Search

To help guide empiric therapy for kidney stone disease, we sought to demonstrate the feasibility of predicting 24-hour urine abnormalities using machine learning methods.We trained a machine learning model (XGBoost [XG]) to predict 24-hour urine abnormalities from electronic health record-derived data (n = 1314). The machine learning model was compared to a logistic regression model [LR]. Additionally, an ensemble (EN) model combining both XG and LR models was evaluated as well. Models predicted binary 24-hour urine values for volume, sodium, oxalate, calcium, uric acid, and citrate; as well as a multiclass prediction of pH. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified predictors for each model.The XG model was able to discriminate 24-hour urine abnormalities with fair performance, comparable to LR. The XG model most accurately predicted abnormalities of urine volume (accuracy = 98%, AUC-ROC = 0.59), uric acid (69%, 0.73) and elevated urine sodium (71%, 0.79). The LR model outperformed the XG model alone in prediction of abnormalities of urinary pH (AUC-ROC of 0.66 vs 0.57) and citrate (0.69 vs 0.64). The EN model most accurately predicted abnormalities of oxalate (accuracy = 65%, ROC-AUC = 0.70) and citrate (65%, 0.69) with overall similar predictive performance to either XG or LR alone. Body mass index, age, and gender were the three most important features for training the models for all outcomes.Urine chemistry prediction for kidney stone disease appears to be feasible with machine learning methods. Further optimization of the performance could facilitate dietary or pharmacologic prevention.

Background: Trigger point is a extremely irritable local spot of exquisite tenderness in the nodule within the tangible taut muscle band. The prevalence studies have shown that the occurrence of myofascial trigger point in the general population. Objective: The aim of the study was compare the effects of low level laser therapy( LLLT) Vs ultrasound therapy in the management of active trapezius trigger point. Methodology: The participants will be allocated into two groups using simple random sampling. One group has to be given Low level laser therapy (LLLT) and Moist Heat and other group treated with US and Moist Heat. Both group receive treatment for 3 times a week. Total number of 9 session has to be given in 21 days. The outcome measure has to be taken at the first day and end of the day. Conclusion: Based on the above results we conclude that Low Level Laser Therapy can be used as a therapeutic device in the management of Active Trapezius Trigger points.

Objectives: To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR). Materials and Methods: We identified kidney stone patients (n = 1296) with both stone composition and 24-hour (24H) urine testing. We trained machine learning models (XGBoost [XG] and logistic regression [LR]) to predict stone composition using 24H urine data and EHR-derived demographic and comorbidity data. Models predicted either binary (calcium vs noncalcium stone) or multiclass (calcium oxalate, uric acid, hydroxyapatite, or other) stone types. We evaluated performance using area under the receiver operating curve (ROC-AUC) and accuracy and identified predictors for each task. Results: For discriminating binary stone composition, XG outperformed LR with higher accuracy (91% vs 71%) with ROC-AUC of 0.80 for both models. Top predictors used by these models were supersaturations of uric acid and calcium phosphate, and urinary ammonium. For multiclass classification, LR outperformed XG with higher accuracy (0.64 vs 0.56) and ROC-AUC (0.79 vs 0.59), and urine pH had the highest predictive utility. Overall, 24H urine analyte data contributed more to the models' predictions of stone composition than EHR-derived variables. Conclusion: Machine learning models can predict calcium stone composition. LR outperforms XG in multiclass stone classification. Demographic and comorbidity data are predictive of stone composition; however, including 24H urine data improves performance. Further optimization of performance could lead to earlier directed medical therapy for kidney stone patients.

Summary GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL’s approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. Availability and implementation GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. Supplementary information Supplementary data are available at Bioinformatics online.

