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This dataset comprises information about 1242 lung cancer patients collected by the Medical Oncology Department of the Puerta de Hierro University Hospital of Majadahonda in Madrid, Spain. It includes information about cancer diagnosis and treatment, as well as personal and medical data recorded during anamneses. The dataset could assist in data analysis with the aim of discovering relationships between the applied treatment(s), the evolution of the disease and the associated adverse effects. A greater understanding of treatment effects based on the particular conditions of the patient and the diagnosis could directly impact the healthcare system, helping to improve expectations about lung cancer as well as reducing treatment toxicities and adverse effects.

Most pathogens are generalists capable of infecting multiple host species or strains. Trade‐offs in performance among different hosts are expected to limit the evolution of generalism. Despite the commonness of generalism, the variation in infectivity, transmission, and trade‐offs in performance among host species have rarely been studied in the wild. To understand the ecological and evolutionary drivers of multi‐host pathogen infectivity and transmission potential, I studied disease severity, transmission dynamics, and infectivity variation of downy mildew pathogen Peronospora sparsa on its three host plants Rubus arcticus, R. chamaemorus, and R. saxatilis. In a survey of 20 wild and cultivated sites of the three host species, disease severity varied by host species and by host population size but not among wild and cultivated sites. To understand how alternative host presence and plant diversity affect transmission of the pathogen, I conducted a transmission experiment. In this experiment, alternative host abundance and plant diversity together modified P. sparsa transmission to trap plants. To understand how resistance to P. sparsa varies among host species and genotypes, I conducted an inoculation experiment using 10 P. sparsa strains from different locations and 20 genotypes of the three host species. Significant variation in infectivity was found among host genotypes but not among host species. When trade‐offs for infectivity were tested, high infectivity in one host species correlated with high infectivity in another host species. However, when pathogen transmission‐related life‐history correlations were tested, a positive correlation was found in R. arcticus but not in R. saxatilis. The results suggest that host resistance may shape pathogen life‐history evolution with epidemiological consequences in a multi‐host pathogen. [ABSTRACT FROM AUTHOR]

Abstract Most pathogens are generalists capable of infecting multiple host species or strains. Trade‐offs in performance among different hosts are expected to limit the evolution of generalism. Despite the commonness of generalism, the variation in infectivity, transmission, and trade‐offs in performance among host species have rarely been studied in the wild. To understand the ecological and evolutionary drivers of multi‐host pathogen infectivity and transmission potential, I studied disease severity, transmission dynamics, and infectivity variation of downy mildew pathogen Peronospora sparsa on its three host plants Rubus arcticus, R. chamaemorus, and R. saxatilis. In a survey of 20 wild and cultivated sites of the three host species, disease severity varied by host species and by host population size but not among wild and cultivated sites. To understand how alternative host presence and plant diversity affect transmission of the pathogen, I conducted a transmission experiment. In this experiment, alternative host abundance and plant diversity together modified P. sparsa transmission to trap plants. To understand how resistance to P. sparsa varies among host species and genotypes, I conducted an inoculation experiment using 10 P. sparsa strains from different locations and 20 genotypes of the three host species. Significant variation in infectivity was found among host genotypes but not among host species. When trade‐offs for infectivity were tested, high infectivity in one host species correlated with high infectivity in another host species. However, when pathogen transmission‐related life‐history correlations were tested, a positive correlation was found in R. arcticus but not in R. saxatilis. The results suggest that host resistance may shape pathogen life‐history evolution with epidemiological consequences in a multi‐host pathogen.

