Multi-Omic Candidate Screening for Markers of Severe Clinical Courses of COVID-19.

BACKGROUND: Severe coronavirus disease 2019 (COVID-19) disease courses are characterized by immuno-inflammatory, thrombotic, and parenchymal alterations. Prediction of individual COVID-19 disease courses to guide targeted prevention remains challenging. We hypothesized that a distinct serologic signature precedes surges of IL-6/D-dimers in severely affected COVID-19 patients. METHODS: We performed longitudinal plasma profiling, including proteome, metabolome, and routine biochemistry, on seven seropositive, well-phenotyped patients with severe COVID-19 referred to the Intensive Care Unit at the German Heart Center. Patient characteristics were: 65 ± 8 years, 29% female, median CRP 285 ± 127 mg/dL, IL-6 367 ± 231 ng/L, D-dimers 7 ± 10 mg/L, and NT-proBNP 2616 ± 3465 ng/L. RESULTS: Based on time-series analyses of patient sera, a prediction model employing feature selection and dimensionality reduction through least absolute shrinkage and selection operator (LASSO) revealed a number of candidate proteins preceding hyperinflammatory immune response (denoted ΔIL-6) and COVID-19 coagulopathy (denoted ΔD-dimers) by 24-48 h. These candidates are involved in biological pathways such as oxidative stress/inflammation (e.g., IL-1alpha, IL-13, MMP9, C-C motif chemokine 23), coagulation/thrombosis/immunoadhesion (e.g., P- and E-selectin), tissue repair (e.g., hepatocyte growth factor), and growth factor response/regulatory pathways (e.g., tyrosine-protein kinase receptor UFO and low-density lipoprotein receptor (LDLR)). The latter are host- or co-receptors that promote SARS-CoV-2 entry into cells in the absence of ACE2. CONCLUSIONS: Our novel prediction model identified biological and regulatory candidate networks preceding hyperinflammation and coagulopathy, with the most promising group being the proteins that explain changes in D-dimers. These biomarkers need validation. If causal, our work may help predict disease courses and guide personalized treatment for COVID-19.

Vascular dysregulation in glaucoma: retinal vasoconstriction and normal neurovascular coupling in altitudinal visual field defects.

Purpose: Vision loss in glaucoma is not only associated with elevated intraocular pressure and neurodegeneration, but vascular dysregulation (VD) is a major factor. To optimize therapy, an improved understanding of concepts of predictive, preventive, and personalized medicine (3PM) is needed which is based on a more detailed understanding of VD pathology. Specifically, to learn if the root cause of glaucomatous vision loss is of neuronal (degeneration) or vascular origin, we now studied neurovascular coupling (NVC) and vessel morphology and their relationship to vision loss in glaucoma. Methods: In patients with primary open angle glaucoma (POAG) (n = 30) and healthy controls (n = 22), NVC was studied using dynamic vessel analyzer to quantify retinal vessel diameter before, during, and after flicker light stimulation to evaluate the dilation response following neuronal activation. Vessel features and dilation were then related to branch level and visual field impairment. Results: Retinal arterial and venous vessels had significantly smaller diameters in patients with POAG in comparison to controls. However, both arterial and venous dilation reached normal values during neuronal activation despite their smaller diameters. This was largely independent of visual field depth and varied among patients. Conclusions: Because dilation/constriction is normal, VD in POAG can be explained by chronic vasoconstriction which limits energy supply to retinal (and brain) neurons with subsequent hypo-metabolism ("silent" neurons) or neuronal cell death. We propose that the root cause of POAG is primarily of vascular and not neuronal origin. This understanding can help to better personalize POAG therapy of not only targeting eye pressure but also vasoconstriction to prevent low vision, slowing its progression and supporting recovery and restoration. Trial registration: ClinicalTrials.gov, # NCT04037384 on July 3, 2019.

Predictive and Prognostic Value of TRIM58 Protein Expression in Patients with Breast Cancer Receiving Neoadjuvant Chemotherapy.

