bluuebunny/crossref_metadata_2025_split huggingface dataset:
PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images
Authors: Yan Chaoyang , Sun Jialiang , Guan Yiming , Feng Jiuxin , Liu Hong , Liu Jian 
Published: June 2024

Abstract

Motivation
Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover morphological patterns associated with HER2 molecular targets and achieve precise status prediction. However, pathological images are typically equipped with high-resolution attributes, and HER2 expression in breast cancer (BC) images often manifests the intratumoral heterogeneity.

Results
We present a phenotype-informed weakly supervised multiple instance learning architecture (PhiHER2) for the prediction of the HER2 status from pathological images of BC. Specifically, a hierarchical prototype clustering module is designed to identify representative phenotypes across whole slide images. These phenotype embeddings are then integrated into a cross-attention module, enhancing feature interaction and aggregation on instances. This yields a phenotype-based feature space that leverages the intratumoral morphological heterogeneity for HER2 status prediction. Extensive results demonstrate that PhiHER2 captures a better WSI-level representation by the typical phenotype guidance and significantly outperforms existing methods on real-world datasets. Additionally, interpretability analyses of both phenotypes and WSIs provide explicit insights into the heterogeneity of morphological patterns associated with molecular HER2 status.

Availability and implementation
Our model is available at https://github.com/lyotvincent/PhiHER2
Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models
Authors: Jeong Minbyul , Sohn Jiwoong , Sung Mujeen , Kang Jaewoo 
Published: June 2024

Abstract

Summary
Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice questions to long-form generations. To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation. However, when applying existing methods to different domain-specific problems, poor generalization becomes apparent, leading to fetching incorrect documents or making inaccurate judgments. In this paper, we introduce Self-BioRAG, a framework reliable for biomedical text that specializes in generating explanations, retrieving domain-specific documents, and self-reflecting generated responses. We utilize 84k filtered biomedical instruction sets to train Self-BioRAG that can assess its generated explanations with customized reflective tokens. Our work proves that domain-specific components, such as a retriever, domain-related document corpus, and instruction sets are necessary for adhering to domain-related instructions. Using three major medical question-answering benchmark datasets, experimental results of Self-BioRAG demonstrate significant performance gains by achieving a 7.2% absolute improvement on average over the state-of-the-art open-foundation model with a parameter size of 7B or less. Similarly, Self-BioRAG outperforms RAG by 8% Rouge-1 score in generating more proficient answers on two long-form question-answering benchmarks on average. Overall, we analyze that Self-BioRAG finds the clues in the question, retrieves relevant documents if needed, and understands how to answer with information from retrieved documents and encoded knowledge as a medical expert does. We release our data and code for training our framework components and model weights (7B and 13B) to enhance capabilities in biomedical and clinical domains.

Availability and implementation
Self-BioRAG is available at https://github.com/dmis-lab/self-biorag.
Integrating patients in time series clinical transcriptomics data
Authors: Hasanaj Euxhen , Mathur Sachin , Bar-Joseph Ziv 
Published: June 2024

Abstract

Motivation
Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort.

Results
We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns.

Availability and implementation
The source code and instructions to download the data have been deposited on GitHub at https://github.com/euxhenh/Truffle.
Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation
Authors: Chen Jiyu , Goudey Benjamin , Geard Nicholas , Verspoor Karin 
Published: June 2024

Abstract

Motivation
Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.

Results
We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.

This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge.

Availability and implementation
https://github.com/jiyuc/de-inconsistency.
Predicting single-cell cellular responses to perturbations using cycle consistency learning
Authors: Huang Wei , Liu Hui 
Published: June 2024

Abstract

Summary
Phenotype-based drug screening emerges as a powerful approach for identifying compounds that actively interact with cells. Transcriptional and proteomic profiling of cell lines and individual cells provide insights into the cellular state alterations that occur at the molecular level in response to external perturbations, such as drugs or genetic manipulations. In this paper, we propose cycleCDR, a novel deep learning framework to predict cellular response to external perturbations. We leverage the autoencoder to map the unperturbed cellular states to a latent space, in which we postulate the effects of drug perturbations on cellular states follow a linear additive model. Next, we introduce the cycle consistency constraints to ensure that unperturbed cellular state subjected to drug perturbation in the latent space would produces the perturbed cellular state through the decoder. Conversely, removal of perturbations from the perturbed cellular states can restore the unperturbed cellular state. The cycle consistency constraints and linear modeling in the latent space enable to learn transferable representations of external perturbations, so that our model can generalize well to unseen drugs during training stage. We validate our model on four different types of datasets, including bulk transcriptional responses, bulk proteomic responses, and single-cell transcriptional responses to drug/gene perturbations. The experimental results demonstrate that our model consistently outperforms existing state-of-the-art methods, indicating our method is highly versatile and applicable to a wide range of scenarios.