Objectives: The aims of this study were, to analyze epicardial adipose tissue and pre-sternal adipose tissue thicknesses and the relationship of the ratio of these two parameters with radiological progression, age, gender, concomitant diseases, hospitalization, length of hospital stay, need for intensive care and survival status of COVID-19 patients. Methods: In this retrospective study, a total number of 204 PCR-positive COVID-19 patients, who have initial lung computed tomography (CT) and a second CT within 15 days due to prolonged symptoms or suspected complications were included. According to patterns of lung involvement at the time of diagnosis, patients were divided into 4 groups. In initial CT scans, epicardial adipose tissue and pre-sternal subcutaneous adipose tissue thickness were measured. Progression or regression of the disease is evaluated by comparing the findings in initial and control CTs. Results: The mean age, epicardial adipose tissue thickness (EAT), pre-sternal adipose tissue thickness (PAT), and the EAT/PAT ratio of patients with involvement in both lungs were found to be higher than those in patients with one lung or without lung involvement and there was a statistically significant positive correlation between them. Conclusions: This study is thought to be the first in which epicardial adipose tissue and pre-sternal adipose tissue were evaluated together in COVID-19 patients. Epicardial adipose tissue is a metabolically active organ and measurement in initial CT scans may give an easy and quick idea of the evolution of the disease. [ABSTRACT FROM AUTHOR]

Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T2W) images obtained from serial brain MRI scans at the baseline (Time0M) and the repeat (Time16-12M) examinations. The subjects were separated into two different groups based on their EDSS: (G1: $1\le $ EDSS $_{\mathrm {2Y}}\le 3.5$ (N=26) and G2: $3.5 < $ EDSS $_{\mathrm {2Y}}\le 8.5$ (N=12) and were monitored over ten years’ time (Time $_{\mathrm {10Y}}$ ). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time0M and Time16-12M. The extracted features were used to develop models that correlated with the disease progression in Time10Y. We found statistically significant differences for features extracted from the two different groups (G1 vs G2 at Time $_{\mathrm {10Y}}$ ) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G1 vs G2 subjects at Time10Y included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

The unexpectable variations of the diagnosed disease symptoms are quite often observed during medical diagnosis. In stochastics, such behavior is called "grey swan" or "black swan" as synonyms of sudden, unpredictable change. Evolution of the disease's symptoms is usually described by means of Markov processes, where dependency on process history is neglected. The common expectation is that such processes are Gaussian. It is demonstrated here that medical observation can be described as a Markov process and is non-Gaussian. Presented non-Gaussian processes have "fat tail" probability density distribution (pdf). "Fat tail" permits a slight change of probability density distribution and triggers an unexpectable big variation of the diagnosed parameter. Such "fat tail" solution is delivered by the anomalous diffusion model applied here to describe disease evolution and to explain the possible presence of "swans" mentioned above. The proposed model has been obtained as solution of the Fractal Fokker–Planck equation (FFPE). The paper shows a comparison of the results of the theoretical model of anomalous diffusion with experimental results of clinical studies using bioimpedance measurements in cardiology. This allows us to consider the practical usefulness of the proposed solutions. [ABSTRACT FROM AUTHOR]

Background: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to compare three update strategies for predicting severe COVID-19 outcomes postdiagnosis: "naive" (a single initial model), "frequent" (periodic retraining), and "context-driven" (retraining informed by clinical insights). The goal is to determine the most effective timing and approach for adapting algorithms to evolving disease dynamics and emerging data., Methods: A dataset of 1.11 million COVID-19 patients from diverse U.S. regions was used to develop and validate an XGBoost algorithm for predicting severe outcomes upon diagnosis. Data included patient demographics, vital signs, comorbidities, and immunity-related factors (prior infection and vaccination status) from January 2007 to November 2021. The study analyzed the performance of the three update strategies from March 2020 to November 2021., Results: Predictive features changed over the pandemic, with comorbidities and vitals being significant initially, and geography, demographics, and immunity-related variables gaining importance later. The "naive" strategy had an average area under the curve (AUC) of 0.77, the "frequent" strategy maintained stability with an average AUC of 0.81, and the "context-driven" strategy averaged an AUC of 0.80, outperforming the "naive" strategy and aligning closely with the "frequent" strategy., Conclusions: A context-driven approach, guided by clinical insights, can enhance predictive performance and offer cost-effective solutions for dynamic public health challenges. These findings have significant implications for efficiently managing healthcare resources during evolving disease outbreaks., (Copyright © 2024 Elsevier Inc. All rights reserved.)