Introduction: Tripartite motif-containing protein (TRIM) family members play crucial roles in carcinogenesis and chemotherapy resistance. In this study, we aimed to determine whether TRIM58 protein expression is related to patient responses to neoadjuvant therapy (NAT) and their survival outcome. Methods: Immunohistochemistry was performed on female breast cancer samples from biopsies before NAT in Shenzhen Second People's Hospital. Univariate and multivariate logistic regression tests were used to analyze the association between TRIM58 protein expression and pathological complete response (pCR). The Cox proportional hazards model was used to calculate the adjusted hazard ratio (HR) with a 95% confidence interval (95% CI). The Kaplan-Meier plotter database was used to analyze the prognostic value of TRIM58. Results: High TRIM58 expression was associated with small tumor size in all the patients (n = 58). Multivariate analysis suggested that low TRIM58 expression was an independent predictive factor for higher pCR (odds ratio = 0.06, 95% CI 0.005-0.741, P = 0.028). The Kaplan-Meier Plotter dataset suggested that the TRIM58 high-expression group showed a worse 5-year overall survival than the low-expression group (HR = 1.34, 95% CI 1.07-1.67, P = 0.01). Pathway analysis revealed the potential mechanisms of TRIM58 in chemoresistance. Discussion: Our study suggests that TRIM58 is a promising biomarker for both neoadjuvant chemosensitivity and long-term clinical outcomes in breast cancer. It may also help to identify candidate responders and determine treatment strategies.

Prognostic significance of pretreatment red blood cell distribution width in primary diffuse large B-cell lymphoma of the central nervous system for 3P medical approaches in multiple cohorts.

Background/aims: Predicting the clinical outcomes of primary diffuse large B-cell lymphoma of the central nervous system (PCNS-DLBCL) to methotrexate-based combination immunochemotherapy treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). The red blood cell distribution width (RDW) has been reported to be associated with the clinical outcomes of multiple cancer. However, its prognostic role in PCNS-DLBCL is yet to be evaluated. Therefore, we aimed to effectively stratify PCNS-DLBCL patients with different prognosis in advance and early identify the patients who were appropriate to methotrexate-based combination immunochemotherapy based on the pretreatment level of RDW and a clinical prognostic model. Methods: A prospective-retrospective, multi-cohort study was conducted from 2010 to 2020. We evaluated RDW in 179 patients (retrospective discovery cohorts of Huashan Center and Renji Center and prospective validation cohort of Cancer Center) with PCNS-DLBCL treated with methotrexate-based combination immunochemotherapy. A generalized additive model with locally estimated scatterplot smoothing was used to identify the relationship between pretreatment RDW levels and clinical outcomes. The high vs low risk of RDW combined with MSKCC score was determined by a minimal P-value approach. The clinical outcomes in different groups were then investigated. Results: The pretreatment RDW showed a U-shaped relationship with the risk of overall survival (OS, P = 0.047). The low RDW (< 12.6) and high RDW (> 13.4) groups showed significantly worse OS (P < 0.05) and progression-free survival (PFS; P < 0.05) than the median group (13.4 > RDW > 12.6) in the discovery and validation cohort, respectively. RDW could predict the clinical outcomes successfully. In the discovery cohort, RDW achieved the area under the receiver operating characteristic curve (AUC) of 0.9206 in predicting the clinical outcomes, and the predictive value (AUC = 0.7177) of RDW was verified in the validation cohort. In addition, RDW combined with MSKCC predictive model can distinguish clinical outcomes with the AUC of 0.8348 for OS and 0.8125 for PFS. Compared with the RDW and MSKCC prognosis variables, the RDW combined with MSKCC scores better identified a subgroup of patients with favorable long-term survival in the validation cohort (P < 0.001). RDW combined MSKCC score remained to be independently associated with clinical outcomes by multivariable analysis. Conclusions: Based on the pretreatment RDW and MSKCC scores, a novel predictive tool was established to stratify PCNS-DLBCL patients with different prognosis effectively. The predictive model developed accordingly is promising to judge the response of PCNS-DLBCL to methotrexate-based combination immunochemotherapy treatment. Thus, hematologists and oncologists could tailor and adjust therapeutic modalities by monitoring RDW in a prospective rather than the reactive manner, which could save medical expenditures and is a key concept in 3PM. In brief, RDW combined with MSKCC model could serve as an important tool for predicting the response to different treatment and the clinical outcomes for PCNS-DLBCL, which could conform with the principles of predictive, preventive, and personalized medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00290-5.

