Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanoparticle engineered TRAIL-overexpressing adipose-derived stem cells target and eradicate glioblastoma via intracranial delivery

Significance Current treatment for glioblastoma multiforme (GBM) fails to address its highly infiltrative nature; treatment often leaves behind microscopic neoplastic satellites, resulting in eventual tumor recurrence. Here we report polymeric nanoparticle-engineered human adipose-derived stem cells (hADSCs) overexpressing the cancer-specific TNF-related apoptosis-inducing ligand for targeting and eradicating glioblastoma cells. Engineered hADSCs exhibited long-range directional migration toward tumor in patient-derived GBM orthotropic xenografts and showed significant inhibition of tumor growth and extension of animal survival. Repetitive injection further prolonged animal survival compared with single injection. Together, our data suggest that nanoparticle-engineered hADSCs exhibit the therapeutically relevant behavior of “seek-and-destroy” tumortropic migration, and may offer a promising therapy for substantial enhancement of GBM treatment. Glioblastoma multiforme (GBM) is one of the most intractable of human cancers, principally because of the highly infiltrative nature of these neoplasms. Tracking and eradicating infiltrating GBM cells and tumor microsatellites is of utmost importance for the treatment of this devastating disease, yet effective strategies remain elusive. Here we report polymeric nanoparticle-engineered human adipose-derived stem cells (hADSCs) overexpressing tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) as drug-delivery vehicles for targeting and eradicating GBM cells in vivo. Our results showed that polymeric nanoparticle-mediated transfection led to robust up-regulation of TRAIL in hADSCs, and that TRAIL-expressing hADSCs induced tumor-specific apoptosis. When transplanted in a mouse intracranial xenograft model of patient-derived glioblastoma cells, hADSCs exhibited long-range directional migration and infiltration toward GBM tumor. Importantly, TRAIL-overexpressing hADSCs inhibited GBM growth, extended survival, and reduced the occurrence of microsatellites. Repetitive injection of TRAIL-overexpressing hADSCs significantly prolonged animal survival compared with single injection of these cells. Taken together, our data suggest that nanoparticle-engineered TRAIL-expressing hADSCs exhibit the therapeutically relevant behavior of “seek-and-destroy” tumortropic migration and could be a promising therapeutic approach to improve the treatment outcomes of patients with malignant brain tumors.
Gamma Interferon Mediates Experimental Cerebral Malaria by Signaling within Both the Hematopoietic and Nonhematopoietic Compartments

ABSTRACT Experimental cerebral malaria (ECM) is a gamma interferon (IFN-γ)-dependent syndrome. However, whether IFN-γ promotes ECM through direct and synergistic targeting of multiple cell populations or by acting primarily on a specific responsive cell type is currently unknown. Here, using a panel of cell- and compartment-specific IFN-γ receptor 2 (IFN-γR2)-deficient mice, we show that IFN-γ causes ECM by signaling within both the hematopoietic and nonhematopoietic compartments. Mechanistically, hematopoietic and nonhematopoietic compartment-specific IFN-γR signaling exerts additive effects in orchestrating intracerebral inflammation, leading to the development of ECM. Surprisingly, mice with specific deletion of IFN-γR2 expression on myeloid cells, T cells, or neurons were completely susceptible to terminal ECM. Utilizing a reductionist in vitro system, we show that synergistic IFN-γ and tumor necrosis factor (TNF) stimulation promotes strong activation of brain blood vessel endothelial cells. Combined, our data show that within the hematopoietic compartment, IFN-γ causes ECM by acting redundantly or by targeting non-T cell or non-myeloid cell populations. Within the nonhematopoietic compartment, brain endothelial cells, but not neurons, may be the major target of IFN-γ leading to ECM development. Collectively, our data provide information on how IFN-γ mediates the development of cerebral pathology during malaria infection.
On the origin and robustness of power-law species–area relationships in ecology