Cancer cells characteristically consume glucose through Warburg metabolism1, a process that forms the basis of tumour imaging by positron emission tomography (PET). Tumour-infiltrating immune cells also rely on glucose, and impaired immune cell metabolism in the tumour microenvironment (TME) contributes to immune evasion by tumour cells2–4. However, whether the metabolism of immune cells is dysregulated in the TME by cell-intrinsic programs or by competition with cancer cells for limited nutrients remains unclear. Here we used PET tracers to measure the access to and uptake of glucose and glutamine by specific cell subsets in the TME. Notably, myeloid cells had the greatest capacity to take up intratumoral glucose, followed by T cells and cancer cells, across a range of cancer models. By contrast, cancer cells showed the highest uptake of glutamine. This distinct nutrient partitioning was programmed in a cell-intrinsic manner through mTORC1 signalling and the expression of genes related to the metabolism of glucose and glutamine. Inhibiting glutamine uptake enhanced glucose uptake across tumour-resident cell types, showing that glutamine metabolism suppresses glucose uptake without glucose being a limiting factor in the TME. Thus, cell-intrinsic programs drive the preferential acquisition of glucose and glutamine by immune and cancer cells, respectively. Cell-selective partitioning of these nutrients could be exploited to develop therapies and imaging strategies to enhance or monitor the metabolic programs and activities of specific cell populations in the TME. Positron emission tomography measurements of nutrient uptake in cells of the tumour microenvironment reveal cell-intrinsic partitioning in which glucose uptake is higher in myeloid cells, whereas glutamine is preferentially acquired by cancer cells.

Currently, there is no comprehensive framework to evaluate the evolutionary forces acting on genomic regions associated with human complex traits and contextualize the relationship between evolution and molecular function. Here, we develop an approach to test for signatures of diverse evolutionary forces on trait-associated genomic regions. We apply our method to regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including conservation, excess population differentiation, accelerated evolution, and balanced polymorphism. Furthermore, we integrate evolutionary context with molecular evidence to hypothesize how these regions contribute to sPTB risk. Finally, we observe enrichment in signatures of diverse evolutionary forces in sPTB-associated regions compared to genomic background. By quantifying multiple evolutionary forces acting on sPTB-associated regions, our approach improves understanding of both functional roles and the mosaic of evolutionary forces acting on loci. Our work provides a blueprint for investigating evolutionary pressures on complex traits., There is a need for analytical frameworks to investigate the evolution of complex genetic traits. Here, the authors develop an approach to simultaneously evaluate different evolutionary signatures on trait-associated genetic variants for complex traits and apply it to study spontaneous preterm birth.

OBJECTIVE: To determine the impact of autoimmunity in the absence of glycemic alterations on pregnancy in type 1 diabetes (T1D). DESIGN: Because nonobese diabetic (NOD) mice experience autoimmunity before the onset of hyperglycemia, we studied pregnancy outcomes in prediabetic NOD mice using flow cytometry and enzyme-linked immunosorbent assays. Once we determined that adverse events in pregnancy occurred in euglycemic mice, we performed an exploratory study using electronic health records to better understand pregnancy complications in humans with T1D and normal hemoglobin A1c levels. SETTING: University Medical Center. PATIENT(S)/ANIMAL(S): Nonobese diabetic mice and electronic health records from Vanderbilt University Medical Center. INTERVENTION(S): Nonobese diabetic mice were administered 200 μg of an anti-interleukin 6 (IL-6) antibody every other day starting on day 5 of gestation. MAIN OUTCOME MEASURE(S): Changes in the number of abnormal and reabsorbed pups in NOD mice and odds of vascular complications in pregnancy in T1D in relation to A1c. RESULT(S): Prediabetic NOD mice had increased adverse pregnancy outcomes compared with nonautoimmune mice; blockade of IL-6, which was secreted by endothelial cells, decreased the number of reabsorbed and abnormal fetuses. Similarly, vascular complications were increased in pregnant patients with T1D across all A1c values. CONCLUSION(S): The vascular secretion of IL-6 drives adverse pregnancy outcomes in prediabetic NOD mice. Pregnant patients with T1D have increased vascular complications even with normal hemoglobin A1cs, indicating a potential effect of autoimmunity on the placental vasculature.

Although non-barrier contraception is commonly prescribed, the risk of urinary tract infections (UTI) with contraceptive exposure is unclear. Using data from Vanderbilt University Medical Centre’s deidentified electronic health record (EHR), women ages 18–52 were randomly sampled and matched based on age and length of EHR. This case-control analysis tested for association between contraception exposure and outcome using UTI-positive (UTI+) as cases and upper respiratory infection+ (URI+) as controls. 24,563 UTI + cases (mean EHR: 64.2 months; mean age: 31.2 years) and 48,649 UTI-/URI + controls (mean EHR: 63.2 months; mean age: 31.9 years) were analysed. In the primary analysis, UTI risk was statistically significantly increased for the oral contraceptive pill (OCP; OR = 1.10 [95%CI = 1.02–1.11], p ≤ 0.05), intrauterine device (IUD; OR = 1.13 [95%CI = 1.04–1.23], p ≤ 0.05), etonogestrel implant (Nexplanon®; OR = 1.56 [95% CI = 1.24–1.96], p ≤ 0.05), and medroxyprogesterone acetate injectable (Depo-Provera®; OR = 2.16 [95%CI = 1.99–2.33], p ≤ 0.05) use compared to women not prescribed contraception. A secondary analysis that included any non-IUD contraception, which could serve as a proxy for sexual activity, demonstrated a small attenuation for the association between UTI and IUD (OR = 1.09 [95%CI = 0.98–1.21], p = 0.13). This study notes potential for a small increase in UTIs with contraceptive use. Prospective studies are required before this information is applied in clinical settings. Although non-barrier contraception is commonly prescribed, the risk of urinary tract infections (UTI) with contraceptive exposure is poorly understood. This large-cohort, case-control study notes potential for a small increase in UTIs with contraceptive use.