• Introduction of the disease evolution-based specificity target discovery (DESTD) strategy. • Identification of specific therapeutic targets for three variations of the jiawei-maxing-shigan decoction. • Elucidation of specific mechanisms at three stages of COVID-19 using the DESTD strategy. Diverse disease courses, each corresponding to a unique pathophysiological mechanism, characterize the coronavirus 2019 disease (COVID-19). Intensive research on discovering specificity targets for treating COVID-19 has significant implications for understanding pathogenesis mechanisms in disease evolution. Traditional Chinese medicine (TCM) has shown distinctive advantages in treating COVID-19 in different periods, which may provide new clues for target discovery. In order to accomplish the goal of providing a Disease Evolution-based Specificity Target Discovery (DESTD), the purpose of this paper was to conduct an analysis of the difference in target weighted value during each cycle of COVID-19. Our study employed a comprehensive methodology combining TCM pharmacology with modern biological techniques. Three Jiawei-Maxing-Shigan Decoctions (MS Decoction), were selected as samples, including Hanshi-Yufei decoction (HY Decoction), Shidu-Yufei decoction (SY Decoction), and Yidu-Bifei decoction (YB Decoction). The core components of these decoctions are the same, while the differences correspond to different periods of COVID-19. The DESTD approach consists of several parts: network pharmacology analysis to identify potential targets, molecular docking simulation to investigate TCM components acting on potential targets, and biological validation. This study employed several cell line models, including RAW264.7 (Mouse Mononuclear Macrophages Cells), 16HBE (Human Bronchial Epithelial Cells), and HUVEC C (Human Umbilical Vein Endothelial Cells), to explore new approaches for key target discovery. During the mild phase of COVID-19, HY Decoction exhibited anti-inflammatory effects by specifically down-regulating the expression of heat shock protein HSP 90-alpha (HSP90AA1), transcription factor JUN (JUN) and other five genes in RAW264.7. In the common phrase, inflammation and fibrosis could be specifically alleviated by down-regulating the expression of interleukin-10 (IL10) and mitogen-activated protein kinase 14 (MAPK14) by SY Decoction respectively acting on RAW264.7 and 16HBE cell lines. In severe cases, YB Decoction specifically down-regulated the expression of prostaglandin endoperoxide synthase-2 (PTGS2), caspase-3 (CASP3), and three other genes to protect vascular endothelial cells from oxidative damage. Moreover, YB also played a role in anti-fibrosis by specifically down-regulating the expression of estrogen receptor beta (ESR2), vascular cell adhesion protein 1 (VCAM1), and other three genes, and up-regulating the expression of prostaglandin endoperoxide synthase-1 (PTGS1) in the severe period. These targeted actions align with a holistic treatment approach and provide tailored therapies for different stages of COVID-19. This research proposed a novel strategy for Disease Evolution-based Specificity Target Discovery (DESTD) to identify specific targets of different stages of disease and specific targets and mechanisms throughout the evolution of COVID-19. [ABSTRACT FROM AUTHOR]

The aims of this study were to describe the trends in the behavior of pediatric CD during the last decade and to describe the seasonal variation of disease presentation. Patients under 18 years old and diagnosed between 2009 and 2019 were included. The clinical, endoscopic, histological, and laboratory data were collected from the medical records. We analyzed the trends of these parameters according to the year and season of diagnosis. 654 patients were included in the study. The number of incident CD cases increased yearly. Patients diagnosed between 2015 and 2019 were younger at diagnosis (OR 2.53, p = 0.02), had more perianal diseases (OR: 2.30, p < 0.0001) and more granulomas (OR: 1.61, p = 0.003), but fewer eosinophils (OR: 0.35, p < 0.0001) and less chronic lymphoplasmacytic infiltrate (OR: 0.56, p = 0.008) as compared to the 2009–2014 cohort. There was fewer CD diagnosis during winter. Patients diagnosed in the fall had lower PCDAIs, less failure to thrive and less extensive digestive involvement. Colonic disease was significantly more frequent during summer and fall. The clinical and histological phenotype of CD has changed over time and there are important seasonal trends in the frequency and severity on disease behavior suggesting possible disease triggers. [ABSTRACT FROM AUTHOR]

