Hasil untuk "Medicine"

Jianmin Li, Ying Chang, Su-Kit Tang et al.

Background: Retrieval augmented generation (RAG) technology can empower large language models (LLMs) to generate more accurate, professional, and timely responses without fine tuning. However, due to the complex reasoning processes and substantial individual differences involved in traditional Chinese medicine (TCM) clinical diagnosis and treatment, traditional RAG methods often exhibit poor performance in this domain. Objective: To address the limitations of conventional RAG approaches in TCM applications, this study aims to develop an improved RAG framework tailored to the characteristics of TCM reasoning. Methods: We developed TCM-DiffRAG, an innovative RAG framework that integrates knowledge graphs (KG) with chains of thought (CoT). TCM-DiffRAG was evaluated on three distinctive TCM test datasets. Results: The experimental results demonstrated that TCM-DiffRAG achieved significant performance improvements over native LLMs. For example, the qwen-plus model achieved scores of 0.927, 0.361, and 0.038, which were significantly enhanced to 0.952, 0.788, and 0.356 with TCM-DiffRAG. The improvements were even more pronounced for non-Chinese LLMs. Additionally, TCM-DiffRAG outperformed directly supervised fine-tuned (SFT) LLMs and other benchmark RAG methods. Conclusions: TCM-DiffRAG shows that integrating structured TCM knowledge graphs with Chain of Thought based reasoning substantially improves performance in individualized diagnostic tasks. The joint use of universal and personalized knowledge graphs enables effective alignment between general knowledge and clinical reasoning. These results highlight the potential of reasoning-aware RAG frameworks for advancing LLM applications in traditional Chinese medicine.

Chengfeng Dou, Ying Zhang, Zhi Jin et al.

Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making processes. Although it benefits from current retrieval-augmented generation~(RAG) technologies, it still faces two significant challenges: the collection of dispersed evidence and the efficient organization of this evidence to support the complex queries necessary for EBM. To tackle these issues, we propose using LLMs to gather scattered evidence from multiple sources and present a knowledge hypergraph-based evidence management model to integrate these evidence while capturing intricate relationships. Furthermore, to better support complex queries, we have developed an Importance-Driven Evidence Prioritization (IDEP) algorithm that utilizes the LLM to generate multiple evidence features, each with an associated importance score, which are then used to rank the evidence and produce the final retrieval results. Experimental results from six datasets demonstrate that our approach outperforms existing RAG techniques in application domains of interest to EBM, such as medical quizzing, hallucination detection, and decision support. Testsets and the constructed knowledge graph can be accessed at \href{https://drive.google.com/file/d/1WJ9QTokK3MdkjEmwuFQxwH96j_Byawj_/view?usp=drive_link}{https://drive.google.com/rag4ebm}.

Keshav Jha, Joseph Mayer

Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preoperative planning model has the capability to move in similar ways to the anatomy that is being represented by the model, or reveal additional issues that may arise during the use of a movement mechanism. The 3D-printed models are constructed in a similar manner to their static counterparts; however, in the digital post-processing phase, additional care is needed to ensure the dynamic functionality of the model. Here, we discuss the process of creating a dynamic 3D-printed model and its benefits and uses in modern medicine.

ChaoBo Zhang, Long Tan

Artificial intelligence technology plays a crucial role in recommending prescriptions for traditional Chinese medicine (TCM). Previous studies have made significant progress by focusing on the symptom-herb relationship in prescriptions. However, several limitations hinder model performance: (i) Insufficient attention to patient-personalized information such as age, BMI, and medical history, which hampers accurate identification of syndrome and reduces efficacy. (ii) The typical long-tailed distribution of herb data introduces training biases and affects generalization ability. (iii) The oversight of the 'monarch, minister, assistant and envoy' compatibility among herbs increases the risk of toxicity or side effects, opposing the 'treatment based on syndrome differentiation' principle in clinical TCM. Therefore, we propose a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph diffusion guidance, namely TCM-HEDPR. Specifically, we pre-train symptom representations using patient-personalized prompt sequences and apply prompt-oriented contrastive learning for data augmentation. Furthermore, we employ a KG-guided homogeneous graph diffusion method integrated with a self-attention mechanism to globally capture the non-linear symptom-herb relationship. Lastly, we design a heterogeneous graph hierarchical network to integrate herbal dispensing relationships with implicit syndromes, guiding the prescription generation process at a fine-grained level and mitigating the long-tailed herb data distribution problem. Extensive experiments on two public datasets and one clinical dataset demonstrate the effectiveness of TCM-HEDPR. In addition, we incorporate insights from modern medicine and network pharmacology to evaluate the recommended prescriptions comprehensively. It can provide a new paradigm for the recommendation of modern TCM.