INTRODUCTION AND OBJECTIVE:To enable earlier preventative interventions or guide empiric therapy for kidney stone disease, our objective was to demonstrate feasibility of predicting 24-hour urine a...

Background The intrauterine device (IUD) as a potential source of uro-gynecologic infection has raised concern for decades. While a causal link between IUD and pelvic inflammatory disease has been refuted, the relationship between IUDs and urinary tract infections (UTIs) remains incompletely understood. Methods We used a PubMed, CINAHL, and Cochrane Library search strategy to identify studies evaluating UTI occurrence and microbial signatures among women exposed to IUD. We evaluated the question, "what is currently known about the IUD as an exposure risk for UTIs?" Results Nine studies met inclusion criteria and were summarized in this structured, scoping review. Studies to date have not reported a significant association between IUD exposue and UTI occurence. While all nine studies acknowledged the breadth of contraceptive methods, none evaluated the impact of different IUD types (i.e., copper vs. hormone-eluting) on UTI incidence. Conclusion Small sample sizes and inconsistent UTI definitions limit the current literature. Future studies should rigorously define the UTI phenotype and evaluate the association of UTI with IUD exposure accounting for known covariates.

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential., Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.

There is general agreement in the literature that self-determination is to be considered as a right of all individuals, at least in a democratic society. It is also a helping technique because helping the client to solve a problem for himself gives him increased confidence in his ability to solve additional problems as they arise. The overlapping of confidentiality and self-determination principles are discussed in the review of previous studies. In this monograph, importance of client self-determination in the field (micro and mezzo) of social work is studied. This study found that, Self-determination is, however, not absolute and should be limited when it may be harmful to the individual or to the rights of others in a social work settings., IJSRED - International Journal of Scientific Research and Engineering Development

Although obesity can promote cancer, it may also increase immunotherapy efficacy in what has been termed the obesity-immunotherapy paradox. Mechanisms of this effect are unclear, although obesity alters key inflammatory cytokines and can promote an inflammatory state that may modify tumor-infiltrating lymphocytes and tumor-associated macrophage populations. To identify mechanisms by which obesity affects antitumor immunity, we examined changes in cell populations and the role of the proinflammatory adipokine leptin in immunotherapy. Single-cell RNAseq demonstrated that obesity decreased tumor-infiltrating lymphocyte frequencies, and flow cytometry confirmed altered macrophage phenotypes with lower expression of inducible NO synthase and MHC class II in tumors of obese animals. When treated with anti-programmed cell death protein 1 (PD-1) Abs, however, obese mice had a greater absolute decrease in tumor burden than lean mice and a repolarization of the macrophages to inflammatory M1-like phenotypes. Mechanistically, leptin is a proinflammatory adipokine that is induced in obesity and may mediate enhanced antitumor immunity in obesity. To directly test the effect of leptin on tumor growth and antitumor immunity, we treated lean mice with leptin and observed tumors over time. Treatment with leptin, acute or chronic, was sufficient to enhance antitumor efficacy similar to anti-PD-1 checkpoint therapy. Further, leptin and anti-PD-1 cotreatment may enhance antitumor effects consistent with an increase in M1-like tumor-associated macrophage frequency compared with non–leptin-treated mice. These data demonstrate that obesity has dual effects in cancer through promotion of tumor growth while simultaneously enhancing antitumor immunity through leptin-mediated macrophage reprogramming.