Canine distemper virus (CDV) and phocine distemper virus (PDV) are major pathogens to terrestrial and marine mammals. Yet little is known about the timing and geographical origin of distemper viruses and to what extent it was influenced by environmental change and human activities. To address this, we (i) performed the first comprehensive time-calibrated phylogenetic analysis of the two distemper viruses, (ii) mapped distemper antibody and virus detection data from marine mammals collected between 1972 and 2018, and (iii) compiled historical reports on distemper dating back to the eighteenth century. We find that CDV and PDV diverged in the early seventeenth century. Modern CDV strains last shared a common ancestor in the nineteenth century with a marked radiation during the 1930s–1950s. Modern PDV strains are of more recent origin, diverging in the 1970s–1980s. Based on the compiled information on distemper distribution, the diverse host range of CDV and basal phylogenetic placement of terrestrial morbilliviruses, we hypothesize a terrestrial CDV-like ancestor giving rise to PDV in the North Atlantic. Moreover, given the estimated timing of distemper origin and radiation, we hypothesize a prominent role of environmental change such as the Little Ice Age, and human activities like globalization and war in distemper virus evolution. [ABSTRACT FROM AUTHOR]

The unexpectable variations of the diagnosed disease symptoms are quite often observed during medical diagnosis. In stochastics, such behavior is called “grey swan” or “black swan” as synonyms of sudden, unpredictable change. Evolution of the disease’s symptoms is usually described by means of Markov processes, where dependency on process history is neglected. The common expectation is that such processes are Gaussian. It is demonstrated here that medical observation can be described as a Markov process and is non-Gaussian. Presented non-Gaussian processes have “fat tail” probability density distribution (pdf). “Fat tail” permits a slight change of probability density distribution and triggers an unexpectable big variation of the diagnosed parameter. Such “fat tail” solution is delivered by the anomalous diffusion model applied here to describe disease evolution and to explain the possible presence of “swans” mentioned above. The proposed model has been obtained as solution of the Fractal Fokker–Planck equation (FFPE). The paper shows a comparison of the results of the theoretical model of anomalous diffusion with experimental results of clinical studies using bioimpedance measurements in cardiology. This allows us to consider the practical usefulness of the proposed solutions.

Abstract Background The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research. Results In this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges. Conclusions The identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.

Introduction: Efficient evaluation with an early surrogate endpoint, taking into account the process of disease evolution, may not only clarify inconsistent or underpowered results but also provide a new insight into the exploration of a new antiviral therapy for treating COVID-19 patients. Methods: We assessed the dynamics of COVID-19 disease spectrum, commencing from low-risk (no or low oxygen supplement), medium-risk (non-invasive ventilator or high oxygen supplement), and high-risk (extracorporeal membrane oxygenation or invasive ventilator) risk state on enrollment, and then the subsequent progression and regression of risk states until discharge or death. The efficacy of antiviral therapy in altering the dynamics was assessed by using the high-risk state as a surrogate endpoint based on the data retrieved from the two-arm Adaptive Covid-19 Treatment Trial. Results: Using the high-risk state as a surrogate endpoint, remdesivir treatment led to a decrease in the high-risk COVID-19 state by 34.8% (95% CI 26.7–42.0%) for a 14-day period and 29.3% (95% CI 28.8–29.8%) up to 28 days, which were consistent with a statistically significant reduction of death by 30.5% (95% CI 6.6, 50.9%) up to a 28-day period. The estimates of numbers needed to be treated were 100.9 (95% CI 88.1, 115.7) for using the high-risk COVID-19 state as a surrogate endpoint for a 14-day period and 133.3 (95% CI 112.5, 158.0) were required for averting one death from COVID-19 up to 28 days. Conclusions: We demonstrate the expedient use of the high-risk COVID-19 disease status as a surrogate endpoint for evaluating the primary outcome of the earliest death. [ABSTRACT FROM AUTHOR]