Heming Zhang, Di Huang, Wenyu Li et al.

In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets. Existing pipelines capture only part of these-numerical omics ignore topological context, text-centric LLMs lack quantitative grounded reasoning, and graph-only models underuse node semantics and the generalization of LLMs-limiting mechanistic interpretability. Although Process Reward Models (PRMs) aim to guide reasoning in LLMs, they remain limited by unreliable intermediate evaluation, and vulnerability to reward hacking with computational cost. These gaps motivate integrating quantitative multi-omic signals, topological structure with node annotations, and literature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context. Therefore, we propose GALAX (Graph Augmented LAnguage model with eXplainability), an innovative framework that integrates pretrained Graph Neural Networks (GNNs) into Large Language Models (LLMs) via reinforcement learning guided by a Graph Process Reward Model (GPRM), which generates disease-relevant subgraphs in a step-wise manner initiated by an LLM and iteratively evaluated by a pretrained GNN and schema-based rule check, enabling process-level supervision without explicit labels. As an application, we also introduced Target-QA, a benchmark combining CRISPR-identified targets, multi-omic profiles, and biomedical graph knowledge across diverse cancer cell lines, which enables GNN pretraining for supervising step-wise graph construction and supports long-context reasoning over text-numeric graphs (TNGs), providing a scalable and biologically grounded framework for explainable, reinforcement-guided subgraph reasoning toward reliable and interpretable target discovery in precision medicine.

Youngseung Jeon, Christopher Hwang, Ziwen Li et al.

While drug discovery is vital for human health, the process remains inefficient. Medicinal chemists must navigate a vast protein space to identify target proteins that meet three criteria: physical and functional interactions, therapeutic impact, and docking potential. Prior approaches have provided fragmented support for each criterion, limiting the generation of promising hypotheses for wet-lab experiments. We present HAPPIER, an AI-powered tool that supports hypothesis generation with integrated multi-criteria support for target identification. HAPPIER enables medicinal chemists to 1) efficiently explore and verify proteins in a single integrated graph component showing multi-criteria satisfaction and 2) validate AI suggestions with domain knowledge. These capabilities facilitate iterative cycles of divergent and convergent thinking, essential for hypothesis generation. We evaluated HAPPIER with ten medicinal chemists, finding that it increased the number of high-confidence hypotheses and support for the iterative cycle, and further demonstrated the relationship between engaging in such cycles and confidence in outputs.

Michael S. Yao, Osbert Bastani, Alma Andersson et al.

The goal of personalized medicine is to discover a treatment regimen that optimizes a patient's clinical outcome based on their personal genetic and environmental factors. However, candidate treatments cannot be arbitrarily administered to the patient to assess their efficacy; we often instead have access to an in silico surrogate model that approximates the true fitness of a proposed treatment. Unfortunately, such surrogate models have been shown to fail to generalize to previously unseen patient-treatment combinations. We hypothesize that domain-specific prior knowledge - such as medical textbooks and biomedical knowledge graphs - can provide a meaningful alternative signal of the fitness of proposed treatments. To this end, we introduce LLM-based Entropy-guided Optimization with kNowledgeable priors (LEON), a mathematically principled approach to leverage large language models (LLMs) as black-box optimizers without any task-specific fine-tuning, taking advantage of their ability to contextualize unstructured domain knowledge to propose personalized treatment plans in natural language. In practice, we implement LEON via 'optimization by prompting,' which uses LLMs as stochastic engines for proposing treatment designs. Experiments on real-world optimization tasks show LEON outperforms both traditional and LLM-based methods in proposing individualized treatments for patients.