The tumor microenvironment (TME) includes transformed cancer and infiltrating immune cells1,2. Cancer cells can consume large quantities of glucose through Warburg metabolism3,4 that can be visualized with positron emission tomography (PET). While infiltrating immune cells also rely on glucose, disruptions to metabolism can contribute to tumor immunological evasion5–9. How immune cell metabolism is programmed or restrained by competition with cancer cells for nutrients, remains uncertain. Here we used PET tracers to measure the accessibility of glucose and glutamine to cell subsets in the TME. Surprisingly, myeloid cells including macrophages were the greatest consumers of intra-tumoral glucose, followed by T cells and cancer cells. Cancer cells, in contrast, had the highest glutamine uptake. This distinct nutrient partitioning was programmed through selective mTORC1 signaling and glucose or glutamine-related gene expression. Inhibition of glutamine uptake enhanced glucose uptake across tumor resident cell types and shifted macrophage phenotype, demonstrating glucose is not limiting in the TME. Thus, cancer cells are not the only cells in tumors which exhibit high glucose uptake in vivo and instead preferentially utilize glutamine over other cell types. We observe that intrinsic cellular programs can play a major role in the use of some nutrients. Together, these data argue cell selective partitioning of glucose and glutamine can be exploited to develop therapies and imaging strategies to alter the metabolic programs of specific cell populations in the TME.

Cancer cells characteristically consume glucose through Warburg metabolism

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC=0.75, PR-AUC=0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC=0.65, PR-AUC=0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth sub-types enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) from a different healthcare system. By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy.

Genetic variation in the membrane trafficking adapter protein complex 4 (AP-4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP-4 function arising from genetic variation, because AP-4 remains poorly understood structurally. Here, we analyze patterns of AP-4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation. We map known variants onto an AP-4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP-4. We find significant clustering of likely pathogenic variants located at the interface between the β4 and N-μ4 subunits, as well as throughout the C-μ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP-4 structure and function. As more individuals with uncharacterized AP-4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted.

Sex and sexual differentiation are pervasive across the tree of life. Because females and males often have substantially different functional requirements, we expect selection to differ between the sexes. Recent studies in diverse species, including humans, suggest that sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci. However, theory and population-level simulations indicate that sex-specific differences in viability would need to be very large to produce and maintain reported levels of between-sex allelic differentiation. We address this contradiction between theoretical predictions and empirical observations by evaluating evidence for sexually antagonistic viability selection on autosomal loci in humans using the largest cohort to date (UK Biobank, n = 487,999) along with a second large, independent cohort (BioVU, n = 93,864). We performed association tests between genetically ascertained sex and autosomal loci. Although we found dozens of genome-wide significant associations, none replicated across cohorts. Moreover, closer inspection revealed that all associations are likely due to cross-hybridization with sex chromosome regions during genotyping. We report loci with potential for mis-hybridization found on commonly used genotyping platforms that should be carefully considered in future genetic studies of sex-specific differences. Despite being well powered to detect allele frequency differences of up to 0.8% between the sexes, we do not detect clear evidence for this signature of sexually antagonistic viability selection on autosomal variation. These findings suggest a lack of strong ongoing sexually antagonistic viability selection acting on single locus autosomal variation in humans.

Human pregnancy requires the coordinated function of multiple tissues in both mother and fetus and has evolved in concert with major human adaptations. As a result, pregnancy-associated phenotypes and related disorders are genetically complex and have likely been sculpted by diverse evolutionary forces. However, there is no framework to comprehensively evaluate how these traits evolved or to explore the relationship of evolutionary signatures on trait-associated genetic variants to molecular function. Here we develop an approach to test for signatures of diverse evolutionary forces, including multiple types of selection, and apply it to genomic regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including evolutionary sequence conservation (consistent with the action of negative selection), excess population differentiation (local adaptation), accelerated evolution (positive selection), and balanced polymorphism (balancing selection). Furthermore, these genomic regions show diverse functional characteristics which enables us to use evolutionary and molecular lines of evidence to develop hypotheses about how these genomic regions contribute to sPTB risk. In summary, we introduce an approach for inferring the spectrum of evolutionary forces acting on genomic regions associated with complex disorders. When applied to sPTB-associated genomic regions, this approach both improves our understanding of the potential roles of these regions in pathology and illuminates the mosaic nature of evolutionary forces acting on genomic regions associated with sPTB.

