Medical Practice Aboard Merchant Ships: Its Present Limitations and Their Correction

Abstract Not Provided

On the Pathology of Hodgkin's Disease

In Part I an attempt was made to offer a brief and reasonably critical account of the inherent peculiarities of the structure and of the diseases of the reticulo---endothelial system. Particular emphasis was accorded to works which have served to integrate the concept of lymphoid tissue sarcoma. While it is doubtful whether knowledge is yet adequate enough to endorse this idea universally, it has the advantage of being a practical generalisation which simplifies the subject. The view that the morbid process, primary reticulosis, was covered by the generic lymphoid tissue sarcoma was also supported. In Part II some account was given of the lymphatics and lymphoid tissue. In this outline attention was drawn to the mysterious and bewildering problems inseparable from the system. The structure of lymph nodes was given with observations on their development, involution, and possible neogenesis in adult life. From these studies it emerged that the full complement of lymph nodes in the locus examined was probably attained in adolescence or early adult life, and that fat replacement was the usual mode of atrophy. Attention was also drawn to the rarity of fibrosis in physiological nodes, except where it was the result of blood vascular hyaline change. In Part III Hodgkin's disease was studied. In the introduction of this part of the work the historical aspect of the malady was recorded, with, it is hoped, advertisement of interesting and possibly less well-known facts about it. This was followed by a critical consideration on the nature of the disease and its morbid anatomy, the latter being /illustrated illustrated in part by analyses of the cases coming to necropsy at Glasgow Royal Infirmary over a period of fifty years. In this part also were the findings of a large series of biopsy specimens. Here endeavours were made to shew the microscopical variations in morphology in the lesion, and to demonstrate the affinities of other lymphoid tissue sarcomata with the disease. Within the resources available the generic lymphoid tissue sarcoma was established, and links between the better recognised variants were presented with a reasonable degree of conviction. In the necropsy series a detailed study of thirteen cases of Hodgkin's disease or reticulum cell sarcoma was offered. In these it was shewn that the favourite locus was lymphoid tissue, that complete systematisation was rare, and that metamorphosis to a more tumour-like lesion was common. In Part IV two components of the Hodgkin's disease complex were studied in relation to general pathology. The view that fibrosis, an essential and inherent peculiarity of the Hodgkin's disease lesion, was represented in certain other morbid states was submitted. This was illustrated by brief accounts of some diseases where quasi-neoplastic features are shewn by connective tissue. Eosinophilia in tumours was also made the subject of investigation and revealed that the phenomenon, while possibly not so rare as might be expected, was not nearly so common as in Hodgkin's disease. Some evidence was found for the cyto-metaplastic origin of eosinophiles in Hodgkin's disease, but possibly due to the restriction to histological as opposed to cytological methods, the results were not highly conclusive. In Part V an experimental attempt to reproduce the disease by /chronic chronic trypan blue poisoning of rats and mice proved unsuccessful, although interesting results followed. The main contention in this thesis has been that Hodgkin's disease is a neoplasm. Perhaps the following may influence the reader more convincingly than I have been able to do by so much work. The reasons for human beliefs depend chiefly upon Authority, Intuition, and Scientific Method. The last two have been exploited as far as I have been able; the foremost remains. As a junior student I saw a case of Hodgkin's disease first in the wards of the Late Professor Archibald Harrington, at Glasgow Royal Infirmary. I was chagrined at the doubt cast on its nature in the discussion which followed the demonstration; at twenty, one is very intolerant of obscure aetiology! On my return home I imprudently assailed my Father with the question at the dinner table, where even renal oedema was taboo. He was exceedingly angry. There was a dreadful silence, and then he relented - 'of course it is tumour, - but mind to whom you say that' Nothing more was said. I submit that this terse pronouncement has been my most precious axiom, with deepest respect and affection

The normal breast microenvironment of premenopausal women differentially influences the behavior of breast cancer cells in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Breast cancer studies frequently focus on the role of the tumor microenvironment in the promotion of cancer; however, the influence of the normal breast microenvironment on cancer cells remains relatively unknown. To investigate the role of the normal breast microenvironment on breast cancer cell tumorigenicity, we examined whether extracellular matrix molecules (ECM) derived from premenopausal African-American (AA) or Caucasian-American (CAU) breast tissue would affect the tumorigenicity of cancer cells <it>in vitro </it>and <it>in vivo</it>. We chose these two populations because of the well documented predisposition of AA women to develop aggressive, highly metastatic breast cancer compared to CAU women.</p> <p>Methods</p> <p>The effects of primary breast fibroblasts on tumorigenicity were analyzed via real-time PCR arrays and mouse xenograft models. Whole breast ECM was isolated, analyzed via zymography, and its effects on breast cancer cell aggressiveness were tested <it>in vitro </it>via soft agar and invasion assays, and <it>in vivo </it>via xenograft models. Breast ECM and hormone metabolites were analyzed via mass spectrometry.</p> <p>Results</p> <p>Mouse mammary glands humanized with premenopausal CAU fibroblasts and injected with primary breast cancer cells developed significantly larger tumors compared to AA humanized glands. Examination of 164 ECM molecules and cytokines from CAU-derived fibroblasts demonstrated a differentially regulated set of ECM proteins and increased cytokine expression. Whole breast ECM was isolated; invasion and soft agar assays demonstrated that estrogen receptor (ER)^-, progesterone receptor (PR)/PR^-cells were significantly more aggressive when in contact with AA ECM, as were ER⁺/PR⁺cells with CAU ECM. Using zymography, protease activity was comparatively upregulated in CAU ECM. In xenograft models, CAU ECM significantly increased the tumorigenicity of ER⁺/PR⁺cells and enhanced metastases. Mass spectrometry analysis of ECM proteins showed that only 1,759 of approximately 8,000 identified were in common. In the AA dataset, proteins associated with breast cancer were primarily related to tumorigenesis/neoplasia, while CAU unique proteins were involved with growth/metastasis. Using a novel mass spectrometry method, 17 biologically active hormones were measured; estradiol, estriol and 2-methoxyestrone were significantly higher in CAU breast tissue.</p> <p>Conclusions</p> <p>This study details normal premenopausal breast tissue composition, delineates potential mechanisms for breast cancer development, and provides data for further investigation into the role of the microenvironment in cancer disparities.</p

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors