Hasil untuk "Pathology"

Haoyang Su, Shaoting Zhang, Xiaosong Wang

The emergence of tool-calling-based agent systems introduces a more evidence-driven paradigm for pathology image analysis in contrast to the coarse-grained text-image diagnostic approaches. With the recent large-scale experimental adoption of spatial transcriptomics technologies, molecularly validated pathological diagnosis is becoming increasingly open and accessible. In this work, we propose LAMMI-Pathology (LVLM-Agent System for Molecularly Informed Medical Intelligence in Pathology), a scalable agent framework for domain-specific agent tool-calling. LAMMI-Pathology adopts a tool-centric, bottom-up architecture in which customized domain-adaptive tools serve as the foundation. These tools are clustered by domain style to form component agents, which are then coordinated through a top-level planner hierarchically, avoiding excessively long context lengths that could induce task drift. Based on that, we introduce a novel trajectory construction mechanism based on Atomic Execution Nodes (AENs), which serve as reliable and composable units for building semi-simulated reasoning trajectories that capture credible agent-tool interactions. Building on this foundation, we develop a trajectory-aware fine-tuning strategy that aligns the planner's decision-making process with these multi-step reasoning trajectories, thereby enhancing inference robustness in pathology understanding and its adaptive use of the customized toolset.

Jiangdong Cai, Yan Chen, Zhenrong Shen et al.

In digital pathology, acquiring all-in-focus images is essential to high-quality imaging and high-efficient clinical workflow. Traditional scanners achieve this by scanning at multiple focal planes of varying depths and then merging them, which is relatively slow and often struggles with complex tissue defocus. Recent prevailing image restoration technique provides a means to restore high-quality pathology images from scans of single focal planes. However, existing image restoration methods are inadequate, due to intricate defocus patterns in pathology images and their domain-specific semantic complexities. In this work, we devise a two-stage restoration solution cascading a transformer and a diffusion model, to benefit from their powers in preserving image fidelity and perceptual quality, respectively. We particularly propose a novel mixture of prompts for the two-stage solution. Given initial prompt that models defocus in microscopic imaging, we design two prompts that describe the high-level image semantics from pathology foundation model and the fine-grained tissue structures via edge extraction. We demonstrate that, by feeding the prompt mixture to our method, we can restore high-quality pathology images from single-focal-plane scans, implying high potentials of the mixture of prompts to clinical usage. Code will be publicly available at https://github.com/caijd2000/MoP.

Yating Huang, Ziyan Huang, Lintao Xiang et al.

Accurate analysis of pathological images is essential for automated tumor diagnosis but remains challenging due to high structural similarity and subtle morphological variations in tissue images. Current vision-language (VL) models often struggle to capture the complex reasoning required for interpreting structured pathological reports. To address these limitations, we propose PathoHR-Bench, a novel benchmark designed to evaluate VL models' abilities in hierarchical semantic understanding and compositional reasoning within the pathology domain. Results of this benchmark reveal that existing VL models fail to effectively model intricate cross-modal relationships, hence limiting their applicability in clinical setting. To overcome this, we further introduce a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning. Experimental evaluations demonstrate that our approach achieves state-of-the-art performance on PathoHR-Bench and six additional pathology datasets, highlighting its effectiveness in fine-grained pathology representation.

Jiamu Wang, Chang-Su Kim, Jin Tae Kwak

Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the development of machine learning and deep learning has greatly bolstered the power of computational pathology. However, there still remains the issue of data scarcity and data imbalance, which can have an adversarial effect on any computational method. In this paper, we introduce an efficient and effective data augmentation strategy to generate new pathology images from the existing pathology images and thus enrich datasets without additional data collection or annotation costs. To evaluate the proposed method, we employed two sets of colorectal cancer datasets and obtained improved classification results, suggesting that the proposed simple approach holds the potential for alleviating the data scarcity and imbalance in computational pathology.

Zunjie Xiao, Xiaoqing Zhang, Risa Higashita et al.

Pathology context and expert experience play significant roles in clinical ocular disease diagnosis. Although deep neural networks (DNNs) have good ocular disease recognition results, they often ignore exploring the clinical pathology context and expert experience priors to improve ocular disease recognition performance and decision-making interpretability. To this end, we first develop a novel Pathology Recalibration Module (PRM) to leverage the potential of pathology context prior via the combination of the well-designed pixel-wise context compression operator and pathology distribution concentration operator; then this paper applies a novel expert prior Guidance Adapter (EPGA) to further highlight significant pixel-wise representation regions by fully mining the expert experience prior. By incorporating PRM and EPGA into the modern DNN, the PCRNet is constructed for automated ocular disease recognition. Additionally, we introduce an Integrated Loss (IL) to boost the ocular disease recognition performance of PCRNet by considering the effects of sample-wise loss distributions and training label frequencies. The extensive experiments on three ocular disease datasets demonstrate the superiority of PCRNet with IL over state-of-the-art attention-based networks and advanced loss methods. Further visualization analysis explains the inherent behavior of PRM and EPGA that affects the decision-making process of DNNs.