We present an explanation for the widely reported power-law species–area relationship (SAR), which relates the area occupied by a biome to the number of species that it supports. We argue that power-law SARs are a robust consequence of a skewed species abundance distribution resembling a lognormal with higher rarity, together with the observation that individuals of a given species tend to cluster. We show that the precise form of the SAR transcends the specific details of organism interactions, enabling us to characterize its broad trends across taxa.
A preliminary study for the assessment of hypertension using static and dynamic IR thermograms

Abstract Structural changes in blood vessels occur due to prolonged hypertension. Early detection of blood pressure (mm Hg) is essential for disease prevention. The aim of this work is to propose a computer-aided diagnostic (CADx) model for the diagnosis of hypertension using variables derived from non-contact static and dynamic thermal imaging in comparison with the pulse wave velocity (PWV)-derived parameters. Static and dynamic infrared (IR) thermograms of selected skin areas of the body from known hypertensive (n=14) and age- and sex-matched normal subjects were captured. The average skin surface temperature [SST (°C)] of selected skin areas of the body was calculated from a static IR thermogram. After denoising the dynamic IR thermogram using wavelets, the statistical variables power, mean, standard deviation (SD), variance, skewness and kurtosis were calculated. The variables derived from both static and dynamic thermograms were used to develop the CADx model. The performance of the CAD model was also tested by feature selection using principal component analysis (PCA). An accuracy of 75% (sensitivity=78.6%, specificity=71.4%) could be achieved with the average SST (°C) of the static IR thermogram alone. The statistical variables derived from the dynamic IR thermogram alone gave an accuracy of 82% (and 85% after feature selection by PCA), whereas the accuracy using standard methods like variables derived from PWV was only 71.4% (with and without feature selection). The highest accuracy of 89% could be achieved by combining variables like average SST (°C) measured from static and dynamic IR thermograms and PWV-derived variables.
Utility-Based Resource Allocation for Multi-Channel Decentralized Networks

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.
Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies

Background: Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This systematic review evaluates the use and reporting quality of PS analysis in oncology studies. Methods: We searched PubMed to identify the use of PS methods in cancer studies (CS) and cancer surgical studies (CSS) in major medical, cancer, and surgical journals over time and critically evaluated 33 CS published in top medical and cancer journals in 2014 and 2015 and 306 CSS published up to November 26, 2015, without earlier date limits. The quality of reporting in PS analysis was evaluated. It was also compared over time and among journals with differing impact factors. All statistical tests were two-sided. Results: More than 50% of the publications with PS analysis from the past decade occurred within the past two years. Of the studies critically evaluated, a considerable proportion did not clearly provide the variables used to estimate PS (CS 12.1%, CSS 8.8%), incorrectly included non baseline variables (CS 3.4%, CSS 9.3%), neglected the comparison of baseline characteristics (CS 21.9%, CSS 15.6%), or did not report the matching algorithm utilized (CS 19.0%, CSS 36.1%). In CSS, the reporting of the matching algorithm improved in 2014 and 2015 (P = .04), and the reporting of variables used to estimate PS was better in top surgery journals (P = .008). However, there were no statistically significant differences for the inclusion of non baseline variables and reporting of comparability of baseline characteristics. Conclusions: The use of PS in cancer studies has dramatically increased recently, but there is substantial room for improvement in the quality of reporting even in top journals. Herein we have proposed reporting guidelines for PS analyses that are broadly applicable to different areas of medical research that will allow better evaluation and comparison across studies applying this approach.
Calibrating Parameters for Microsimulation Disease Models