Daniel Mas Montserrat, Ray Verma, Míriam Barrabés et al.

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resource utilization and long runtimes. In this work, we propose multiple mechanisms for adaptive, RAM-efficient parallelization of chromosome-level bioinformatics workflows. First, we develop a symbolic regression model that estimates per-chromosome memory consumption for a given task and introduces an interpolating bias to conservatively minimize over-allocation. Second, we present a dynamic scheduler that adaptively predicts RAM usage with a polynomial regression model, treating task packing as a Knapsack problem to optimally batch jobs based on predicted memory requirements. Additionally, we present a static scheduler that optimizes chromosome processing order to minimize peak memory while preserving throughput. Our proposed methods, evaluated on simulations and real-world genomic pipelines, provide new mechanisms to reduce memory overruns and balance load across threads. We thereby achieve faster end-to-end execution, showcasing the potential to optimize large-scale genomic workflows.

PLOS One Editors

A. V. Novozhilov, S. E. Grigoriev, O. Yu. Yakovleva et al.

Introduction. Kidney transplantation (KT) is often considered the best option for renal replacement therapy (RRT), significantly improving patient outcomes. Post-transplant, life expectancy doubles, and mortality decreases more than 4-fold compared to other RRT modalities. This article presents KT outcomes in Irkutsk Region from 2018 to 2023. All procedures were performed at a single center – the Irkutsk Regional Clinical Hospital.Objective: to analyze the immediate and long-term outcomes of KT in Irkutsk Region.Material and methods. A retrospective analysis was conducted on the treatment outcomes of 125 patients with kidney failure (KF). Among them, 74 were men with a median age of 42 (35–49) years, and 51 were women with a median age of 46 (37–55) years. The median transplant waitlist time was 15.5 (range: 6–32) months. The leading cause of KF was chronic glomerulonephritis, observed in 60 patients (48%). There were no HLA matches in 36 patients (28.8%), while 38 patients (30.4%) had one match. Arterial anastomosis was primarily performed end-to-end with the external iliac artery in 121 cases (96.8%), while in 3 cases (2.4%), the internal iliac artery was used due to external iliac artery spasm. Cold ischemia time was 222 minutes (range: 162–360), and warm ischemia time was 39 minutes (range: 30–46).Results. Length of hospital stay was 16 (range: 13–25) bed days. Primary renal function was achieved in 95 patients (77%), while 25 patients (20%) experienced delayed graft function. Blood tacrolimus reached target levels by postoperative days 9–12. Creatinine level at discharge was 120 μmol/L (range: 97–165). Surgical complications occurred in 24 patients (19.2%), while urinary tract infections were observed in 36 patients (28.8%), with 17 cases (13.6%) presenting clinical symptoms. Immunosuppressive therapy was initiated in 124 patients (99.2%) using a standard triple-drug regimen (calcineurin inhibitors, mycophenolates, and glucocorticoids). One patient (0.8%) succumbed to complications from COVID-19. One-year graft survival was 94.1%.Conclusion. The immediate outcomes align with national averages. There is a consistent upward trend in the number of kidney transplants performed. Further development of the regional transplant program will enhance access to this high-tech medical service, meeting the needs of the local population.

Ping Yu, Kaitao Song, Fengchen He et al.

The recently unprecedented advancements in Large Language Models (LLMs) have propelled the medical community by establishing advanced medical-domain models. However, due to the limited collection of medical datasets, there are only a few comprehensive benchmarks available to gauge progress in this area. In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. Specifically, our TCMD collects massive questions across diverse domains with their annotated medical subjects and thus supports us in comprehensively assessing the capability of LLMs in the TCM domain. Extensive evaluation of various general LLMs and medical-domain-specific LLMs is conducted. Moreover, we also analyze the robustness of current LLMs in solving TCM QA tasks by introducing randomness. The inconsistency of the experimental results also reveals the shortcomings of current LLMs in solving QA tasks. We also expect that our dataset can further facilitate the development of LLMs in the TCM area.