Mikhail Goncharov, Eugenia Soboleva, Mariia Donskova et al.

Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentation (UVAS) problem, leveraging the inherent rarity of pathological patterns compared to healthy ones. We enhance the existing density-based UVAS framework with two key innovations: (1) dense self-supervised learning for feature extraction, eliminating the need for supervised pretraining, and (2) learned, masking-invariant dense features as conditioning variables, replacing hand-crafted positional encodings. Trained on over 30,000 unlabeled 3D CT volumes, our fully self-supervised model, Screener, outperforms existing UVAS methods on four large-scale test datasets comprising 1,820 scans with diverse pathologies. Furthermore, in a supervised fine-tuning setting, Screener surpasses existing self-supervised pretraining methods, establishing it as a state-of-the-art foundation for pathology segmentation. The code and pretrained models will be made publicly available.

Ahmed Morad Asaad, Sara A. Saied, Mohammad M. Torayah et al.

Abstract Background Recent advances in nanomedicine have derived novel prospects for development of various bioactive nanoparticles and nanocomposites with significant antibacterial and antifungal properties. This study aims to investigate some characteristics of the novel Se-NPs/Cu2O nanocomposite such as morphological, physicochemical, and optical properties, as well as to assess the antibacterial activity of this fabricated composite in different concentrations against some MDR Gram-positive and Gram-negative clinical bacterial isolates. Methods The Se-NPs/Cu2O nanocomposite was fabricated using the chemical deposition method. The fabricated nanocomposite was fully characterized by X-Ray diffraction analysis (XRD), fourier transforms infrared spectroscopy (FTIR), and transmission electron microscope (TEM). The antimicrobial activity of Se-NPs/Cu2O was investigated using the standard broth microdilution method. The fabricated Se-NPs/Cu2O nanocomposites were detected as stable and highly crystallized nanospheres with an average size of 98.6 nm. Results The Se-NPs/Cu2O nanocomposite showed a potent antimicrobial activity with MIC values ranged from 6.25 to 12.5 µg/ml for Gram-positive isolates, and 25 to 50 µg/ml for gram-negative isolates. The bactericidal activity was higher for gram-negative isolates with MBC/MIC ratios of 1–2 µg/ml for gram-negative, versus 8 µg/ml for gram positive pathogens. Conclusion These findings would support further research in development of a novel Se-NPs/Cu2O nanocomposite as a promising alternative therapeutic option for improving the quality of patients’ management.

Svyatoslav M. Yurin, Dmitry A. Apalkov, Tatiana A. Minenkova et al.

Background. Congenital disorders of sex development (DSD) represent a heterogeneous group of dysontogenetic conditions characterized by a discordance between chromosomal, gonadal, and phenotypic sex. Among them, idiopathic forms of male pseudohermaphroditism with a 46,XY karyotype are among the most diagnostically challenging variants, as the presence of testicular tissue is accompanied by incomplete masculinization of the external genitalia in the absence of detectable mutations within the androgen-regulatory gene system. Given the clinico-endocrinological ambiguity of this pathology, an integrative diagnostic strategy combining hormonal, cytogenetic, and morphofunctional assessments, along with the timely determination of optimal timing for surgical and hormonal correction, acquires particular clinical importance. Objective. To perform a detailed clinico-endocrinological characterization of the idiopathic variant of DSD in a prepubertal child with a 46,XY karyotype and to determine the principles of rational diagnostic and therapeutic management. Materials and methods. The study is based on the clinical observation of a 9-year-old boy examined in the Endocrinology Department of the Kursk Regional Children’s Clinical Hospital. The analysis included medical history, physical status, serum levels of LH, FSH, testosterone, and anti-Müllerian hormone, cytogenetic data, and ultrasonographic features of the gonads and pelvic organs, correlated with up-to-date literature sources. Conclusions. Idiopathic forms of male pseudohermaphroditism with a normal male karyotype require a multidisciplinary approach and prolonged follow-up. Early surgical correction and subsequent hormonal monitoring contribute to the formation of an adequate phenotypic outcome, reduction of endocrine complications, and improvement of psychosocial adaptation during puberty.

Jiawen Li, Tian Guan, Qingxin Xia et al.

Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the dissection of cancer-specific signals. However, these static general features constrain the flexibility and pathological relevance in the ever-evolving needs of clinical applications, hindering the broad use of the current models. Here we introduce PathFiT, a dynamic feature learning method that can be effortlessly plugged into various pathology foundation models to unlock their adaptability. Meanwhile, PathFiT performs seamless implementation across diverse pathology applications regardless of downstream specificity. To validate PathFiT, we construct a digital pathology benchmark with over 20 terabytes of Internet and real-world data comprising 28 H\&E-stained tasks and 7 specialized imaging tasks including Masson's Trichrome staining and immunofluorescence images. By applying PathFiT to the representative pathology foundation models, we demonstrate state-of-the-art performance on 34 out of 35 tasks, with significant improvements on 23 tasks and outperforming by 10.20% on specialized imaging tasks. The superior performance and versatility of PathFiT open up new avenues in computational pathology.

Jeaung Lee, Jeewoo Lim, Keunho Byeon et al.

In computational pathology, several foundation models have recently emerged and demonstrated enhanced learning capability for analyzing pathology images. However, adapting these models to various downstream tasks remains challenging, particularly when faced with datasets from different sources and acquisition conditions, as well as limited data availability. In this study, we benchmark four pathology-specific foundation models across 14 datasets and two scenarios-consistency assessment and flexibility assessment-addressing diverse adaptation scenarios and downstream tasks. In the consistency assessment scenario, involving five fine-tuning methods, we found that the parameter-efficient fine-tuning approach was both efficient and effective for adapting pathology-specific foundation models to diverse datasets within the same downstream task. In the flexibility assessment scenario under data-limited environments, utilizing five few-shot learning methods, we observed that the foundation models benefited more from the few-shot learning methods that involve modification during the testing phase only. These findings provide insights that could guide the deployment of pathology-specific foundation models in real clinical settings, potentially improving the accuracy and reliability of pathology image analysis. The code for this study is available at: https://github.com/QuIIL/BenchmarkingPathologyFoundationModels.

Peirong Liu, Oula Puonti, Annabel Sorby-Adams et al.

Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acquisitions. This limits their applicability to diverse real-world clinical data, where scans commonly exhibit variations in appearances due to being obtained with varying sequence parameters, resolutions, and orientations -- especially in the presence of pathology. In this paper, we propose PEPSI, the first pathology-enhanced, and pulse-sequence-invariant feature representation learning model for brain MRI. PEPSI is trained entirely on synthetic images with a novel pathology encoding strategy, and enables co-training across datasets with diverse pathologies and missing modalities. Despite variations in pathology appearances across different MR pulse sequences or the quality of acquired images (e.g., resolution, orientation, artifacts, etc), PEPSI produces a high-resolution image of reference contrast (MP-RAGE) that captures anatomy, along with an image specifically highlighting the pathology. Our experiments demonstrate PEPSI's remarkable capability for image synthesis compared with the state-of-the-art, contrast-agnostic synthesis models, as it accurately reconstructs anatomical structures while differentiating between pathology and normal tissue. We further illustrate the efficiency and effectiveness of PEPSI features for downstream pathology segmentations on five public datasets covering white matter hyperintensities and stroke lesions. Code is available at https://github.com/peirong26/PEPSI.

Petter Gottschalk

The theory of convenience addresses white-collar and corporate crime. The theory is applied in this article to a case study of Norwegian publishing houses having to pay infringement fees because of competition act violation. Cartel members agreed and coordinated a boycott of a distribution channel. This article reviews the research literature on cartels before presenting the convenience case study. Combatting cartels is a matter of reducing the attractiveness and convenience of joining cartels. Guardianship, oversight, and controls are at the core of reducing deviance convenience. Detection is an element of oversight. However, detection is rare, as this case illustrated by email sent by mistake. Combatting cartels is a matter of control at the top of organizations where typically each chief executive officer (CEO) is involved. Therefore, the corporate compliance officer should never report to the CEO but rather to the chairperson on the board and to the external auditor.

