bacteria was mostly more than 1000 and that of viruses was generally less than 500. The pathogens most likely to cause SARS-CoV-2 coinfection or superinfection include Streptococcus pneumoniae, Haemophilus influenzae, Staphylococcus aureus, Klebsiella pneumoniae, and Human gammaherpesvirus 4. Conclusions: The current coinfection and superinfection status is not optimistic. Bacteria are the major threat group that increases the risk of complications and death in COVID-19 patients and attention should be paid to the use and control of antibiotics. Our study investigated the main types of respiratory pathogens prone to coexisting or superinfection in COVID-19 patients, which is valuable for identifying and treating SARS-CoV-2.

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation.

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research.

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath with a demo video at Youtube.

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching.

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.

Local Contrastive Learning for Medical Image Recognition.

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings.

Comparative transcriptomes of nine tissues for the Heilongjiang brown frog (Rana amurensis).

The Heilongjiang brown frog (Rana amurensis) is widely used in traditional Chinese medicine. In particular, the oviduct and skin have been developed into various health products. However, limited numbers of complete genomes of amphibian species have been reported, excluding the Heilongjiang brown frog. Here, the transcriptomes of 45 samples from the liver, spleen, heart, ovaries, thigh muscles, skin, oviduct, stomach and intestine of five Heilongjiang brown frog were reassembled and analyzed. A total of 1,085,532 unigenes with an average length of 676.6 bp and N50 of 722 bp were obtained. Comparative transcriptomics of different tissues detected tissue-specific expression. There were 3248 differentially expressed genes (DEGs) in the ovary, and the number of unique DEGs between the ovary and spleen was the largest. The results of DEGs enrichment showed there were many pathways and items related to protein synthesis and metabolism in the oviduct. The DEGs of the skin were enriched with many bacterial defense items, indicating that there were a large number of antimicrobial peptides in the skin. Thus, these were suitable as biological sources for the development and extraction of antimicrobial peptides. Through the assembly of transcriptome sequencing data and functional annotation of the Heilongjiang brown frog genome, this study provides reference materials for further exploring and utilizing functional gene resources of frogs and lays a foundation for medical research and the development of new products.

Two chronically misdiagnosed patients infected with Nocardia cyriacigeorgica accurately diagnosed by whole genome resequencing.

Nocardiosis is a rare but life-threatening infection particularly affecting immuno-compromised hosts, causing localized or systemic suppurative disease usually in human beings. Nocardia species, as the pathogen of nocardiosis, are difficult to differentiate because of their complex colony morphological features. In this study, we describe two patients who had been misdiagnosed for a long time infected with Nocardia cyriacigeorgica with completely different morphology were accurately diagnosed. Single colonies were analyzed by Gram staining, acid-fast stain, mass spectrometry and whole genome resequencing (WGRS). These two bacterial, strains L5.53 and L5.54, were found to be Gram-negative and acid-fast-weak positive. Blood sample culturing of strain L5.53 yielded white colonies, which were like a layer of hoarfrost, while colonies of L5.54 were yellow, rough, slightly convex. The two strains were identified as Nocardia sp. by mass spectrometry, and WGRS accurately determined them as N. cyriacigeorgica. After medical treatment, one patient was cured and the other was still receiving treatment in the hospital. It can be seen that Nocardia sp. cannot be accurately classified and identified only by phenotypic tests such as bacterial morphological differences, so it is necessary to identify Nocardia spp. with phenotypic tests in combination with other molecular biology technologies, such as WGRS.

Genomics and morphometrics reveal the adaptive evolution of pikas.