Systemic Effects Reflected in Specific Biomarker Patterns Are Instrumental for the Paradigm Change in Prostate Cancer Management: A Strategic Paper.

Prostate cancer (PCa) is reported as the most common malignancy and second leading cause of death in America. In Europe, PCa is considered the leading type of tumour in 28 European countries. The costs of treating PCa are currently increasing more rapidly than those of any other cancer. Corresponding economic burden is enormous, due to an overtreatment of slowly developing disease on one hand and underestimation/therapy resistance of particularly aggressive PCa subtypes on the other hand. The incidence of metastatic PCa is rapidly increasing that is particularly characteristic for young adults. PCa is a systemic multi-factorial disease resulting from an imbalanced interplay between risks and protective factors. Sub-optimal behavioural patterns, abnormal stress reactions, imbalanced antioxidant defence, systemic ischemia and inflammation, mitochondriopathies, aberrant metabolic pathways, gene methylation and damage to DNA, amongst others, are synergistically involved in pathomechanisms of PCa development and progression. To this end, PCa-relevant systemic effects are reflected in liquid biopsies such as blood patterns which are instrumental for predictive diagnostics, targeted prevention and personalisation of medical services (PPPM/3P medicine) as a new paradigm in the overall PCa management. This strategic review article highlights systemic effects in prostate cancer development and progression, demonstrates evident challenges in PCa management and provides expert recommendations in the framework of 3P medicine.

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer's Disease.

Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer's disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD. Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set. Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively. Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.

Association of systemic inflammation indices with visual field loss progression in patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches.

RELEVANCE: Accumulating evidence suggests a dysfunction of the para-inflammation in the retinal ganglion cell layer and the optic nerve head in patients with glaucoma. Currently, circulating blood platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) are regarded as novel indicators of systemic inflammation. Biomarkers allow early identification of patients with visual field (VF) loss progression and timely implementation of replacement therapies. OBJECTIVE: This study aimed to investigate whether higher inflammatory indices (PLR, NLR, and LMR) were associated with VF loss progression in patients with primary angle-closure glaucoma (PACG) for the predictive diagnostics, targeted prevention, and personalization of medical services. METHODS: This prospective cohort study followed up 277 patients with PACG for at least 24 months, with clinical examination and VF testing every 6 months. Inflammatory cell quantification, including platelets, neutrophils, lymphocytes, and monocytes, was measured using the Sysmex XN-A1 automated inflammatory cells quantification system. Three systemic inflammatory indices, PLR, NLR, and LMR, were determined on the basis of baseline neutrophil, lymphocyte, monocyte, and platelet counts in patients with PACG. The risk factors for PACG were analyzed using logistic regression, Cox proportional hazards regression, and the Kaplan-Meier curve. RESULTS: Our results revealed that 111 (40.07%) patients showed VF loss progression. The PLR was significantly higher (P = 0.046) in the progression group than in the non-progression group. A higher PLR (OR 1.05, 95% CI 1.01-1.08, P = 0.004) was a risk factor for PACG progression. In multivariate analyses, PLR independently predicted VF loss progression (HR 1.01, 95% CI 1.00-1.01, P = 0.04). Kaplan-Meier curve analysis showed that higher PLR indicated significantly higher rates of VF loss progression (66.91% vs. 52.90%, P = 0.03). Comparable results were observed in the male and female subgroups. CONCLUSION: Our findings revealed the significant association between a high PLR and a greater risk of VF loss progression in patients with PACG. PLR may be highly recommended as a novel predictive/diagnostic tool for the assessment of VF loss progression from the perspectives of predictive, preventive, and personalized medicine in vulnerable populations and for individual screening. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00260-3.