Rukhsana Tumrani, S. Sabahat Haider, Mehvish Sana

Background: Serum electrolytes are one of the most frequently requested tests in patients from critical care settings. Methodsfor estimation of serum electrolytes include direct and indirect ion selective electrode (ISE). In case of indirect ISE, the pre-analytical dilution step can result in pseudonormonatremia or pseudohypernatremia in setting of hypoalbuminemia. Discrepancy in sodium results can lead to the misdiagnosis and mismanagement of critically ill patients. Objective: To evaluate the difference of mean serum sodium level measured by direct and indirect ISE in critically ill patients with hypoalbuminemia. Study type,settings & duration: This cross-sectional study conducted in Department of Chemical Pathology, Sheikh Zayed Hospital,Rahim Yar Khan from February to August 2022. Methodology: A total 408 study subjects aged between 20 to 60 years admitted in intensive care units of Sheikh Zayed hospital Rahim yar khan with serum albumin level <3.5g/dl were included in study. Serum sodium level was estimated concurrently by both methods for all study subjects satisfying the inclusion criteria. Mean difference of serum sodium level measured by both methods was evaluated to see the statistically significant difference between two methods. Results: Mean difference of serum sodium level measured by indirect and direct ISE (Indirect ISE-direct ISE) was 4.216±13.571mmol/L withstatistically significant difference (pvalue: 0.000). The difference was not acceptable according to CLIA (Clinical Laboratory Improvement Amendments) requirements for acceptable performance between the methods. Effect of triglyceride was statistically significant for mean difference of serum sodium level between two methods (pvalue: 0.026). No statistically significant difference of mean difference of serum sodium level with respect to other variables was found. Conclusion: Interchangeable use of directand indirect ISE should be avoided in the setting of hypoalbuminemia. Indirect ion selective electrode results of serum sodium level are misleading in the setting of hypoalbuminemia and critically ill patients can be misdiagnosed and mismanaged if sodium level is measured by indirect ion selective electrode. Standardization should be considered by hospital laboratories to use direct ISE for serum sodium measurement.

Daniella Tosta Link, Gustavo Guimarães Fernandes Viana, Lívia Pasolini Siqueira et al.

The consumption of seafood is crucial for food security, but poor hygiene along the food production chain can result in low microbiological quality, posing significant risks for public health and seafood quality. Thus, this study aimed to assess the microbiological quality and antimicrobial sensitivity of <i>E. coli</i> from 69 samples of illegally marketed shrimp and mussels in the Vitória Region, Brazil. These foods exhibited poor microbiological quality due to high counts of mesophilic, psychrotrophic, and enterobacteria microorganisms. While this issue is widespread in this area, shrimp samples displayed higher microbial counts compared to mussels, and fresh mussels had elevated counts of enterobacteria compared to frozen ones. Among the 10 <i>E. coli</i> isolates, none carried the genes <i>blaCTX-M-1</i>, <i>blaCTX-M-2</i>, <i>blaCTX-M-3</i>, <i>blaCTX-M-15</i>, <i>mcr-1</i>, <i>mcr-2</i>, <i>mcr-3</i>, <i>mcr-4</i>, and <i>tet</i>, associated with antibiotic resistance. Phenotypical resistance to tetracycline and fosfomycin was not observed in any isolate, while only 20% demonstrated resistance to ciprofloxacin. Regarding ampicillin and amoxicillin with clavulanic acid, 60% of isolates were resistant, 10% showed intermediate susceptibility, and 30% were sensitive. One isolate was considered simultaneously resistant to β-lactams and quinolones, and none were conserved as ESBL producers. These findings highlight the inherent risks to local public health that arise from consuming improperly prepared seafood in this area.

Pingyi Chen, Honglin Li, Chenglu Zhu et al.

Whole slide images are the foundation of digital pathology for the diagnosis and treatment of carcinomas. Writing pathology reports is laborious and error-prone for inexperienced pathologists. To reduce the workload and improve clinical automation, we investigate how to generate pathology reports given whole slide images. On the data end, we curated the largest WSI-text dataset (PathText). In specific, we collected nearly 10000 high-quality WSI-text pairs for visual-language models by recognizing and cleaning pathology reports which narrate diagnostic slides in TCGA. On the model end, we propose the multiple instance generative model (MI-Gen) which can produce pathology reports for gigapixel WSIs. We benchmark our model on the largest subset of TCGA-PathoText. Experimental results show our model can generate pathology reports which contain multiple clinical clues and achieve competitive performance on certain slide-level tasks. We observe that simple semantic extraction from the pathology reports can achieve the best performance (0.838 of F1 score) on BRCA subtyping surpassing previous state-of-the-art approaches. Our collected dataset and related code are available.

Jaeung Lee, Keunho Byeon, Jin Tae Kwak

Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potential for improving the accuracy and quality of cancer diagnosis. However, the issues with the robustness and reliability of such methods have not been fully resolved yet. Herein, we propose a centroid-aware feature recalibration network that can conduct cancer grading in an accurate and robust manner. The proposed network maps an input pathology image into an embedding space and adjusts it by using centroids embedding vectors of different cancer grades via attention mechanism. Equipped with the recalibrated embedding vector, the proposed network classifiers the input pathology image into a pertinent class label, i.e., cancer grade. We evaluate the proposed network using colorectal cancer datasets that were collected under different environments. The experimental results confirm that the proposed network is able to conduct cancer grading in pathology images with high accuracy regardless of the environmental changes in the datasets.