Pikas (Lagomorpha: Ochotonidae) are small mouse-like lagomorphs. To investigate their adaptation to different ecological environments during their dispersal from the Qinghai-Xizang (Tibet) Plateau (QTP), we collected 226 pikas and measured 20 morphological characteristics and recorded habitat information. We also sequenced the genome of 81 specimens, representing 27 putative pika species. The genome-wide tree based on 4 090 coding genes identified five subgenera, i.e., Alienauroa, Conothoa, Lagotona, Ochotona, and Pika, consistent with morphometric data. Morphologically, Alienauroa and Ochotona had similar traits, including smaller size and earlier divergence time compared to other pikas. Consistently, the habitats of Alienauroa and Ochotona differed from those of the remaining subgenera. Phylogenetic signal analysis detected 83 genes significantly related to morphological characteristics, including several visual and hearing-related genes. Analysis of shared amino acid substitutions and positively selected genes (PSGs) in Alienauroa and Ochotona identified two genes, i.e., mitochondrial function-related TSFM (p.Q155E) and low-light visual sensitivity-related PROM1 (p.H419Y). Functional experiments demonstrated that TSFM-155E significantly enhanced mitochondrial function compared to TSFM-155Q in other pikas, and PROM1-419Y decreased the modeling of dynamic intracellular chloride efflux upon calcium uptake. Alienauroa and Ochotona individuals mostly inhabit different environments (e.g., subtropical forests) than other pikas, suggesting that a shift from the larger ancestral type and changes in sensory acuity and energy enhancement may have been required in their new environments. This study increases our understanding of the evolutionary history of pikas.

Transcriptomics and metagenomics of common cutworm (Spodoptera litura) and fall armyworm (Spodoptera frugiperda) demonstrate differences in detoxification and development.

BACKGROUND: Spodoptera litura is an important polyphagous pest that causes significant damage to the agricultural sector. We performed RNA-seq of 15 S. litura individuals from larval (fifth and sixth instar larvae), chrysalis, and adult developmental stages. We also compared the S. litura transcriptome data with Spodoptera frugiperda across the same developmental stages, which was sequenced in our previous study. RESULTS: A total of 101,885 differentially expressed transcripts (DETs) were identified in S. litura. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that S. litura may undergo active xenobiotic and detoxifying metabolism during its larval and adult stages, which may explain difficulties with current population control measures. We also found that DETs of single-copy orthologous genes between S. litura and S. frugiperda were involved in basic metabolism and development. However, energy and metabolic processes genes had a higher expression in S. litura, whereas nervous and olfactory function genes had a higher expression in S. frugiperda. Metagenomics analysis in larval S. litura and S. frugiperda revealed that microbiota participate in the detoxification and metabolism processes, but the relative abundance of detoxification-related microbiota was more abundant in S. frugiperda. Transcriptome results also confirmed the detoxification-related pathway of S. frugiperda was more abundant than in S. litura. CONCLUSIONS: Significant changes at transcriptional level were identified during the different development stages of S. litura. Importantly, we also identified detoxification associated genes and gut microbiota between S. litura and S. frugiperda at different developmental stages, which will be valuable in revealing possible mechanisms of detoxification and development in these two lepidopterans.

Defense Against Explanation Manipulation.

Explainable machine learning attracts increasing attention as it improves the transparency of models, which is helpful for machine learning to be trusted in real applications. However, explanation methods have recently been demonstrated to be vulnerable to manipulation, where we can easily change a model's explanation while keeping its prediction constant. To tackle this problem, some efforts have been paid to use more stable explanation methods or to change model configurations. In this work, we tackle the problem from the training perspective, and propose a new training scheme called Adversarial Training on EXplanations (ATEX) to improve the internal explanation stability of a model regardless of the specific explanation method being applied. Instead of directly specifying explanation values over data instances, ATEX only puts constraints on model predictions which avoids involving second-order derivatives in optimization. As a further discussion, we also find that explanation stability is closely related to another property of the model, i.e., the risk of being exposed to adversarial attack. Through experiments, besides showing that ATEX improves model robustness against manipulation targeting explanation, it also brings additional benefits including smoothing explanations and improving the efficacy of adversarial training if applied to the model.

Comparative transcriptomes of three different skin sites for the Asiatic toad (Bufo gargarizans).