Differential Protein Interactome in Esophageal Squamous Cell Carcinoma Offers Novel Systems Biomarker Candidates with High Diagnostic and Prognostic Performance.

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.

Background: Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them. Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model's performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators. Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies. Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.

Endothelial progenitor cells as the target for cardiovascular disease prediction, personalized prevention, and treatments: progressing beyond the state-of-the-art.

Stimulated by the leading mortalities of cardiovascular diseases (CVDs), various types of cardiovascular biomaterials have been widely investigated in the past few decades. Although great therapeutic effects can be achieved by bare metal stents (BMS) and drug-eluting stents (DES) within months or years, the long-term complications such as late thrombosis and restenosis have limited their further applications. It is well accepted that rapid endothelialization is a promising approach to eliminate these complications. Convincing evidence has shown that endothelial progenitor cells (EPCs) could be mobilized into the damaged vascular sites systemically and achieve endothelial repair in situ, which significantly contributes to the re-endothelialization process. Therefore, how to effectively capture EPCs via specific molecules immobilized on biomaterials is an important point to achieve rapid endothelialization. Further, in the context of predictive, preventive, personalized medicine (PPPM), the abnormal number alteration of EPCs in circulating blood and certain inflammation responses can also serve as important indicators for predicting and preventing early cardiovascular disease. In this contribution, we mainly focused on the following sections: the definition and classification of EPCs, the mechanisms of EPCs in treating CVDs, the potential diagnostic role of EPCs in predicting CVDs, as well as the main strategies for cardiovascular biomaterials to capture EPCs.

In glaucoma patients, low blood pressure is accompanied by vascular dysregulation.

BACKGROUND: There are many risk factors contributing to glaucomatous optic neuropathy. Beside increased intraocular pressure, vascular factors play a prominent role, particularly low blood pressure (BP), and vascular dysregulation. Both of them are essential components of the Flammer syndrome. The aim of this retrospective study was to evaluate whether in glaucoma patients there is a relationship between vascular dysregulation and the BP. METHODS: Medical records of 57 unselected glaucoma patients were retrospectively studied. RESULTS: Based on the outcome of the capillaroscopy, patients were divided in a group of patients with vascular dysregulation, also called long-stoppers (flow cessation for 13 s or more), and a group of patients with normal vascular regulation, also called short-stoppers (flow cessation for 12 s or less). BP was significantly lower in the group of long-stoppers than in the group of short-stoppers. This applies for both systolic (p = 0.028) and diastolic BP (p = 0.036). The regression analysis revealed also a significant inverse relationship between the duration of blood flow cessation and the systolic (p = 0.025) and diastolic BP (p = 0.016). After adjustment for age, gender, use of antihypertensive therapy, and excluding patients taking calcium channel blockers, the relationship was still significant for systolic (p = 0.025) and diastolic BP (p = 0.003). CONCLUSIONS: In glaucoma patients, vascular dysregulation (as defined by response in the nailfold capillaroscopy to a cold provocation) and low BP are statistically related. This is in line with the observation that Flammer syndrome subjects have both primary vascular dysregulation and low BP and that Flammer syndrome is a risk factor for glaucomatous optic neuropathy, at least in normal tension glaucoma patients. The detection of vascular factors in glaucoma patients may lead to a more efficient treatment, better tailored to the individual patient.

[Predictive diagnosis in breast cancer - Whats new in 2018?] / Prediktivní diagnostika u karcinomu prsu - co je nového pro rok 2018?

