Miracle-dz/UniFAR-FTUs huggingface dataset:
Medication-Induced Oral Hyperpigmentation: A Systematic Review

Background Medication-induced oral hyperpigmentation is an oral condition that impacts patients’ quality of life and has been linked to many systemic therapeutic agents. The exact pathogenesis of tissue pigmentation varies greatly and is not completely known. This systematic review aimed to present data on the causal association between medications and the development of oral/mucosal pigmentation as an adverse drug reaction. Methods A systematic review and analysis of literature were conducted using the following databases: PubMed, Science Direct, ProQuest, Web of Science, and Scopus. The systematic review included original articles written in English and published between January 1982 and June 2020. Following the PRISMA statement, eligible articles were systematically reviewed, and data were extracted from eligible studies and analyzed. Results A total of 235 articles were identified, of which 57 met the inclusion criteria and were included in this review. The mean age of included patients was 46.2±16.38 years (range: 10–90 years) with a male to female ratio of 1:1.45. Oral mucosal hyperpigmentation was reported following the use of several classes of medications such as antiviral (eg, zidovudine), antibiotic (eg, minocycline), antimalarial (eg, chloroquine), anti-fungal (eg, ketoconazole), antileprotic (eg, clofazimine), antihypertensive (eg, amlodipine), chemotherapeutic, and antineoplastic drugs. The risk of developing oral pigmentation was significantly higher with antimalarial medications, antibiotics, antineoplastic and chemotherapeutic agents. Medication-induced oral hyperpigmentation was most frequent among women and in the hard palate. Conclusion Future research is warranted to better understand the pathogenesis and risk factors for medication-induced oral hyperpigmentation in order to reassure patients during prescription and management.
COVID-19 Patients: A Systematic Review and Meta-Analysis of Laboratory Findings, Comorbidities, and Clinical Outcomes Comparing Medical Staff versus the General Population

This review compared coronavirus disease 2019 (COVID-19) laboratory findings, comorbidities, and clinical outcomes in patients from the general population versus medical staff to aid diagnosis of COVID-19 in a more timely, efficient, and accurate way. Electronic databases were searched up to 23rd March, 2020. The initial search yielded 6,527 studies. Following screening, 24 studies were included [18 studies (11,564 cases) of confirmed COVID-19 cases in the general public, and 6 studies (394 cases) in medical staff] in this review. Significant differences were observed in white blood cell counts (p < 0.001), lymphocyte counts (p < 0.001), platelet counts (p = 0.04), procalcitonin levels (p < 0.001), lactate dehydrogenase levels (p < 0.001), and creatinine levels (p = 0.03) when comparing infected medical staff with the general public. The mortality rate was higher in the general population than in medical staff (8% versus 2%). This review showed that during the early stages of COVID-19, laboratory findings alone may not be significant predictors of infection and may just accompany increasing C-reactive protein levels, erythrocyte sedimentation rates, and lactate dehydrogenase levels. In the symptomatic stage, the lymphocyte and platelet counts tended to decrease. Elevated D-dimer fibrin degradation product was associated with poor prognosis.
Chlamydia psittaci reference genes for normalisation of expression data differ depending on the culture conditions and selected time points during the chlamydial replication cycle

Abstract Introduction: Chlamydia psittaci is a gram-negative obligate intracellular pathogen of birds. Poultry infections lead to economic losses and can be transmitted to humans. No vaccine is available and the bacterium-host cell interaction is not completely understood. Replicating bacteria cause pneumonia, but C. psittaci can also be non-replicating and persistent inside the cytoplasm of avian cells. RT-qPCR provides insight into the molecular pathogenesis of both active replicating and persistent Chlamydia psittaci in birds, but requires identification of stably expressed reference genes to avoid biases. Material and Methods: We investigated the expression stability of 10 C. psittaci candidate reference genes for gene expression analysis during normal growth and penicillin-induced persistence. C. psittaci Cal10 was cultured in HeLa229 and RNA was extracted. The expression level of each candidate was examined by RT-qPCR and Cq values were analysed using geNorm. Results: The genes tyrS, gidA, radA, and 16S rRNA ranked among the most stably expressed. The final selected reference genes differed according to the bacterial growth status (normal growth versus persistent status), and the time points selected during the duration of the normal chlamydial developmental cycle. Conclusion: The study data show the importance of systematic validation of reference genes to confirm their stability within the strains and under the conditions selected.
On Principal Component Regression in a High-Dimensional Error-in-Variables Setting

