பயோமெட்ரிக்ஸ் & பயோஸ்டாடிஸ்டிக்ஸ் ஜர்னல்

Extended Spectrum Beta lactamases (ESBLs) are special enzymes which are found in groups and can hydrolyze the third generation cephalosporins. Their Production is the commonest cause of resistance to beta-lactam antibacterial agents among Gram-negative bacteria. There is spread of drug resistance among food animals due to the use of antimicrobials. Pathogens develop resistance in their animal reservoirs and then these resistant strains are transmitted to humans where they may cause infections which are difficult to treat. In this study the antimicrobial resistance due to ESBL producing coliforms in humans and in edible animal meat was examined. The main objective of this study was to determine the fact that a substantial increase in antimicrobial resistance posed by ESBL producing coliforms in humans is due to edible animal meat. Various chicken, meat and beef samples were collected from different sites in Faisalabad. The samples were enriched in Trypticase Soy Broth and were streaked on Nutrient Agar then on MacConkey Agar to check the presence of Gram negative bacteria. Gram Negative isolates were isolated by gram staining and biochemical test (Triple Sugar Iron; TSI). Antibiotic susceptibility testing was performed through disc diffusion method. In which 28% isolates were resistant to Cefixime, 20% to Ceftriaxone, 24% to Clavulanic acid and 30% to Cefotaxime. As the next step, a set of PCR was optimized for amplification of major genes of ESBL conferring resistance to selected antibiotics. As a result 20% Bla (CTX-M), 4% Bla(OXA), 2% Bla(PER) and 1% Bla(Ges) were amplified through PCR. These are the major genes of ESBL which are going to become more and more resistant towards the first, second and third generation cephalosporins.

The aim of the present study is to construct confidence intervals for the shape and the scale parameters of the twoparameter Gamma Distribution. Using the square error loss function, closed form approximate Bayesian confidence intervals are derived. Numerical results show that the obtained Approximate Bayesian models rely only on the observations under study and have great coverage accuracy.

Gaucher disease is a hereditary genetic abnormality which defects the pathway of sphingolipid catabolism. The mutation of GBA gene which encodes lysosomal β-glucosidase enzyme is the main characteristics of the disease also is observed in different cancer types. To find the relation between the disease and colon adenocarcinoma, the responsible gene expression of Gaucher disease was analyzed. The gene expression of colon adenocarcinoma was was compared between death and alive patients and analyzed statistically to profile the differences between Gaucher disease genes expression changes. GBA, GBA2, GBA3, SCARB2 and PSAP have the maximum genetic alteration which is observed in colon adenocarcinoma.

Objectives: Future outcomes of computed tomography in lung cancer screening were evaluated using recently derived probability formula in the disease progressive model, and the recently completed National Lung Screening Trial computed tomography (NLST-CT) data.
Methods: Every participant in a screening program would fall into one of the four disjoint groups eventually: symptom-free-life, no-early-detection, true-early-detection and overdiagnosis, depending on whether he/she would be diagnosed with cancer and whether symptoms would have appeared before death. The probability of each outcome was a function of an individual’s current age, past and future screening frequency and the three key parameters: screening sensitivity, sojourn time and time in the disease-free state. The predictive probability was estimated for people with and without screening histories. Percentage of over-diagnosis among the screen-detected cases was also presented with human lifetime as a random variable.
Results: The probability of heavy smokers to live a lung-cancer-free life would depend on their current age; it was about 80%, 86% and 94% for the 60, 70, and 80 years old respectively. The probabilities of no-early detection and true-early-detection were determined by the future screening interval and the current age: the probability of no-earlydetection would increase to about three times if the future screening interval changes from annual to biennial; while the probability of true-early-detection would decrease to about 75% if the future screening interval changes from annual to biennial. The probability of over-diagnosis among the screen-detected was increasing as people aging: ~3%, 5% and 9% for the 60, 70, and 80 years old correspondingly; this probability decreases slightly when the historic screening interval increases.
Conclusion: This research provided the estimated probabilities of the four outcomes in the future and the percentage of overdiagnosis among the screen-detected cases. It provided a practical approach on the evaluation of long-term outcomes via CT in lung cancer screening.

Mixed-lineage leukemia (MLL) is a subtype of acute myeloid leukemia with more aggressive prognosis than other subtypes. Translocations of MLL gene with other partner genes, forming the MLL-fusion proteins (MLL-FPs), are the main characteristics of MLL leukemia. Many studies have demonstrated that MLL-FPs such as: MLL-AF4, MLL-AF6, MLL-AF9, MLL-AF10, MLL-ENL, MLL-ELL, MLL-EPS15, as well as partial tandem duplication are the most common abnormalities that play significant role in MLL-rearranged leukemia. Gene expression profiles from 197 patients and 180 clinical data were downloaded from TCGA database. R statistical program has classified clinical and genomic data simultaneously according to cytogenetic abnormalities. As a result of this analysis, the most frequent 47 MLLFPs genes expression have been detected and compared with other cytogenetic abnormalities such as t(4;11), t(9;11), t(8;21), t(15;17), complex, inversion 16, trisomy 8 and cytogenetically normal AML. 35 out of 46 MLL-FPs genes presented with abnormal gene expression profile. This study showed that MLL-FPs are not just active and related with MLL, but also with other subtypes of AML.