Anh Le, Amirreza Hashemi, Mark P. Ottensmeyer et al.

The design of nuclear imaging scanners is crucial for optimizing detection and imaging processes. While advancements have been made in simplistic, symmetrical modalities, current research is progressing towards more intricate structures, however, the widespread adoption of computer-aided design (CAD) tools for modeling and simulation is still limited. This paper introduces FreeCAD and the GDML Workbench as essential tools for designing and testing complex geometries in nuclear imaging modalities. FreeCAD is a parametric 3D CAD modeler, and GDML is an XML-based language for describing complex geometries in simulations. Their integration streamlines the design and simulation of nuclear medicine scanners, including PET and SPECT scanners. The paper demonstrates their application in creating calibration phantoms and conducting simulations with Geant4, showcasing their precision and versatility in generating sophisticated components for nuclear imaging. The integration of these tools is expected to streamline design processes, enhance efficiency, and facilitate widespread application in the nuclear imaging field.

Lingxiao Luo, Bingda Tang, Xuanzhong Chen et al.

Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks demand more versatile approaches. Additionally, while most VLMs process only 2D images, a large portion of medical images are 3D. The lack of medical data further compounds these obstacles. To address these challenges, we present VividMed, a vision language model with versatile visual grounding for medicine. Our model supports generating both semantic segmentation masks and instance-level bounding boxes, and accommodates various imaging modalities, including both 2D and 3D data. We design a three-stage training procedure and an automatic data synthesis pipeline based on open datasets and models. Besides visual grounding tasks, VividMed also excels in other common downstream tasks, including Visual Question Answering (VQA) and report generation. Ablation studies empirically show that the integration of visual grounding ability leads to improved performance on these tasks. Our code is publicly available at https://github.com/function2-llx/MMMM.

Jun-Young Oh, In-Gyu Lee, Tae-Eui Kam et al.

In the cutting-edge domain of medical artificial intelligence (AI), remarkable advances have been achieved in areas such as diagnosis, prediction, and therapeutic interventions. Despite these advances, the technology for image segmentation faces the significant barrier of having to produce extensively annotated datasets. To address this challenge, few-shot segmentation (FSS) has been recognized as one of the innovative solutions. Although most of the FSS research has focused on human health care, its application in veterinary medicine, particularly for pet care, remains largely limited. This study has focused on accurate segmentation of the heart and left atrial enlargement on canine chest radiographs using the proposed deep prototype alignment network (DPANet). The PANet architecture is adopted as the backbone model, and experiments are conducted using various encoders based on VGG-19, ResNet-18, and ResNet-50 to extract features. Experimental results demonstrate that the proposed DPANet achieves the highest performance. In the 2way-1shot scenario, it achieves the highest intersection over union (IoU) value of 0.6966, and in the 2way-5shot scenario, it achieves the highest IoU value of 0.797. The DPANet not only signifies a performance improvement, but also shows an improved training speed in the 2way-5shot scenario. These results highlight our model's exceptional capability as a trailblazing solution for segmenting the heart and left atrial enlargement in veterinary applications through FSS, setting a new benchmark in veterinary AI research, and demonstrating its superior potential to veterinary medicine advances.

Michael Vollenweider, Manuel Schürch, Chiara Rohrer et al.

Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it faces significant challenges due to complex biases in clinical observational data and the high-dimensional nature of biological data. This study models various types of treatment assignment biases using mutual information and investigates their impact on ML models for counterfactual prediction and biomarker identification. Unlike traditional counterfactual benchmarks that rely on fixed treatment policies, our work focuses on modeling different characteristics of the underlying observational treatment policy in distinct clinical settings. We validate our approach through experiments on toy datasets, semi-synthetic tumor cancer genome atlas (TCGA) data, and real-world biological outcomes from drug and CRISPR screens. By incorporating empirical biological mechanisms, we create a more realistic benchmark that reflects the complexities of real-world data. Our analysis reveals that different biases lead to varying model performances, with some biases, especially those unrelated to outcome mechanisms, having minimal effect on prediction accuracy. This highlights the crucial need to account for specific biases in clinical observational data in counterfactual ML model development, ultimately enhancing the personalization of treatment decisions in precision medicine.