Toads release toxic dry secretions from glands in their skin. Toxin possesses a wide range of biological effects, but little is known about its specific gene expression pattern and regulatory mechanisms. The Asiatic toad (Bufo gargarizans) is widely used to produce toxin. Here, we explored the gene expression of 30 tissue samples from three different skin sites (parotoid gland, dorsal skin, and abdomen skin) of B. gargarizans. After de novo assembly, 783,130 unigenes with an average length of 489 bp (N50 = 556 bp) were obtained. A total of 9,248 significant differentially expressed genes (DEGs) were detected. There were 8,819 DEGs between the parotoid gland and abdomen skin and 1,299 DEGs between the dorsal skin and abdomen skin, while only 1,283 DEGs were obtained between the parotoid gland and dorsal skin. Through enrichment analysis, it was found that the detected differential gene expressions corresponded to the different functions of different skin sites. Our key findings were the genetic expression of toxin secretion, the protection function of skin, and the related genes such as HSD3B, Cyp2c, and CAT, LGALS9. In conclusion, we provide useful transcript resources to study the gene expression and gene function of B. gargarizans and other amphibians. The detected DEGs between different sites of the skin provided better insights into the genetic mechanisms of toxin secretion and the protection function of skin for amphibians.

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning.

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.

Transcriptomic landscape of persistent diarrhoea in rhesus macaques and comparison with humans and mouse models with inflammatory bowel disease.

Diarrhoea is a widespread disease in captive rhesus macaques (Macaca mulatta) and a small proportion of individuals may experience persistent diarrhoea. Persistent diarrhoea can lead to a compromised immune system, intestinal inflammation and malnutrition. We analyzed the blood transcriptomes of 10 persistent diarrhoeal and 12 healthy rhesus macaques to investigate the gene expression differences between the two groups. We identified 330 DEGs between persistent diarrhoeal and healthy rhesus macaques. The 211 up-regulated DEGs in the diarrhoeal group were mainly enriched in immune-related and interleukin-related categories. Among them, three interleukin (IL) 18 related DEGs (IL18, IL18R1, and IL18BP) played important roles in actively regulating pro-inflammatory responses. Interestingly, the up- and down-regulated DEGs were both enriched in the same immune-related categories. Thus, we applied a new method to examine the distribution of DEGs in all child categories. We found that interleukin and T cell related categories were mainly occupied by up-regulated DEGs, while immunoglobulin production and B cell related categories were enriched by down-regulated DEGs. We also compared rhesus macaque DEGs with the DEGs of inflammatory bowel disease (IBD) humans and IBD mouse models and found that 30-40% of macaque DEGs were shared with IBD humans and mouse models. In conclusion, our results showed that there were significant immune differences between persistent diarrhoeal rhesus macaques and healthy macaques, which was similar to the expression differences in IBD patients and mouse models.

Sex-specific gene expression in the blood of four primates.

Blood is an important non-reproductive tissue, but little is known about the sex-specific gene expressions in the blood. Therefore, we investigated sex-specific gene expression differences in the blood tissues of four primates, rhesus macaques (Macaca mulatta), Tibetan macaques (M. thibetana), yellow baboons (Papio cynocephalus), and humans. We identified seven sex-specific differentially expressed genes (SDEGs) in each non-human primate and 31 SDEGs in humans. The four primates had only one common SDEG, MAP7D2. In humans, immune-related SDEGs were identified as up-regulated, but also down-regulated in females. We also found that most of the X-Y gene pairs had similar expression levels between species, except pair EIF1AY/EIF1AX. The expression level of X-Y gene pairs of rhesus and Tibetan macaques showed no significant differential expression levels, while humans had six significant XY-biased and three XX-biased X-Y gene pairs. Our observed sex differences in blood should increase understanding of sex differences in primate blood tissue.

Gene Expression Differences Between Developmental Stages of the Fall Armyworm (Spodoptera frugiperda).

The fall armyworm (Spodoptera frugiperda) is one of the most significant agricultural pests in the world and invaded China in early 2019. We sampled and sequenced RNA-seq data from 15 individuals across different developmental stages. Developmental stages were the larval stage (5th instar larvae and 6th instar larvae), chrysalis stage, and adult stage (female adult and male adult). Individual samples were mainly clustered by developmental stages and we then identified variation between developmental stages of differentially expressed transcripts (DETs). There were 2136 upregulated DETs and 1391 downregulated DETs in the larval stage when comparing larval and chrysalis stages. In the comparison between the chrysalis and adult stages, there were 2033 upregulated DETs and 1391 downregulated DETs in the chrysalis stage. In total, 19,195 abundantly expressed transcripts were obtained and 10% of them were DETs. We then obtained stage-specific DETs to investigate the potential function of the fall armyworm during different developmental stages. We also constructed our annotation background set for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. This indicated that the fall armyworm may undergo active metabolism during its lifespan, even in the chrysalis stage. And it also may experience detoxifying and xenobiotic metabolism throughout its life, especially in the larval stage, which partially explains the difficulty to eradicate using chemical control. Our study is the first insight into the developmental patterns of the fall armyworm and we also provide the fundamental information about enhanced drug resistance at the level of transcriptome. These results are beneficial for a future investigation related to the eradication and/or control stage.