Background. Calibration (estimation of model parameters) compares model outcomes with observed outcomes and explores possible model parameter values to identify the set of values that provides the best fit to the data. The goodness-of-fit (GOF) criterion quantifies the difference between model and observed outcomes. There is no consensus on the most appropriate GOF criterion, because a direct performance comparison of GOF criteria in model calibration is lacking. Methods. We systematically compared the performance of commonly used GOF criteria (sum of squared errors [SSE], Pearson chi-square, and a likelihood-based approach [Poisson and/or binomial deviance functions]) in the calibration of selected parameters of the MISCAN-Colon microsimulation model for colorectal cancer. The performance of each GOF criterion was assessed by comparing the 1) root mean squared prediction error (RMSPE) of the selected parameters, 2) computation time of the calibration procedure of various calibration scenarios, and 3) impact on estimated cost-effectiveness ratios. Results. The likelihood-based deviance resulted in the lowest RMSPE in 4 of 6 calibration scenarios and was close to best in the other 2. The SSE had a 25 times higher RMSPE in a scenario with considerable differences in the values of observed outcomes, whereas the Pearson chi-square had a 60 times higher RMSPE in a scenario with multiple studies measuring the same outcome. In all scenarios, the SSE required the most computation time. The likelihood-based approach estimated the cost-effectiveness ratio most accurately (up to −0.15% relative difference versus 0.44% [SSE] and 13% [Pearson chi-square]). Conclusions. The likelihood-based deviance criteria lead to accurate estimation of parameters under various circumstances. These criteria are recommended for calibration in microsimulation disease models in contrast with other commonly used criteria.
Mutation Bias is the Driving Force of Codon Usage in the Gallus gallus genome

Synonymous codons are used with different frequencies both among species and among genes within the same genome and are controlled by neutral processes (such as mutation and drift) as well as by selection. Up to now, a systematic examination of the codon usage for the chicken genome has not been performed. Here, we carried out a whole genome analysis of the chicken genome by the use of the relative synonymous codon usage (RSCU) method and identified 11 putative optimal codons, all of them ending with uracil (U), which is significantly departing from the pattern observed in other eukaryotes. Optimal codons in the chicken genome are most likely the ones corresponding to highly expressed transfer RNA (tRNAs) or tRNA gene copy numbers in the cell. Codon bias, measured as the frequency of optimal codons (Fop), is negatively correlated with the G + C content, recombination rate, but positively correlated with gene expression, protein length, gene length and intron length. The positive correlation between codon bias and protein, gene and intron length is quite different from other multi-cellular organism, as this trend has been only found in unicellular organisms. Our data displayed that regional G + C content explains a large proportion of the variance of codon bias in chicken. Stepwise selection model analyses indicate that G + C content of coding sequence is the most important factor for codon bias. It appears that variation in the G + C content of CDSs accounts for over 60% of the variation of codon bias. This study suggests that both mutation bias and selection contribute to codon bias. However, mutation bias is the driving force of the codon usage in the Gallus gallus genome. Our data also provide evidence that the negative correlation between codon bias and recombination rates in G. gallus is determined mostly by recombination-dependent mutational patterns.
Identification and development of novel indazole derivatives as potent bacterial peptidoglycan synthesis inhibitors

Background: Tuberculosis is well-known airborne disease caused by Mycobacterium tuberculosis. Available treatment regimen was unsuccessful in eradicating the deaths caused by the disease worldwide. Owing to the drawbacks such as prolonged treatment period, side effects, and drug tolerance, there resulted in patient noncompliance. In the current study, we attempted to develop inhibitors against unexplored key target glutamate racemase. Methods: Lead identification was done using thermal shift assay from in-house library; inhibitors were developed by lead derivatization technique and evaluated using various biological assays. Results: In indazole series, compounds 11 (6.32 ± 0.35 μM) and 22 (6.11 ± 0.51 μM) were found to be most promising potent inhibitors among all. These compounds also showed their inhibition on replicating and nonreplicating bacteria. Conclusion: We have developed the novel inhibitors against M. tuberculosis capable of inhibiting active and dormant bacteria, further optimization of inhibitor derivatives can results in better compounds for eradicating tuberculosis.
Preprocedural Prediction Model for Contrast‐Induced Nephropathy Patients

Background Several models have been developed for prediction of contrast‐induced nephropathy (CIN); however, they only contain patients receiving intra‐arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure‐related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. Methods and Results A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5‐fold cross‐validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver‐operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. Conclusions The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.
Social Assistive Robot for Cardiac Rehabilitation: A Pilot Study with Patients with Angioplasty