Wenwen Zhang, Huimin Tang, Qiucheng Jia et al.

Background: To evaluate the effect of carbohydrate antigen 125 (CA125) and CA19-9 in distinguishing stage Ⅲ and Ⅳ endometriosis from benign and malignant tumors, and to explore whether it is related to the clinical features of the disease. Methods: In a retrospective cohort study based on clinical data from hospitals, a total of 183 patients with pathologically confirmed diagnosis of ovarian endometriotic cysts (OEC) in Hainan Provincial People’s Hospital for surgical treatment from January 2019 to August 2022 were selected as the case group, and a total of 276 cases of benign diseases, including 184 cases of benign ovarian tumors, 94 cases of gynecological common diseases, and 102 cases of malignant ovarian tumors were selected as the control group, with a total of 276 cases of benign diseases, including 184 cases of benign ovarian tumors, 94 cases of gynecological common diseases, and 102 cases of malignant ovarian tumors. There were also 23 cases of ruptured ectopic cysts. We compared the clinical characteristics (age of onset, fertility, dysmenorrhea, preoperative CA125 and CA19-9 values) of the patients in the OEC group with those of the other control groups; analyzed the serum CA125 and CA19-9 values in relation to the pathological characteristics of OEC (recurrence, unilateral and bilaterality, multilocularity and unilocularity, rupture, dysmenorrhea, fertility, and staging); and analyzed the CA125 and CA19-9 values by unordered logistic regression, CA19-9 to predict OEC; sensitivity, specificity and cut-off values of CA125, CA19-9 and their combined indexes to diagnose OEC. Results: The symptoms of dysmenorrhea and infertility in OEC group were significantly higher than those in the other three groups. The preoperative CA125 value in OEC group was higher than that in benign tumor and other gynecological diseases group, and significantly lower than that in malignant tumor group. There was no significant difference in the value of CA19-9 and CA125 in the degree of dysmenorrhea, recurrence and infertility. The values of CA19-9 and CA125 of multilocular cysts were higher than those of unicameral cysts, bilateral cysts were higher than unilateral cysts, and ruptured cysts were significantly higher than unruptured cysts. The value of CA125 in the dysmenorrhea group was higher than that in the non-dysmenorrhea group, and that in the fourth stage was higher than that in the third stage, and the difference was statistically significant (p < 0.05). Unordered multicategorical logistic regression analysis determined that CA125, could be a predictor in the comparison of OEC with benign disease; in the benign control group the cut-off value for CA125 was >23.1 IU/mL with an area under the curve (AUC) value of 0.90 (0.869–0.926), a sensitivity of 89.62% and a specificity of 81.52%. In the malignant control group the cut-off value for CA125 was ≤209.2 with an AUC value of 0.859 (0.813–0.897), sensitivity 95.08% and specificity 71.57%. Conclusions: The effect of serum CA19-9 in the diagnosis of Endometriosis (EMT) is not ideal. CA125 has a certain value in the diagnosis of endometriosis, but it is necessary to explore the range of cut-off value.

O.O. Khaustova , A.E. Asanova, N.O. Dzeruzhynska et al.

