Development of a Novel Method for Biochemical Systems Simulation: Incorporation of Stochasticity in a Deterministic Framework

Heart disease, cancer, diabetes and other complex diseases account for more than half of human mortality in the United States. Other diseases such as AIDS, asthma, Parkinson’s disease, Alzheimer’s disease and cerebrovascular ailments such as stroke not only augment this mortality but also severely deteriorate the quality of human life experience. In spite of enormous financial support and global scientific effort over an extended period of time to combat the challenges posed by these ailments, we find ourselves short of sighting a cure or vaccine. It is widely believed that a major reason for this failure is the traditional reductionist approach adopted by the scientific community in the past. In recent times, however, the systems biology based research paradigm has gained significant favor in the research community especially in the field of complex diseases. One of the critical components of such a paradigm is computational systems biology which is largely driven by mathematical modeling and simulation of biochemical systems. The most common methods for simulating a biochemical system are either: a) continuous deterministic methods or b) discrete event stochastic methods. Although highly popular, none of them are suitable for simulating multi-scale models of biological systems that are ubiquitous in systems biology based research. In this work a novel method for simulating biochemical systems based on a deterministic solution is presented with a modification that also permits the incorporation of stochastic effects. This new method, through extensive validation, has been proven to possess the efficiency of a deterministic framework combined with the accuracy of a stochastic method. The new crossover method can not only handle the concentration and spatial gradients of multi-scale modeling but it does so in a computationally efficient manner. The development of such a method will undoubtedly aid the systems biology researchers by providing them with a tool to simulate multi-scale models of complex diseases

Predictors of Neonatal Sepsis in Rural Karnataka, India

Background: Neonatal sepsis can present with subtle signs but have a fulminant and fatal course if not recognised and treated early. Many practitioners in resource-poor settings are forced to empirically manage infants at risk for sepsis without access to blood cultures. We sought to identify predictors of poor outcomes in infants with suspected sepsis at a hospital in rural Karnataka, India. Materials and Methods: This was an observational study of infants aged zero to 30 days who were admitted from January to December 2011 with a diagnosis of presumed bacterial sepsis. We extracted perinatal risk factors, gestational age, birth weight, history and physical exam at the time of admission, white blood cell count, C-reactive protein, duration of hospitalisation, disposition, and blood culture results from medical charts. Poor outcome was defined as death, positive blood culture, hospitalisation greater than five days, or transfer for higher level of care. We calculated predictive values and odds ratio for each variable using univariate logistic regression. Results: Seventy-nine infants were included; 58 (73.4%) experienced a poor outcome. Prematurity and temperature instability were significantly associated with poor outcome, with trends towards higher risk for those having very low birth weight, convulsions, a bulging fontanelle, or lethargy on admission. Nine blood cultures were positive, including seven with Staphylococcus. Conclusions: In a cohort of infants admitted for presumed sepsis in rural Karnataka, prematurity and temperature instability were associated with poor outcome. Larger studies are needed to evaluate bacterial aetiologies and determine the optimal antibiotic regimen

Principles of Resistance to Targeted Cancer Therapy: Lessons from Basic and Translational Cancer Biology

Identification of the genomic drivers of cancer has led to the clinical development of targeted therapies that strike at the heart of many malignancies. Nonetheless, many cancers outsmart such precision-medicine efforts, and thus therapeutic resistance contributes significantly to cancer mortality. Attempts to understand the basis for resistance in patient samples and laboratory models has yielded two major benefits: one, more effective chemical inhibitors and rational combination therapies are now employed to prevent or circumvent resistance pathways; and two, our understanding of how oncogenic mutations drive cancer cell survival and oncogene addiction is deeper and broader, highlighting downstream or parallel cellular programs that shape these phenotypes. This review discusses emerging principles of resistance to therapies targeted against key oncogenic drivers

FGFR Fusions in the Driver's Seat

Through a clinical deep sequencing protocol, Wu and colleagues have identified multiple FGFR fusion proteins in diverse cancers. Pharmacologic inhibition of FGFR suppressed the growth of FGFR fusion-positive tumor models, suggesting that these FGFR fusions are oncogenic drivers and highlighting the use of streamlined clinical sequencing efforts to identify novel, actionable driver oncoproteins in human tumors

FGFR Fusions in the Driver's Seat

Through a clinical deep sequencing protocol, Wu and colleagues have identified multiple FGFR fusion proteins in diverse cancers. Pharmacologic inhibition of FGFR suppressed the growth of FGFR fusion positive tumor models, suggesting that these FGFR fusions are oncogenic drivers and highlighting the utility of streamlined clinical sequencing efforts to identify novel, actionable driver oncoproteins in human tumors

Staghorn classification: Platform for morphometry assessment

Introduction: The majority of staghorn classifications do not incorporate volumetric stone burden assessment. Accurate volumetric data can easily be acquired with the ever-increasingly available computerized tomography (CT) scan. This manuscript reviews the available staghorn stone classifications and rationalizes the morphometry-based classification. Materials and Methods: A Pubmed search was performed for articles concerning staghorn classification and morphometry. Twenty abstracts were shortlisted from a total of 43 published abstracts. In view of the paucity of manuscripts on staghorn morphometry (4), older staghorn classifications were analyzed with the aim to determine the most optimum one having relevance to the percutaneous nephrolithotomy (PCNL) monotherapy outcome. Results: All available staghorn classifications are limited with non-widespread applicability. The traditional partial and complete staghorn are limited due to non-descript stone volumetric data and considerable overlap of the intermediate ones in either group. A lack of standardized definition limits intergroup comparison as well. Staghorn morphometry is a recent addition to the clinical classification profiling of a staghorn calculus. It comprises extensive CT volumetric stone distribution assessment of a staghorn in a given pelvi-calyceal anatomy. It allowsmeaningful clinical classification of staghorn stones from a contemporary PCNL monotherapy perspective. Conclusions: Morphometry-based classification affords clinically relevant nomenclature in predicting the outcome of PCNL for staghorn stones. Further research is required to reduce the complexity associated with measuring the volumetric stone distribution in a given calyceal system

Bacteriological Profile and Drug Resistance Patterns of Blood Culture Isolates in a Tertiary Care Nephrourology Teaching Institute

Blood stream infections can lead to life threatening sepsis and require rapid antimicrobial treatment. The organisms implicated in these infections vary with the geographical alteration. Infections caused by MDR organisms are more likely to increase the risk of death in these patients. The present study was aimed to study the profile of organisms causing bacteremia and understand antibiotic resistance patterns in our hospital. 1440 blood samples collected over a year from clinically suspected cases of bacteremia were studied. The isolates were identified by standard biochemical tests and antimicrobial resistance patterns were determined by CLSI guidelines. Positive blood cultures were obtained in 9.2% of cases of which Gram-positive bacteria accounted for 58.3% of cases with staph aureus predominance; gram negative bacteria accounted for 40.2% with enterobactereciea predominence; and 1.5% were fungal isolates. The most sensitive drugs for Gram-positive isolates were vancomycin, teicoplanin, daptomycin, linezolid, and tigecycline and for Gram-negative were carbapenems, colistin, aminoglycosides, and tigecycline. The prevalence of MRSA and vancomycin resistance was 70.6% and 21.6%, respectively. ESBL prevalence was 39.6%. Overall low positive rates of blood culture were observed