Socially Assistive Robots (SAR) have been gaining significant attention in multiple health care applications. However, SAR has not been fully explored in cardiac rehabilitation (CR). One of the most critical issues in CR is the lack of adherence of patients to the rehabilitation process. Hence, based on the evidence that the presence of an embodied agent increases compliance, we present in this paper the integration of a social robot in a CR programme. The setup is evaluated with four patients divided into two conditions (robot and no robot), in order to evaluate its first four sessions as a preliminary study. The results show that this system might have a promising impact on CR and holds promise to be extended to a larger group of patients.
Growth of Semiconductor Nanowires for Solar Cell Applications

Nanowires have the ability to absorb light much more efficient than conventional thin film layers. This makes them candidates for the development of new types of solar cells that have higher efficiency and lower material usage than current technologies. In this thesis fabrication of nanowires with techniques suitable for large area applications are investigated. The nanowires are grown by either Metal Organic Vapor Phase Epitaxy (MOVPE) or a novel technique called Aerotaxy. When using MOVPE nanowires are nucleated on a substrate. The position of nucleation can be determined with nanoimprint lithography by which several square centimeter surfaces can be covered with nanoscale patterns. The local environment will thus be similar for all nanowires during synthesis which is a prerequisite to achieve homogenous nanowire properties. During synthesis we have developed an optical technique to monitor the growth in situ. In situ monitoring can be used to precisely control the length of different sections, increase reproducibility, and achieve a better understanding of the complex nanowire growth process. Aerotaxy grown nanowires nucleate from gold particles which are suspended in a stream of gas. This can increase the production rate substantially compared to substrate based techniques. By controlling the gold particle size and the reaction conditions in the growth chamber the nanowire properties can be tuned. By incorporating a p-n junction within each nanowire it is possible to fabricate solar cell devices. We have connected two p-n junctions in series within a single nanowire by using a tunnel junction. This type of structure can in the future be used to achieve higher efficiencies of nanowire solar cells by implementing a multi-junction structure, with several sub-cells of different absorbing material. In a radial solar cell design carriers are extracted orthogonal to the direction of incoming light. With this geometry we could compare a vertical array device with that of a single nanowire device. With the array device an efficiency of 5.3% was achieved. (Less)
Aging in the Americas: Disability-free Life Expectancy Among Adults Aged 65 and Older in the United States, Costa Rica, Mexico, and Puerto Rico

Objectives To estimate and compare disability-free life expectancy (DFLE) and current age patterns of disability onset and recovery from disability between the United States and countries in Latin America and the Caribbean. Method Disability is measured using the activities of daily living scale. Data come from longitudinal surveys of older adult populations in Costa Rica, Mexico, Puerto Rico, and the United States. Age patterns of transitions in and out of disability are modeled with a discrete-time logistic hazard model, and a microsimulation approach is used to estimate DFLE. Results Overall life expectancy for women aged 65 is 20.11 years in Costa Rica, 19.2 years in Mexico, 20.4 years in Puerto Rico, and 20.5 years in the United States. For men, these figures are 19.0 years in Costa Rica, 18.4 years in Mexico, 18.1 years in Puerto Rico, and 18.1 years in the United States. Proportion of remaining life spent free of disability for women at age 65 is comparable between Mexico, Puerto Rico, and the United States, with Costa Rica trailing slightly. Male estimates of DFLE are similar across the four populations. Discussion Though the older adult population of Latin America and the Caribbean lived many years exposed to poor epidemiological and public health conditions, their functional health in later life is comparable with the older adult population of the United States.
Predicting urinary tract infections in the emergency department with machine learning

Background Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24–48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools. Objective Our aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients. Methods Single-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration). Results The machine learning models had an area under the curve ranging from 0.826–0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3–34.3) and 29.6 (28.5–30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1–39.4) and 33.2 (32.5–33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive. Conclusion The best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.