Availability and implementation
The source code is available at: https://github.com/hliulab/cycleCDR.
Optimal phylogenetic reconstruction of insertion and deletion events
Authors: Tule Sanjana , Foley Gabriel , Zhao Chongting , Forbes Michael , Bodén Mikael 
Published: June 2024

Abstract

Motivation
Insertions and deletions (indels) influence the genetic code in fundamentally distinct ways from substitutions, significantly impacting gene product structure and function. Despite their influence, the evolutionary history of indels is often neglected in phylogenetic tree inference and ancestral sequence reconstruction, hindering efforts to comprehend biological diversity determinants and engineer variants for medical and industrial applications.

Results
We frame determining the optimal history of indel events as a single Mixed-Integer Programming (MIP) problem, across all branch points in a phylogenetic tree adhering to topological constraints, and all sites implied by a given set of aligned, extant sequences. By disentangling the impact on ancestral sequences at each branch point, this approach identifies the minimal indel events that jointly explain the diversity in sequences mapped to the tips of that tree. MIP can recover alternate optimal indel histories, if available. We evaluated MIP for indel inference on a dataset comprising 15 real phylogenetic trees associated with protein families ranging from 165 to 2000 extant sequences, and on 60 synthetic trees at comparable scales of data and reflecting realistic rates of mutation. Across relevant metrics, MIP outperformed alternative parsimony-based approaches and reported the fewest indel events, on par or below their occurrence in synthetic datasets. MIP offers a rational justification for indel patterns in extant sequences; importantly, it uniquely identifies global optima on complex protein data sets without making unrealistic assumptions of independence or evolutionary underpinnings, promising a deeper understanding of molecular evolution and aiding novel protein design.

Availability and implementation
The implementation is available via GitHub at https://github.com/santule/indelmip.
TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records
Authors: Al Olaimat Mohammad , Bozdag Serdar ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: June 2024

Abstract

Motivation
Electronic health records (EHRs) represent a comprehensive resource of a patient’s medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient’s clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit.

Results
The results of the experiments conducted on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer’s Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions.

Availability and implementation
https://github.com/bozdaglab/TA-RNN.
CPC11 A case of C3 bullous dermatosis
Authors: Gaglani Bindi , De Mozzi Paola , Watts Sophia 
Published: June 2024

Abstract
A 44-year-old Asian man was seen as an urgent referral in dermatology for insidious-onset multiple pruritic tense serous-filled blisters. These were of varying sizes and occurred over acral sites and the body, sparing mucosal sites. He has a history of eczema as a child, which was in remission now, and no other medical problems and no regular medications. He denied any bowel problems. A punch biopsy showed subepidermal blistering with few eosinophils and a perivascular lymphocytic infiltrate. A perilesional indirect immunofluorescence was negative for IgG, IgM, IgA and C3 initially, and was repeated later to show granular C3 deposition at the basement membrane. Indirect immuno­fluorescence and enzyme-linked immunosorbent assay were negative, as was as an autoimmune screen including antinuclear antibodies, extractable nuclear antigen, dsDNA and coeliac screen. The condition was very sensitive to oral steroids, with relapse of the condition after slow wean of prednisolone. C3 bullous dermatosis is a rare form of bullous dermatosis first described by Hashimoto et al. in 2016 (Hashimoto T, Tsuruta D, Yasukochi A et al. Granular C3 dermatosis. Acta Derm Venereol 2016; 96: 748–53). Since its initial report, only 21 such patients have been reported in the literature in total. This condition appears to exhibit clinical characteristics resembling dermatitis herpetiformis in the absence of other diagnostic criteria for coeliac disease. It also presents histopathological features marked by subepidermal blister formation, oedema, and liquefaction degeneration accompanied by infiltrations of lymphocytes, eosinophils and neutrophils in varying combinations. Despite its benign disease progression, it demonstrates a positive response to oral dapsone and topical steroids. Additionally, granular deposition of C3 and C5–C9 is observed, while other complement components do not show significant deposition. Notably, there is a slight reduction in the expression of C3 in the epidermis.
Bristol Cup PostersP001 A descriptive, observational and cross-sectional study of clinicoepidemiological, dermoscopic and histopathological aspects of primary localized cutaneous amyloidosis and its effect on the quality of life of the patient
Authors: Rane Meghana , Mahajan Sunanda 
Published: June 2024