Detection of predictive markers in breast carcinoma has undergone significant changes, the most important ones - at least in the context of Czech Republic - are related to the detection of HER2 - detection of both over-expression of oncoprotein HER-2/neu and amplification of the c-erbB-2 gene, respectively. In the Czech Republic, immunohistochemical testing is performed as a method of first choice, followed, if needed, by in situ hybridization. The update of the guidelines published in 2013 decreased the threshold for positive tumor cells from 30% to 10%, shifted the threshold for gene amplification (HER2/CEP17 ratio) from 2.2 to 2.0 and slightly changed criteria for classification of expression as 2+. These changes resulted in relatively significant increase of cases classified as "borderline" or "equivocal", requiring confirmation by in situ hybridization. In order to reduce the risk of false results, the cases diagnosed as positive in small (primary) laboratories, have to be confirmed in one of the large central laboratories. This confirmation of HER2 positivity is required before targeted therapy can be started. HER2 testing is recommended in core-cut biopsies virtually always; it is absolutely essential in patients undergoing neoadjuvant systemic therapy. In patients treated primarily by surgery can be the testing performed either in the core cut biopsy or in the final resection specimen. However, it should be kept in mind that the accuracy of some parameters in the core-cut biopsies may be limited, even in cases not influenced by the neoadjuvant chemotherapy (NACHT). The degree of concordance between results of molecular tests in the core-cut biopsy and resection specimen can achieve only 67 % and the precise concordance of histological typing reaches only 84 %, respectively; the concordance of HER2 expression, on the other hand, reaches more than 90%. In patients with positive HER2 result in core-cut biopsy, it is no longer required to repeat the testing in the resection specimen, whereas in case of HER2 negative core-cut biopsy, it is required to repeat the test from resection specimen to minimize the risk of false negative result. The probability of pathological complete response (pCR) varies in individual breast carcinoma subtypes - it reaches 27-51% in triple-negative and HER2+ cases, while in hormone-dependent tumors, namely in those with low proliferative activity, it is significantly lower. Even within the HER2+ carcinoma subgroup, the probability of pCR varies. HER2+ tumors co-expressing ER and PR have a lower rate of pCR than HER2+ carcinomas without co-expression of hormonal receptors. Carcinomas expressing high-molecular weight keratins (CK14, CK 5/6) with basal phenotype or tumors with mutations of HER2/AKT signal pathway (PI3K, PTEN) have also lower response to treatment and worse prognosis.

[Predictive diagnostics of gastric cancer in 2018]. / Prediktivní diagnostika adenokarcinomu zaludku - stav v roce 2018.

Anti-HER2 monoclonal antibody trastuzumab remains the only targeted therapy of gastric carcinoma based on histopathological predictive diagnostics used in current routine clinical practice. In the Czech Republic only the adenocarcinomas with HER2 immunohistochemical score of 3+, together with HER2 amplification detected with in situ hybridization are indicated for treatment with trastuzumab. There has been recent progress in our understanding of the molecular biology of gastric cancer, the role of its tumor microenvironment and vascular supply points to PD-1/PD-L1 and VEGFR2 as possible future targets of targeted therapy. Unfortunately, the interpretation of the results of pharmacological studies, as well as establishing new algorithms of predictive diagnostics are complicated by insufficient molecular stratification of tumors enrolled in the study groups.

Endothelial dysfunction in cardiovascular disease and Flammer syndrome-similarities and differences.

The endothelium has increasingly been recognized as a smart barrier and a key regulator of blood flow in micro- and macrovascular beds. Endothelial dysfunction marks a stage of atherosclerosis and is an important prognostic marker for cardiovascular disease. Yet, some people who tend to be slim and physically active and with rather low blood pressure show a propensity to respond to certain stimuli such as emotional stress with endothelial-mediated vascular dysregulation (Flammer syndrome). This leads to characteristic vascular symptoms such as cold hands but also a risk for vascular-mediated diseases such as normal-tension glaucoma. It is the aim of this review to delineate the differences between Flammer syndrome and its "counterpart" endothelial dysfunction in the context of cardiovascular diseases.

A diagnostic microdosing approach to investigate platinum sensitivity in non-small cell lung cancer.