Abbirami Balachandran, Sy Bing Choi, Morak-Młodawska Beata et al.

1. Diabetic chronic wounds, mainly foot ulcers, constitute one of the most common complications of poorly managed diabetes mellitus. The most typical reasons are insufficient glycemic management, latent neuropathy, peripheral vascular disease, and neglected foot care. In addition, it is a common cause of foot osteomyelitis and amputation of the lower extremities. Patients are admitted in larger numbers attributable to chronic wounds compared to any other diabetic disease. In the United States, diabetes is currently the most common cause of non-traumatic amputations. Approximately five percent of diabetics develop foot ulcers, and one percent require amputation. Therefore, it is necessary to identify sources of lead with wound-healing properties. Redox imbalance due to excessive oxidative stress is one of the causes for the development of diabetic wounds. Antioxidants have been shown to decrease the progression of diabetic neuropathy by scavenging ROS, regenerating endogenous and exogenous antioxidants, and reversing redox imbalance. Matrix metalloproteinases (MMPs) play vital roles in numerous phases of the wound healing process. Antioxidant and fibroblast cell migration activity of <i>Marantodes pumilum</i> (MP) crude extract has previously been reported. Through their antioxidant, epithelialization, collagen synthesis, and fibroblast migration activities, the authors hypothesise that naringin, eicosane and octacosane identified in the MP extract may have wound-healing properties. 2. The present study aims to identify the bioactive components present in the dichloromethane (DCM) extract of <i>M. pumilum</i> and evaluate their antioxidant and wound healing activity. Bioactive components were identified using LCMS, HPTLC and GCMS. Excision wound on STZ-induced diabetic rat model, human dermal fibroblast (HDF) cell line and colorimetric antioxidant assays were used to evaluate wound healing and antioxidant activities, respectively. Molecular docking and pkCMS software would be utilised to predict binding energy and affinity, as well as ADME parameters. 3. Naringin (NAR), eicosane (EIC), and octacosane (OCT) present in MP displayed antioxidant action and wound excision closure. Histological examination HDF cell line demonstrates epithelialization, collagen production, fibroblast migration, polymorphonuclear leukocyte migration (PNML), and fibroblast movement. The results of molecular docking indicate a substantial attraction and contact between MMPs. pkCMS prediction indicates inadequate blood-brain barrier permeability, low toxicity, and absence of hepatotoxicity. 4. Wound healing properties of (NEO) naringin, eicosane and octacosane may be the result of their antioxidant properties and possible interactions with MMP.

Consolato M. Sergi, Consolato M. Sergi, Josef Hager

Demi Brownlie, Andreas von Kries, Giampiero Valenzano et al.

Lung cancer is a leading cause of cancer-related death worldwide. Despite recent advances in tissue immunology, little is known about the spatial distribution of tissue-resident lymphocyte subsets in lung tumors. Using high-parameter flow cytometry, we identified an accumulation of tissue-resident lymphocytes including tissue-resident NK (trNK) cells and CD8+ tissue-resident memory T (TRM) cells toward the center of human non-small cell lung carcinomas (NSCLC). Chemokine receptor expression patterns indicated different modes of tumor-infiltration and/or residency between trNK cells and CD8+ TRM cells. In contrast to CD8+ TRM cells, trNK cells and ILCs generally expressed low levels of immune checkpoint receptors independent of location in the tumor. Additionally, granzyme expression in trNK cells and CD8+ TRM cells was highest in the tumor center, and intratumoral CD49a+CD16− NK cells were functional and responded stronger to target cell stimulation than their CD49a− counterparts, indicating functional relevance of trNK cells in lung tumors.In summary, the present spatial mapping of lymphocyte subsets in human NSCLC provides novel insights into the composition and functionality of tissue-resident immune cells, suggesting a role for trNK cells and CD8+ TRM cells in lung tumors and their potential relevance for future therapeutic approaches.

Jorge Martínez, David Meza-Alcántara, Carlos Uzcátegui

We address some phenomena about the interaction between lower semicontinuous submeasures on $\mathbb{N}$ and $F_σ$ ideals. We analyze the pathology degree of a submeasure and present a method to construct pathological $F_σ$ ideals. We give a partial answers to the question of whether every nonpathological tall $F_σ$ ideal is Katětov above the random ideal or at least has a Borel selector. Finally, we show a representation of nonpathological $F_σ$ ideals using sequences in Banach spaces.