Abstract
The aims of this study were to examine the clinicoepidemiological factors related to primary localized cutaneous amyloidosis; to investigate its dermoscopic and histopathological findings; to determine the clinicohistopathological and dermoscopic correlation; and to study its effect on the quality of life of the patients (Al-Ratrout JT, Satti MB. Primary localized cutaneous amyloidosis: a clinicopathological study from Saudi Arabia. Int J Dermatol 1997; 36: 428–34; Rasi A, Khatami A, Javaheri SM. Macular amyloidosis: an assessment of prevalence, sex and age. Int J Dermatol 2004; 43: 898–9). An open, prospective, descriptive and observational study was conducted in patients with primary cutaneous localized amyloidosis suspected clinically. It was carried out for a period of 18 months, with 100 patients in the study. Only patients who gave written informed consent for the study were included. Pregnant and lactating patients were also excluded. Clinical examination with clinical photographs, skin biopsy and dermoscopy of the same lesions was done using a disposable handheld biopsy punch under local anaesthesia. The final diagnosis was then made after compiling all of the clinical, dermoscopic and histopathological features. Microsoft Excel and SPSS version 19 (IBM, Armonk, NY, USA) were used for statistical analysis. Of the 100 patients included in the study, 56 had macular amyloidosis, 33 had lichenoid amyloidosis, eight had biphasic amyloidosis, two had dyschromic amyloidosis and had primary amyloidosis of the concha. The most common age group was 40–60 years, and the condition was more common in women. A history of use of scrubbing and rubbing while bathing was reported in 45 patients: 33 (73%) of these had macular amyloidosis, nine (20%) had lichenoid amyloidosis, two (4%) had mixed amyloidosis and one (2%) had dyschromic amyloidosis. Pruritus was seen in 92 patients (92%). Dermoscopy of amyloidosis showed a hub-and-spoke pattern and amorphous pigmented globules; 11 patients showed nonspecific pigmented globules and seven showed scales and a central scar pattern. On histopathology of macular amyloidosis, 94 patients showed amyloid deposits stained positively with Congo red. The mean Dermatology Life Quality Index was 3 in macular amyloidosis, and 4 in lichenoid and biphasic amyloidosis. Studies using confocal laser scanning microscopy and direct immunofluorescence could further provide insight and can allow better outcomes. Primary cutaneous amyloidosis has a small-to-moderate effect on the quality of life of patients as it is pruritic and disfiguring. Dermoscopy is a valuable tool to diagnose characteristic features and thus helps to make quick and accurate diagnosis and avoid skin biopsies, as shown in this study.
P007 Secretory carcinoma arising from axillary accessory breast: a rare presentation
Authors: Li Chia-Lun , Huang Chi-Cheng , Kao Tzu-Hang , Chang Tsung-Hsien 
Published: June 2024

Abstract
A 61-year-old woman presented with a 7-month history of a subcutaneous nodule in her left axilla. On examination, a 1.5 × 1.5-cm mobile rubbery nodule was noted in her left axilla. Biopsy of the lesion revealed neoplastic cuboidal epithel­ial cells in a microcystic and papillary-cystic pattern adjacent to non-neoplastic accessory breast tissue in the deep subcutaneous layer. The cells had monotonous nuclei, eosinophilic cytoplasm and distinct nucleoli. Immunohistochemically, the tumour cells were diffusely positive for S100, CK5/7, GATA3 and pan-TRK, while oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2) stained negative. Further image investigation revealed no evidence of malignancy in the breasts, lymph nodes or salivary glands. A diagnosis of secretory carcinoma (SC) arising from the accessory breast was made based on the clinicopathological correlation. The main differential diagnoses included primary cutaneous SC (PCSC), metastatic breast SC, and metastatic salivary SC. In this case, the presence of accessory breast tissue adjacent to the neoplasm, tumour involvement in the deep dermal–subcutaneous layer, and negative primary tumour in the breast and salivary glands favoured SC of the accessory breast. A wide excision with 1-cm safety margin was performed. The next-generation sequencing analysis confirmed presence of neurotrophic tyrosine receptor kinase 3 (NTRK3) fusion genes. SC of the breast is a very rare subtype of breast carcinoma, accounting for only 0.15% of all breast cancers. Clinically, it manifests as an indolent subcutaneous mass on the breast. Pathologically, SC is characterized by tumour cells exhibiting intracellular and extracellular secretory materials, forming patterns including cribriform, papillary-cystic, lobulated and microcystic. Immunohistochemically, SC expresses S100, basal-like markers (such as CK5/6), epidermal growth factor receptor and pan-TRK, and is usually negative for oestrogen receptor, progesterone receptor and HER2. Studies have shown that these carcinomas develop due to t(12,15) ETV6–NTRK3 gene translocation. Notably, the genetic alteration is not exclusive to a specific origin site, emphasizing the importance of clinicopathological correlation for accurate diagnosis. Furthermore, while most PCSCs are superficially located in the dermis, SC originating from accessory breast is found in the deeper subcutaneous layer, often adjacent to breast tissue. To our knowledge, this is the third report of SC arising from the axillary accessory breast. Our patient highlights the significance of considering SC of the accessory breast as a potential differential diagnosis for indolent axillary masses.