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The human sense of smell is often analyzed as being composed of three main components comprising olfactory threshold, odor discrimination and the ability to identify odors. A relevant distinction of the three components and their differential changes in distinct disorders remains a research focus. The present data-driven analysis aimed at establishing a cluster structure in the pattern of olfactory subtest results. Therefore, unsupervised machine-learning was applied onto olfactory subtest results acquired in 10,714 subjects with nine different olfactory pathologies. Using the U-matrix, Emergent Self-organizing feature maps (ESOM) identified three different clusters characterized by (i) low threshold and good discrimination and identification, (ii) very high threshold associated with absent to poor discrimination and identification ability, or (iii) medium threshold, i.e., in the mid-range of possible thresholds, associated with reduced discrimination and identification ability. Specific etiologies of olfactory (dys)function were unequally represented in the clusters (p < 2.2 · 10−16). Patients with congenital anosmia were overrepresented in the second cluster while subjects with postinfectious olfactory dysfunction belonged frequently to the third cluster. However, the clusters provided no clear separation between etiologies. Hence, the present verification of a distinct cluster structure encourages continued scientific efforts at olfactory test pattern recognition.
A machine-learned analysis suggests non-redundant diagnostic information in olfactory subtests
(2019)
Background: The functional performance of the human sense of smell can be approached via assessment of the olfactory threshold, the ability to discriminate odors or the ability to identify odors. Contemporary clinical test batteries include all or a selection of these components, with some dissent about the required number and choice.
Methods: Olfactory thresholds, odor discrimination and odor identification scores were available from 10,714 subjects (3662 with anomia, 4299 with hyposmia, and 2752 with normal olfactory function). To assess, whether the olfactory subtests confer the same information or each subtest confers at least partly non-redundant information relevant to the olfactory diagnosis, we compared the diagnostic accuracy of supervised machine learning algorithms trained with the complete information from all three subtests with that obtained when performing the training with the information of only two or one subtests.
Results: The training of machine-learned algorithms with the full information about olfactory thresholds, odor discrimination and odor identification from 2/3 of the cases, resulted in a balanced olfactory diagnostic accuracy of 98% or better in the 1/3 remaining cases. The most pronounced decrease in the balanced accuracy, to approximately 85%, was observed when omitting olfactory thresholds from the training, whereas omitting odor discrimination or identification was associated with smaller decreases (balanced accuracies approximately 90%).
Conclusions: Results support partly non-redundant contributions of each olfactory subtest to the clinical olfactory diagnosis. Olfactory thresholds provided the largest amount of non-redundant information to the olfactory diagnosis.
The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models.
Background: It is assumed that different pain phenotypes are based on varying molecular pathomechanisms. Distinct ion channels seem to be associated with the perception of cold pain, in particular TRPM8 and TRPA1 have been highlighted previously. The present study analyzed the distribution of cold pain thresholds with focus at describing the multimodality based on the hypothesis that it reflects a contribution of distinct ion channels.
Methods: Cold pain thresholds (CPT) were available from 329 healthy volunteers (aged 18 - 37 years; 159 men) enrolled in previous studies. The distribution of the pooled and log-transformed threshold data was described using a kernel density estimation (Pareto Density Estimation (PDE)) and subsequently, the log data was modeled as a mixture of Gaussian distributions using the expectation maximization (EM) algorithm to optimize the fit.
Results: CPTs were clearly multi-modally distributed. Fitting a Gaussian Mixture Model (GMM) to the log-transformed threshold data revealed that the best fit is obtained when applying a three-model distribution pattern. The modes of the identified three Gaussian distributions, retransformed from the log domain to the mean stimulation temperatures at which the subjects had indicated pain thresholds, were obtained at 23.7 °C, 13.2 °C and 1.5 °C for Gaussian #1, #2 and #3, respectively.
Conclusions: The localization of the first and second Gaussians was interpreted as reflecting the contribution of two different cold sensors. From the calculated localization of the modes of the first two Gaussians, the hypothesis of an involvement of TRPM8, sensing temperatures from 25 - 24 °C, and TRPA1, sensing cold from 17 °C can be derived. In that case, subjects belonging to either Gaussian would possess a dominance of the one or the other receptor at the skin area where the cold stimuli had been applied. The findings therefore support a suitability of complex analytical approaches to detect mechanistically determined patterns from pain phenotype data.
The measurement of concentrations of drugs and endogenous substances is widely used in basic and clinical pharmacology research and service tasks. Using data science‐derived visualizations of laboratory data, it is demonstrated on a real‐life example that basic statistical exploration of laboratory assay results or advised standard visual methods of data inspection may fall short in detecting systematic laboratory errors. For example, data pathologies such as generating always the same value in all probes of a particular assay run may pass undetected when using standard methods of data quality check. It is shown that the use of different data visualizations that emphasize different views of the data may enhance the detection of systematic laboratory errors. A dotplot of single data in the order of assay is proposed that provides an overview on the data range, outliers and a particular type of systematic errors where similar values are wrongly measured in all probes.