High-risk neuroblastoma (HR-NB) is branded with hematogenous metastasis, relapses, and dismal long-term survival. Intensification of consolidation therapy with tandem/triple autologous stem cell (SC) rescue (with bone marrow [BM]/peripheral blood [PB] CD34+ selection) after myeloablative chemotherapy has improved long-term survival. However, the benefit is limited by the indication of NB cells in CD34+ PBSCs, CD34 expression in NB cells, and the risk of reinfusing NB cancer stem cells (NB CSCs) that could lead to post-transplant relapse. We investigated the association of CD34 surface expression (92 patients) with NB evolution/clinical outcomes. CD34 gene-level status in NB was assessed through RNA-Seq data mining (18 cohorts, n, 3324). Genetic landscape of CD34-expressing NB CSCs (CD133+CD34+) was compared with CD34− CSCs (CD133+CD34−). RNA-seq data revealed equivocal association patterns of CD34 expression with patient survival. Our immunohistochemistry data revealed definite, but rare (mean, 0.73%; range 0.00–7.87%; median, 0.20%) CD34 positivity in NB. CD34+ significantly associated with MYCN amplification (p, 0.003), advanced disease stage (p, 0.016), and progressive disease (PD, p < 0.0009) after clinical therapy. A general high-is-worse tendency was observed in patients with relapsed disease. High CD34+ correlated with poor survival in patients with N-MYC-amplified HR-NB. Gene expression analysis of CD34+-NB CSCs identified significant up (4631) and downmodulation (4678) of genes compared with NB CSCs that lack CD34. IPA recognized the modulation of crucial signaling elements (EMT, stemness maintenance, differentiation, inflammation, clonal expansion, drug resistance, metastasis) that orchestrate NB disease evolution in CD34+ CSCs compared with CD34− CSCs. While the function of CD34 in NB evolution requires further in-depth investigation, careful consideration should be exercised for autologous stem cell rescue with CD34+ selection in NB patients. [ABSTRACT FROM AUTHOR]

Abstract More is not automatically better. Generation and accumulation of information reflecting the complexity of zoonotic diseases as ecological systems do not necessarily lead to improved interpretation of the obtained information and understanding of these complex systems. The traditional conceptual framework for analysis of diseases ecology is neither designed for, nor adaptable enough, to absorb the mass of diverse sources of relevant information. The multidirectional and multidimensional approaches to analyses form an inevitable part in defining a role of zoonotic pathogens and animal hosts considering the complexity of their inter‐relations. And the more data we have, the more involved the interpretation needs to be. The keyword for defining the roles of microbes as pathogens, animals as hosts, and environmental parameters as infection drivers is “functional importance.” Microbes can act as pathogens toward their host only if/when they recognize the animal organism as the target. The same is true when the host recognizes the microbe as a pathogen rather than harmless symbiont based on the context of its occurrence in that host. Here, we propose conceptual tools developed in the realm of the interdisciplinary sciences of complexity and biosemiotics for extending beyond the currently dominant mindset in ecology and evolution of infectious diseases. We also consider four distinct hierarchical levels of perception guiding how investigators can approach zoonotic agents, as a subject of their research, representing differences in emphasizing particular elements and their relations versus more unified systemic approaches.

Introduction Tuberculosis affects commonly the lungs, but any other organs can be affected as well. Urogenital tuberculosis is usually misdiagnosed. In this perspective, we aimed to give an update on the epidemiological, clinical and evolutionary features of urogenital tuberculosis in Southern Tunisia. Methods We conducted a retrospective study including all patients with extrapulmonary tuberculosis notified during the period from 1992 to 2017 in Southern Tunisia. We specified the particularities of urogenital tuberculosis cases, and we compared them with other extrapulmonary tuberculosis cases. Results Overall, we analyzed 240 cases with urogenital tuberculosis, among 1702 patients with extrapulmonary tuberculosis (14.1%). There were 121 women (50.4%). The mean age was 49±17 years. Multifocal tuberculosis was noted in 29 cases (12.1%). There were 169 cases with urinary tract tuberculosis (70.4%). Chronological trends analysis showed that the median age at diagnosis increased significantly (Rho=0.41; p=0.039) and the number of urogenital tuberculosis declined during the study period, without a statistical significance (Rho = -0.07; p=0.721). Compared to other extrapulmonary tuberculosis sites, patients aged 60 years and above (OR=2.7; p<0.001) and coming from rural areas (OR=1.4; p=0.021) were more frequently diagnosed with urogenital tuberculosis. Treatment duration was significantly longer in patients with urogenital tuberculosis (10.13±3.79 vs 9.20±3.77 months; p<0.001). As for the disease evolution, relapse was significantly more frequent in patients with urogenital tuberculosis (OR=4.1; p=0.045). Conclusions Although decreasing trends over time were noted, the prognosis of urogenital tuberculosis was more severe compared to other extrapulmonary tuberculosis sites. [ABSTRACT FROM AUTHOR]

Mixed connective tissue disease was first described as a new autoimmune rheumatic disease in 1972 based on the claim of a distinct clinical picture associated with anti-RNP antibody positivity. Subsequently, this new entity has divided opinions in the rheumatology community. We have reviewed recent cohort studies with more than 100 patients, comparing the clinical and immunological features, treatment, prognosis and evolution to well-defined autoimmune rheumatic diseases. We also reviewed clinical features of undifferentiated autoimmune rheumatic diseases based on the most recent studies. After gathering and reviewing these data, we discuss whether the designation "mixed connective tissue disease" should be maintained. [ABSTRACT FROM AUTHOR]

Purpose This prospective study was undertaken to describe patterns of fragmentation of the femoral epiphysis following a proximal femoral varus osteotomy (PFVO) done during stage I of LCPD and to assess the disease duration and outcome in each pattern. Methods A total of 25 children treated by a PFVO in stage I of LCPD were followed until healing. The MRI Perfusion Index, radiographic changes in the femoral epiphysis, disease duration and the Sphericity Deviation Score (SDS) at healing were documented. The reproducibility of classification of the pattern of fragmentation, estimation of disease duration and SDS were assessed. The duration of the disease and SDS in the patterns of fragmentation were compared. Results Four patterns of fragmentation were noted, namely, typical fragmentation, bypassing fragmentation, abortive fragmentation and atypical fragmentation with horizontal fissuring. The reproducibility of classifying the pattern of fragmentation was moderate (Kappa: 0.48) while the reproducibility of other continuous variables was excellent. The Perfusion Index was less than 50% in every affected hip. The duration of the disease and SDS were lowest in children in whom the stage of fragmentation was bypassed but these differences were not statistically significant. Conclusion Following a proximal femoral osteotomy during stage I of LCPD the fragmentation stage may be bypassed partially or completely and the chances of a good outcome appear to be very good if fragmentation is bypassed. [ABSTRACT FROM AUTHOR]

Book details A review of L. Spinney. 2017 Pale Rider: The Spanish Flu of 1918 and How It Changed the World Public Affairs 352 pg., ISBN: 978-1610397674.

Background: The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research. Results: In this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges. Conclusions: The identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients. [ABSTRACT FROM AUTHOR]

Abstract: More is not automatically better. Generation and accumulation of information reflecting the complexity of zoonotic diseases as ecological systems do not necessarily lead to improved interpretation of the obtained information and understanding of these complex systems. The traditional conceptual framework for analysis of diseases ecology is neither designed for, nor adaptable enough, to absorb the mass of diverse sources of relevant information. The multidirectional and multidimensional approaches to analyses form an inevitable part in defining a role of zoonotic pathogens and animal hosts considering the complexity of their inter‐relations. And the more data we have, the more involved the interpretation needs to be. The keyword for defining the roles of microbes as pathogens, animals as hosts, and environmental parameters as infection drivers is “functional importance.” Microbes can act as pathogens toward their host only if/when they recognize the animal organism as the target. The same is true when the host recognizes the microbe as a pathogen rather than harmless symbiont based on the context of its occurrence in that host. Here, we propose conceptual tools developed in the realm of the interdisciplinary sciences of complexity and biosemiotics for extending beyond the currently dominant mindset in ecology and evolution of infectious diseases. We also consider four distinct hierarchical levels of perception guiding how investigators can approach zoonotic agents, as a subject of their research, representing differences in emphasizing particular elements and their relations versus more unified systemic approaches. [ABSTRACT FROM AUTHOR]