The tumor microenvironment (TME) includes cancer and infiltrating immune cells. Tumors canonically consume glucose through Warburg metabolism, a process forming the basis of cancer imaging by positron emission tomography (PET). Activated immune cells also rely on glucose, and impaired immune cell metabolism in the TME contributes to tumor progression. It remains uncertain, however, if immune cell metabolism is dysregulated in the TME by cell intrinsic programs or by competition with cancer cells for limiting nutrients. Here we used PET tracers to measure access and uptake of glucose and glutamine by specific cell subsets in the TME. Surprisingly, myeloid cells had the greatest capacity to uptake glucose in vivo, followed by T cells and cancer cells across a range of cancer models. Cancer cells, in contrast, demonstrated high glutamine uptake. This distinct nutrient partitioning was cell intrinsically programmed through mTORC1 signaling and glucose and glutamine-related gene expression. Inhibiting glutamine uptake enhanced glucose uptake across tumor resident cell types, suggesting that glutamine metabolism suppresses glucose uptake without glucose being limiting in the TME. Thus, cell intrinsic programs dictate the preferential immune and cancer cell acquisition of glucose and glutamine. Cell selective partitioning of these nutrients may be exploited to develop therapies and imaging strategies to enhance or monitor the metabolism and activities of specific cell populations in the TME.

Background: Erythroderma is defined as generalized erythema and scaling of the skin affecting more than 90% of body surface area. Identification of the underlying disease process represents one of the most complex challenges in proper patient care.Methods: A retrospective study was done in Department of Dermatology in a Tertiary Care Centre. History, clinical findings and investigations of erythroderma patients were recorded and clinic-histopathological correlation was analyzed by kappa coefficient (К).Results: Erythroderma was more prevalent in elderly males with a mean age of 64.56 years and a male to female ratio of 3:1. A clinical evidence of pre-existing dermatoses was found in 65 patients, commonest being eczema (41.3%) followed by psoriasis (40.3%). Evidence of a trigger was seen in 54.54% patients, commonest being the use of ayurvedic medications (42.8%). Clinico-histopathological correlation was seen in 53.9% cases.Conclusions: Although the clinical presentation of erythroderma is similar, etiological factors are varied and it depends largely on the population studied. Most commonly, erythroderma is due to generalization of pre-existing dermatoses as seen in our study. Hence careful evaluation of clinical clues and histopathological correlation plays a pivotal role in diagnosis of the primary cause and the effective management of erythroderma.

Conventional immunostaining methods consume large quantities of expensive antibodies and are limited in terms of the number of antigens that can be detected from a single sample. In order to achieve multiplexed immunostaining, we micropatterned antibodies using aqueous two-phase systems (ATPS) formed from polyethylene glycol (PEG) and dextran. Multiple antigens can be detected on a single fixed sample by incorporating antibodies within dextran solutions, which are then patterned by micropipetting at specific sites on the sample in a solution of PEG. The antibodies are retained within the dextran phase due to biomolecular partitioning, allowing multiple protein markers to be visualized simultaneously by way of chromogenic, chemiluminescent, or immunofluorescent detection. This aqueous two-phase system-mediated antibody micropatterning approach allows antibody dilutions to be easily optimized, reduces the consumption of expensive primary antibodies and can prevent antibody cross-reactions, since the antibodies are retained at separate sites within the dextran microdroplets.

Face is considered to be one of the most important visual objects for identification. Recognition of h uman face is complex and it converts the face into a mathematical model. Face r ecognition is the most efficient and sophisticated method for the security systems. It is a biometric technology with a wide range of a pplications such as use in ATM machines, preventing voter’s fraud, criminal identification, human computer interaction, etc. Th is paper describes the building of a face recogniti on system by using Principal Component Analysis method. PCA is the method for re duce the data dimension of the image. It is based o n the approach that breaks the face images into a small set of characteristic feature images. These“eigenfaces” are the principal components of the initial data set of face images. Recognition is done by comparin g the input face image with the faces in the data s et through distance measuring methods. Here the face recognition system is develo ped using Matlab and it recognizes the input face f rom a set of training faces.

In recent years, the popularity for natural interfaces has experienced tremendous growth. Natural interfaces provide the computers or the so called machines with a sense of humanity. The basic knowledge required to understand and utilize a computer system is reduced to a minimal extent. There have been recent commercial developments in the area of natural interfaces, including the Microsoft XBOX Kinect and Nintendo WII. However, the capital cost of such a system remains very high. The primary intention of this paper is to design a technique to redesign every aspect of our interaction with the computer. It explores and develops an interface that helps simple computer systems to support gestural interface. We could utilize the low cost cameras available with the laptop or desktop to support detection of human gestures and multiple touch experiences, which would ultimately lead to the direct interaction of computers with humans. In such a system, the need for external input devices like mouse and keyboard could be eliminated. The paper discusses how such a system can be implemented, along with a brief description of the approach used.