Biomedinformatics: A New Journal for the New Decade to Publish Biomedical Informatics Research
(2021)
With this volume, the peer-reviewed open access journal Biomedinformatics published online on the website https://www.mdpi.com/journal/biomedinformatics, and bearing the current International Standard Serial Number ISSN 2673-7426 enters the scientific community. At the beginning of the 3rd decade of the 21st century, this new journal is dedicated to research reports in the field of biomedical informatics. Biomedinformatics appears at a time when computational methods have reached clinical practice and the transformation to digital medicine is accelerating. Both digitized healthcare and bioinformatics-based research is producing and benefiting from increasingly complex data. This requires the development of tools and methods to extract information from these data and translate it into new knowledge. While biomedical research continues to require clinical and experi- mental data collection, digital healthcare research has clearly evolved from a collection of supporting methods to an equivalent scientific approach, enabling a paradigm shift from almost exclusively hypothesis-driven approaches to increasingly data-driven biomedical research. Indeed, computational science is a rapidly growing multidisciplinary field that uses advanced computational capabilities to understand and solve complex problems by applying new methods of computational intelligence, machine learning, and advanced statistics [1].
Immune checkpoint modulation in cancer has been demonstrated as a high-value therapeutic strategy in many tumor entities. VISTA is an immune checkpoint receptor regulating T-cell function. To the best of our knowledge, nothing is known about the expression and prognostic impact of VISTA on tumor infiltrating lymphocytes (TILs) in the tumor microenvironment of esophageal adenocarcinoma (EAC). We analyzed in total 393 EACs within a test-cohort (n = 165) and a validation-cohort (n = 228) using a monoclonal antibody (clone D1L2G). These data were statistically correlated with clinical as well as molecular data. 22.2% of the tumor cohort presented with a VISTA expression on TILs. These patients demonstrated an improved median overall survival compared to patients without VISTA expression (202.2 months vs. 21.6 months; p < 0.0001). The favorable outcome of VISTA positive tumors is significant in the entire cohort but mainly driven by the general better prognosis of T1/T2 tumors. However, in the pT1/2 group, VISTA positive tumors show a tremendous survival benefit compared to VISTA negative tumors revealing real long-term survivors in this particular subgroup. The survival difference is independent of the T-stage. This unique characteristic could influence neoadjuvant therapy concepts for EAC, since a profit of therapy could be reduced in the already favorable subgroup of VISTA positive tumors. VISTA emerges as a prognostic biomarker for long-term survival especially in the group of early TNM-stages. Future studies have to show the relevance of VISTA positive TILs within a tumor concerning response to specific immune checkpoint inhibition.
The CDK inhibitor SNS-032 had previously exerted promising anti-neuroblastoma activity via CDK7 and 9 inhibition. ABCB1 expression was identified as major determinant of SNS-032 resistance. Here, we investigated the role of ABCB1 in acquired SNS-032 resistance. In contrast to ABCB1-expressing UKF-NB-3 sub-lines resistant to other ABCB1 substrates, SNS-032-adapted UKF-NB-3 (UKF-NB-3rSNS- 032300nM) cells remained sensitive to the non-ABCB1 substrate cisplatin and were completely re-sensitized to cytotoxic ABCB1 substrates by ABCB1 inhibition. Moreover, UKF-NB-3rSNS-032300nM cells remained similarly sensitive to CDK7 and 9 inhibition as UKF-NB-3 cells. In contrast, SHEPrSNS-0322000nM, the SNS-032-resistant sub-line of the neuroblastoma cell line SHEP, displayed low level SNS-032 resistance also when ABCB1 was inhibited. This discrepancy may be explained by the higher SNS-032 concentrations that were used to establish SHEPrSNS-0322000nM cells, since SHEP cells intrinsically express ABCB1 and are less sensitive to SNS-032 (IC50 912 nM) than UKF-NB-3 cells (IC50 153 nM). In conclusion, we show that ABCB1 expression represents the primary (sometimes exclusive) resistance mechanism in neuroblastoma cells with acquired resistance to SNS-032. Thus, ABCB1 inhibitors may increase the SNS-032 efficacy in ABCB1-expressing cells and prolong or avoid resistance formation.
Background: Hemorrhagic shock/resuscitation is associated with aberrant neutrophil activation and organ failure. This experimental porcine study was done to evaluate the effects of Fas-directed extracorporeal immune therapy with a leukocyte inhibition module (LIM) on hemodynamics, neutrophil tissue infiltration, and tissue damage after hemorrhagic shock/resuscitation. Methods: In a prospective controlled double-armed animal trial 24 Munich Mini Pigs (30.3 +/- 3.3 kg) were rapidly haemorrhaged to reach a mean arterial pressure (MAP) of 35 +/- 5 mmHg, maintained hypotensive for 45 minutes, and then were resuscitated with Ringer's solution to baseline MAP. With beginning of resuscitation 12 pigs underwent extracorporeal immune therapy for 3 hours (LIM group) and 12 pigs were resuscitated according to standard medical care (SMC). Haemodynamics, haematologic, metabolic, and organ specific damage parameters were monitored. Neutrophil infiltration was analyzed histologically after 48 and 72 hours. Lipid peroxidation, and apoptosis were specifically determined in lung, bowel, and liver. Results: In the LIM group, neutrophil counts were reduced versus SMC during extracorporeal immune therapy. After 72 hours, the haemodynamic parameters MAP and cardiac output (CO) were significantly better in the LIM group. Histological analyses showed reduction of shock-related neutrophil tissue infiltration in the LIM group, especially in the lungs. Lower amounts of apoptotic cells and lipid peroxidation were found in organs after LIM treatment. Conclusions: Transient Fas-directed extracorporeal immune therapy may protect from posthemorrhagic neutrophil tissue infiltration and tissue damage.
Background: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma.
Methods: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics.
Results: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on "double-hit" MYC and BCL2 transformed lymphomas.
Conclusions: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.