The platinum-based drugs cisplatin, carboplatin and oxaliplatin are often used for chemotherapy, but drug resistance is common. The prediction of resistance to these drugs via genomics is a challenging problem since hundreds of genes are involved. A possible alternative is to use mass spectrometry to determine the propensity for cells to form drug-DNA adducts-the pharmacodynamic drug-target complex for this class of drugs. The feasibility of predictive diagnostic microdosing was assessed in non-small cell lung cancer (NSCLC) cell culture and a pilot clinical trial. Accelerator mass spectrometry (AMS) was used to quantify [14 C]carboplatin-DNA monoadduct levels in the cell lines induced by microdoses and therapeutic doses of carboplatin, followed by correlation with carboplatin IC50 values for each cell line. The adduct levels in cell culture experiments were linearly proportional to dose (R2 = 0.95, p < 0.0001) and correlated with IC50 across all cell lines for microdose and therapeutically relevant carboplatin concentrations (p = 0.02 and p = 0.01, respectively). A pilot microdosing clinical trial was conducted to define protocols and gather preliminary data. Plasma pharmacokinetics (PK) and [14 C]carboplatin-DNA adducts in white blood cells and tumor tissues from six NSCLC patients were quantified via AMS. The blood plasma half-life of [14 C]carboplatin administered as a microdose was consistent with the known PK of therapeutic dosing. The optimal [14 C]carboplatin formulation for the microdose was 107 dpm/kg of body weight and 1% of the therapeutic dose for the total mass of carboplatin. No microdose-associated toxicity was observed in the patients. Additional accruals are required to significantly correlate adduct levels with response.

Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time.

Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences.

[Changes in pathology since the times of Purkinje]. / Promeny patologie od dob Purkynových.

The discipline of pathology was founded in the Purkinje era on the principles of building scientific knowledge of diseases through co-operation among clinical specialists. During the 20th century the focus of pathologists work has progressed from autopsies to biopsy diagnostics. The discipline of histopathological and cytopathological biopsy took form and continuously incorporates new methodologies and new standards. Its future course compels us to reflect on the changing requirements of clinical medicine. Current undergraduate education programs are directed toward forming a basis of expertise for both clinicians and non-medical healthcare professionals. At the postgraduate level and in research we are seeing an ever-closer interdisciplinary link, especially within the fields of genetics. This approach brings pathological processes to a new level, but also demands new levels of knowledge. Through comprehensive diagnostic experience, both prognostic and predictive, a pathologist is an indispensable member of the diagnostic and therapeutic team.

Molecular pathological predictive diagnostics in a patient with non-small cell lung cancer treated with crizotinib therapy: A case report.

Lung cancer is one of the most common malignant cancers in the Czech Republic in men, with the highest mortality rate of all the malignant diseases. The development of biological treatment enables study into novel personalized treatment options. This type of treatment is usually of high quality, and is often demanding of predictive and biopsy diagnostics, which is dependent on the quality of the collected material and close cooperation among particular departments. The present study describes the complete biopsy and predictive examinations performed in a male patient with lung adenocarcinoma, with an emphasis on the logistics of the whole process and the application of the tyrosine kinase inhibitors, crizotinib and LDK378. The patient experienced a long overall survival time of 28 months from diagnosis.

Multiple sclerosis and primary vascular dysregulation (Flammer syndrome).

BACKGROUND: Multiple sclerosis (MS) is an inflammatory demyelinating disease of the CNS of still unknown aetiology. Flammer syndrome (FS) encompasses a set of symptoms and signs that are primarily but not solely related to the dysregulation of blood vessels. The purpose of the present study was to determine whether FS symptoms occur more often in MS patients than in controls. METHODS: Fifty-eight MS patients and 259 controls answered a questionnaire covering 15 symptoms and signs of FS. RESULTS: Six of the 15 symptoms and signs of FS (dizziness, low body mass index, cold hands and/or feet, tendency toward perfectionism, reduced thirst, feeling cold) were found significantly more often in MS patients than in controls. Seven additional symptoms and signs (tinnitus, headaches, increased pain sensation, long sleep-onset time, migraines, increased response to certain drugs, low blood pressure) also occurred more often in MS patients, but the difference in frequency was not statistically significant. One sign (reversible skin blotches) was found less often in MS patients, but the difference in frequency was not statistically significant. One symptom (increased smell perception) was found significantly less often in MS patients. CONCLUSIONS: MS patients suffer significantly more often from FS symptoms and signs than controls. The reason for this association between MS and FS and the potential implications of this association still need to be determined.

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics.

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.