Determining the optimal antipsychotic drugs, its effective dose, duration of therapy, form, and route of administration play a key role in the treatment of schizophrenia. In addition, special attention should be paid to the effectiveness of using different forms of antipsychotic drugs, in particular, the orally disintegrating form as exemplified by olanzapine. To study the peculiarities of a personalized approach in the use of antipsychotic drugs to achieve more effective results in treating schizophrenia, a content analysis was conducted using Ukrainian and English-language publications for the past 15 years. The search was conducted using the PubMed and CrossRef databases. An important conclusion is that the optimal therapeutic formula or drug should be selected individually, considering the specific patient's clinical condition. However, the most important factor in achieving successful results is the individually selected form and dose of the antipsychotic drugs. In addition to the patient's mental state, the choice of antipsychotic therapy is influenced by the spectrum of side effects, individual sensitivity to the active substance, pharmacological history, economic factors, etc. This is especially true for patients with insufficient adherence to treatment, which can often arise due to the side effects of drugs. In this case, it is important to correctly select both the active substance and the appropriate route of administration. Personalized selection of antipsychotic drugs also involves dynamic monitoring of changes in the patient's clinical condition, allowing for timely diagnosis of drug side effects, dose adjustments, or changes in the route of administration. These measures help increase patient adherence to treatment and improve their health-related quality of life.

Harold D. Green PhD, Patrick C. Kaminski MA

Opioid overdose and Opioid Use Disorder (OUD) statistics underscore an urgent need to significantly expand access to evidence-based OUD treatment. Office Based Opioid Treatment (OBOT) has proven effective for treating OUD. However, limited access to these treatments persists. Recognizing the need for significant investment in clinical, behavioral, and translational research, the Indiana State Department of Health and Indiana University embarked on a research initiative supported by the “Responding to the Addictions Crisis” Grand Challenge Program. This brief presents recommendations based on existing research and our own analyses of medical claims data in Indiana, where opioid misuse is high and treatment access is limited. The recommendations cover target providers, intervention focus, priority regions, and delivery methods.

Deepak R , Seema Shende , Namrata Jain et al.

Background: Cisatracurium and rocuronium are non-depolarizing neuromuscular blockers with an intermediate duration of action and are used safely in short and intermediate-duration surgical procedures in the pediatric population. Aims and Objectives: A prospective randomized study is to assess the efficacy of cisatracurium compared to rocuronium in terms of intubating conditions, clinical duration of action, hemodynamic parameters, and side effects in pediatric patients undergoing surgeries under general anesthesia. Materials and Methods: In this study, 50 patients aged 2–12 years with the American Society of Anesthesiologists grades I and II were randomly allocated into two groups: Group I received injection cisatracurium 0.15 mg/kg IV and Group II received injection rocuronium 0.6 mg/kg IV for intubation. Intubating conditions by Cooper et al., score, TOF count, hemodynamic parameters, signs of histamine release, and complications if any were noted. Results: According to the Cooper et al., score, intubating conditions were excellent in 100% of patients in Group II and 84% of patients in Group I, which was statistically significant. The time required for the first maintenance dose was shorter in Group II (14.04±2.95 min) compared to Group I (20.08±3.68 min). Hemodynamic parameters and demographic profiles were comparable between the two groups. No associated signs of histamine release or any other complications were noted in either group. Conclusion: We concluded that rocuronium 0.6 mg/kg provides better intubating conditions and a shorter duration of action compared to cisatracurium 0.15 mg/kg without any signs of histamine release in pediatric patients.

Tatsuya Yamazaki, Kenta Iwasaki, Susumu Tomono et al.

Summary: Detecting antibodies, particularly those targeting donor human leukocyte antigens in organ transplantation and self-antigens in autoimmune diseases, is crucial for diagnosis and therapy. Radioprotective 105 (RP105), a Toll-like receptor family protein, is expressed in immune-competent cells, such as B cells. Studies in mice have shown that the anti-mouse RP105 antibody strongly activates B cells and triggers an adjuvant effect against viral infections. However, the anti-human RP105 antibody (ɑhRP105) weakly activates human B cells. This study established new culture conditions under, which human B cells are strongly activated by the ɑhRP105. When combined with CpGDNA, specific antibody production against blood group carbohydrates, ɑGal, and SARS-CoV-2 was successfully detected in human B cell cultures. Furthermore, comprehensive analysis using liquid chromatography-electrospray ionization tandem mass spectrometry, single-cell RNA sequencing, and quantitative real-time PCR revealed that ɑhRP105 triggered a different activation stimulus compared to CpGDNA. These findings could help identify antibody-producing B cells in cases of transplant rejection and autoimmune diseases.