The capabilities of the human body motion seem endless, through the long evolutionary process. The progresses made from the first step of a baby to an Olympic performance suggest that human movements have attained perfection in their specialized functions. However, the ability to predict how the whole body will move and how it will exchange forces with environment is becoming very vital for performances optimization or development of devices or safety; particularly in the fields of research of sport sciences, ergonomics, safety, clinical sciences and industries. Modeling human body motion is a huge issue due to the requirement of multifaceted researches obviously extremely diverse to apply. Indeed, they require the understanding of internal/external biological and physical principles that make possible and guide human movement and coordination, as well as, the capacity of giving them a realistic representation with high-fidelity. Since over 30 years of research Biomechanics, the research area studying human motion has undertaken progress in the modeling human motion. But the results are mitigated. The purpose of this review is to report the state of knowledge and progress of the biomechanics regarding its application to the field of sport.

The use of high dimensional linear models is common in large database settings. The linearity of such models is often assumed. In sparse settings with the number of subjects (n) less than the number of variables (p) standard algorithms include the lars-LASSO approach which often provides stable convergence. In some cases the underlying data may be more appropriately modeled with a nonlinear model. The use of a linear model in such cases creates model mis-specification and instability for lars-LASSO based approaches. This is studied by using simulations with various relative sample sizes, correlation structures and error distributions.

Recently, Big Data science has been a hot topic in the scientific, industrial and the business worlds. The healthcare and biomedical sciences have rapidly become data-intensive as investigators are generating and using large, complex, high dimensional, and diverse domain specific datasets. This paper provides a general survey of recent progress and advances in Big Data science, healthcare, and biomedical research. Big Data science impacts, important features, infrastructures, and basic and advanced analytical tools are presented in detail. Additionally, various challenges, debates, and opportunities inside this quickly emerging scientific field are explored. The human genome research, one of the most promising medical and health areas as an example and application of Big Data science, is discussed to demonstrate how the adaptive advanced computational analytical tools could be utilized for transforming millions of data points into predictions and diagnostics for precision medicine and personalized healthcare with better patient outcomes.

The authors of this article developed new approaches to present analytic results from mixed-effects binary logit models in longitudinal data analysis. We first described basic specifications of mixed-effects logit models, the derivation of the fixed and the random effects, and nonlinear predictions of the response probability and the corresponding standard errors. Particular attention was paid to the interpretability of the conventional odds ratio in the longitudinal setting. The authors contended that without information on averaging of the random effects for two population subgroups of interest, the regression coefficient of an explanatory variable and its antilog in mixed-effects binary logit models are not interpretable. We recommended the computation of the conditional effect and the conditional odds ratio to aid in displaying a covariate’s effect on the longitudinal binary response. An empirical illustration was provided to demonstrate how to create interpretable summary measures for aiding in the interpretation of the results from mixed-effects logit models when analyzing binary longitudinal data.

Since the release of FDA draft guidance on adaptive design (2010), adaptive randomization (e.g. responseadaptive (RA) randomization) has become popular in clinical research community because of its flexibility and efficiency improvement, which also have the patient centric advantage of assigning fewer patients to inferior treatment arms. The RA design based on binary outcome is commonly used in clinical trial where “success” is defined as the desired (or undesired) event occurring within (or beyond) a clinical relevant time. As patients entering into trial sequentially, only part of patients have sufficient follow-up during interim analysis. This results in a loss of information as it is unclear how patients without sufficient follow-up should be handled. Alternatively, adaptive design for survival trial was proposed for this type of trial. However, most of current practice assumes the event times following a pre-specified parametric distribution. We adopt a nonparametric model of survival outcome which is robust to model of event time distribution, and then apply it to response-adaptive design. The operating characteristics of the proposed design along with parametric design are compared by simulation studies, including their robustness properties with respect to model misspecifications.

In clinical trials, it is unethical to use a placebo control in treating patients with severe or life-threatening diseases such as cancer when approved and effective therapies (e.g., standard of care or active control agents) are available. Alternatively, an active control or non-inferiority trial is often considered. In practice, one of the key issues for a noninferiority trial is the determination of non-inferiority margin which has an impact on the power analysis for sample size calculation. In its 2010 draft guidance, the United States Food and Drug Administration (FDA) recommend a couple of margins for testing non-inferiority of a test treatment as compared to an active control agent or a standard of care treatment. In this article, several margins, which not only adjust for variability associated with the observed data but also take into consideration of the retention rate of the treatment effect, are proposed.

Big data is created every day by the interactions of billions of people using computers, GPS devices, cell phones, censors and medical devices, data-intensive areas such as atmospheric science, genome research and , astronomical studies. Today big data opens huge opportunities to those who can use it effectively. Now realizing the great importance of big data, many analytical companies are engaged in finding hidden information in big data. According to internet experts the present technological advances to collect and analyze massive sets of data is likely to lead to revolutionary changes in business, and society. Till date a lot of work has been done on the tools, software, platforms, analytics etc. applied to big data. Many organizations are giving attention in big data analytics for development, education, disaster management, health care, and natural resource management for benefit of society. Therefore, it is attempted to compile and document the real use cases, benefits, advantages, impact and future challenges of big data. UN Global Pulse has worked on several research projects in collaboration with public and private partners demonstrating the beneficial effect of analytics from monitoring early indicators of unemployment hikes to tracking fluctuations of commodity prices before they are recorded in official statistics. According to thought leaders big data is already showing the potential in areas as genetic mapping and personalized e-commerce. The unprecedented growth in processing power and software technologies such as Hadoop, are allowing organizations “to make decisions that simply could not be made before” having profound impact. Its influence is felt in business planning, research, sales, production and elsewhere. These are considered as new industrial revolution. Scientists used to decode human DNA in minutes, find cures for cancer, accurately predict human behaviour, optimise marketing efforts, prevent diseases and foil terrorist attacks, utilising big data. Finally, concerns about privacy expressed by experts cannot be ignored. As many companies use our private information. The presentation is focused on how Big Data Analytics impact health care and Society.