Evaluation of Hippo Pathway and CD133 in Radiation Resistance in Small-Cell Lung Cancer.

Although the Hippo pathway and CD133 have been reported to play pertinent roles in a variety of cancer, knowledge about their contribution to radiation resistance in small-cell lung cancer (SCLC) is limited. In this first-of-a-kind study, we have reported the expression of key Hippo pathway proteins in SCLC patients by immunohistochemical staining. We assessed the involvement of yes-associated protein 1 (YAP1) in radiation resistance by Cell Counting Kit-8 (CCK-8) and flow cytometry. In addition, we analysed the impact of CD133 on radiotherapy for SCLC. The mammalian Ste20-like serine/threonine kinase 2(MST2), pMST2, and pYAP1 in the Hippo pathway were not significantly associated with the disease stage and survival time in patients with SCLC. However, the pYAP1 expression showed some significance in the "YAP/TAZ subgroup" of SCLC patients. The proportion of CD133 in the SCLC cells was controlled by the YAP1 expression. The CD133 and YAP1 levels were significantly correlation with each other in tissues of SCLC patients. We sorted and isolated the CD133+ and CD133-cells in H69 and found that the cell surface glycoprotein may be associated with the radiation resistance of SCLC.In summary, we have firstly reported the expression of key Hippo pathway proteins in SCLC patients. Furthermore, we also identified that CD133 may be controlled by the expression of YAP1 in the Hippo pathway and that CD133 may be associated with the radiation resistance of SCLC.

Development and validation of prognostic markers in sarcomas base on a multi-omics analysis.

BACKGROUND: In sarcomas, the DNA copy number and DNA methylation exhibit genomic aberrations. Transcriptome imbalances play a driving role in the heterogeneous progression of sarcomas. However, it is still unclear whether abnormalities of DNA copy numbers are systematically related to epigenetic DNA methylation, thus, a comprehensive analysis of sarcoma occurrence and development from the perspective of epigenetic and genomics is required. METHODS: RNASeq, copy number variation (CNV), methylation data, clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA) and GEO database. The association between methylation and CNV was analyzed to further identify methylation-related genes (MET-Gs) and CNV abnormality-related genes (CNV-Gs). Subsequently DNA copy number, methylation, and gene expression data associated with the MET-Gs and CNV-Gs were integrated to determine molecular subtypes and clinical and molecular characteristics of molecular subtypes. Finally, key biomarkers were determined and validated in independent validation sets. RESULTS: A total of 5354 CNV-Gs and 4042 MET-Gs were screened and showed a high degree of consistency. Four molecular subtypes (iC1, iC2, iC3, and iC4) with different prognostic significances were identified by multiomics cluster analysis, specifically, iC2 had the worst prognosis and iC4 indicated an immune-enhancing state. Three potential prognostic markers (ENO1, ACVRL1 and APBB1IP) were determined after comparing the molecular characteristics of the four molecular subtypes. The expression of ENO1 gene was significantly correlated with CNV, and was noticeably higher in iC2 subtype with the worst prognosis than any other subtypes. The expressions of ACVRL1 and APBB1IP were negatively correlated with methylation, and were high-expressed in the iC4 subtype with the most favorable prognosis. In addition, the number of silent/nonsilent mutations and neoantigens in iC2 subtype were significantly more than those in iC1/iC3/iC4 subtype, and the same trend was also observed in CNV Gain/Loss. CONCLUSION: The current comprehensive analysis of genomic and epigenomic regulation provides new insights into multilayered pathobiology of sarcomas. Four molecular subtypes and three prognostic markers developed in this study improve the current understanding of the molecular mechanisms underlying sarcoma.