Background: Levofloxacin is a broad spectrum anti-infective agent, which is rapidly and completely absorbed after oral administration. The half life of Levofloxacin is 6-8 h after conventional dosing. The objective of the present work was to develop Levofloxacin nanoparticles to retain the dosage form in the absorption site more than the half life of the drug, enhance the bioavailability of drugs, reduce dose frequency, toxicity and patient compliance. Methods: The compositions of different formulations of Levofloxacin nanoparticles by the ionic gelation method using biodegradable polymer chitosan and tripolyphosphate as cross linking agent. Result and Discussion: The particle size lies of the prepared nanoparticles between 199 and 369 nm and the drug content found between 51.13± 0.28 and 71.12 ± 0.14 %. The particle size of nanoparticles increased with increasing concentration of polymer matrix density and this may be due to the increased viscosity of the inner phase and decreased with increasing concentration of β-cyclodextrin. Scanning electron microscopy indicated that the prepared nanoparticles were discrete, uniform and spherical with a smooth surface. The in vitro release showed that the drug release from the prepared nanoparticles was characterized by an initial fast release and followed by a delayed release phase. During and at the end of the accelerated stability study, the tested formulation showed almost same drug content, in vitro drug release and no colour changes were observed from that observed at the opening of the study. Conclusion: Among all the formulations (GIA, GIB, GIC, GID, GIE and GIF), the formulations G1C, G1E and G1F followed the drug release in a controlled manner. The in vitro release profile showed that this is a potential drug delivery for Levofloxacin and has to confirm in the in vivo settings as a separate investigation in future. Key words: Controlled drug delivery, In vitro drug release, Nanoparticle, Particle size, Stability studies, Surface morphology

Cell patterning technologies that are fast, easy to use and affordable will be required for the future development of high throughput cell assays, platforms for studying cell-cell interactions and tissue engineered systems. This detailed protocol describes a method for generating co-cultures of cells using biocompatible solutions of dextran (DEX) and polyethylene glycol (PEG) that phase-separate when combined above threshold concentrations. Cells can be patterned in a variety of configurations using this method. Cell exclusion patterning can be performed by printing droplets of DEX on a substrate and covering them with a solution of PEG containing cells. The interfacial tension formed between the two polymer solutions causes cells to fall around the outside of the DEX droplet and form a circular clearing that can be used for migration assays. Cell islands can be patterned by dispensing a cell-rich DEX phase into a PEG solution or by covering the DEX droplet with a solution of PEG. Co-cultures can be formed directly by combining cell exclusion with DEX island patterning. These methods are compatible with a variety of liquid handling approaches, including manual micropipetting, and can be used with virtually any adherent cell type.

Objective To assess the ability of urinary acute kidney injury biomarkers and renal near-infrared spectroscopy (NIRS) to predict outcomes in infants after surgery for congenital heart disease. Methods Urinary levels of neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), and cystatin C were measured preoperatively and postoperatively in 49 infants younger than 6 months of age. Renal NIRS was monitored for the first 24 hours after surgery. A composite poor outcome was defined as death, the need for renal replacement therapy, prolonged time to first extubation, or prolonged intensive care unit length of stay. Results Forty-two (86%) patients had acute kidney injury as indicated by at least Acute Kidney Injury Network/Kidney Disease: Improving Global Outcomes (AKIN/KDIGO) stage 1 criteria, and 17 (35%) patients had poor outcomes, including 3 deaths. With the exception of KIM-1, all biomarkers demonstrated significant increases within 24 hours postoperatively among patients with poor outcomes. Low levels of NGAL and IL-18 demonstrated high negative predictive values (91%) within 2 hours postoperatively. Poor outcome infants had greater cumulative time with NIRS saturations less than 50% (60 vs 1.5 minutes; P = .02) in the first 24 hours. Conclusions Within the first 24 hours after cardiopulmonary bypass, infants at increased risk for poor outcomes demonstrated elevated urinary NGAL, IL-18, and cystatin C and increased time with low NIRS saturations. These findings suggest that urinary biomarkers and renal NIRS may differentiate patients with good versus poor outcomes in the early postoperative period, which could assist clinicians when counseling families and inform the development of future clinical trials.