We analyze the classical method of Principal Component Regression (PCR) in the high-dimensional error-in-variables setting. Here, the observed covariates are not only noisy and contain missing data, but the number of covariates can also exceed the sample size. Under suitable conditions, we establish that PCR identifies the unique model parameter with minimum $\ell_2$-norm, and derive non-asymptotic $\ell_2$-rates of convergence that show its consistency. We further provide non-asymptotic out-of-sample prediction performance guarantees that again prove consistency, even in the presence of corrupted unseen data. Notably, our results do not require the out-of-samples covariates to follow the same distribution as that of the in-sample covariates, but rather that they obey a simple linear algebraic constraint. We finish by presenting simulations that illustrate our theoretical results.
Slow entropy of some combinatorial constructions

Measure-theoretic slow entropy is a more refined invariant than the classical measure-theoretic entropy to characterize the complexity of dynamical systems with subexponential growth rates of distinguishable orbit types. In this paper we prove flexibility results for the values of upper and lower polynomial slow entropy of rigid transformations as well as maps admitting a good cyclic approximation. Moreover, we show that there cannot exist a general upper bound on the lower measure-theoretic slow entropy for systems of finite rank.
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory

We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine. Unfortunately, current large-scale sketching algorithms show poor memory-accuracy trade-off due to the irreversible collision and accumulation of the stochastic gradient noise in the sketched domain. Here, we develop a second-order ultra-high dimensional feature selection algorithm, called BEAR, which avoids the extra collisions by storing the second-order gradients in the celebrated Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm in Count Sketch, a sublinear memory data structure from the streaming literature. Experiments on real-world data sets demonstrate that BEAR requires up to three orders of magnitude less memory space to achieve the same classification accuracy compared to the first-order sketching algorithms. Theoretical analysis proves convergence of BEAR with rate O(1/t) in t iterations of the sketched algorithm. Our algorithm reveals an unexplored advantage of second-order optimization for memory-constrained sketching of models trained on ultra-high dimensional data sets.
It's All in the Name: A Character Based Approach To Infer Religion

Large-scale microdata on group identity are critical for studies on identity politics and violence but remain largely unavailable for developing countries. We use personal names to infer religion in South Asia—where religion is a salient social division, and yet, disaggregated data on it are scarce. Existing work predicts religion using a dictionary-based method and, therefore, cannot classify unseen names. We provide character-based machine-learning models that can classify unseen names too with high accuracy. Our models are also much faster and, hence, scalable to large datasets. We explain the classification decisions of one of our models using the layer-wise relevance propagation technique. The character patterns learned by the classifier are rooted in the linguistic origins of names. We apply these to infer the religion of electoral candidates using historical data on Indian elections and observe a trend of declining Muslim representation. Our approach can be used to detect identity groups across the world for whom the underlying names might have different linguistic roots.
Mapping Regional Well-Being in the Universal Health Coverage System in Taiwan

Purpose Regarding the universal health coverage (UHC) goal of eliminating health disparity, this study seeks to examine whether this objective has actually been achieved and whether residence affects health and well-being inequality. Methods Based on Taiwan’s experience with its UHC system, this research quantifies health and well-being indicators, including quality-adjusted life expectancy (QALE), consumption, and utility-adjusted life expectancy (UALE), and uses the geographic information system (GIS) to map regional well-being throughout Taiwan. Using spatial lag regressions, this study estimates how residence and socio-economic factors affect population’s well-being. Results Estimation results indicate a 1‰ increase in the mortality rate reduces the population’s UALE by 0.4131 utility-adjusted life-years (UALYs). The differences in health and well-being indicators between urban and rural residents were 6.49 quality-adjusted life-years (QALYs) and 3.84 UALYs. Residents living in Taipei City had the highest level of QALE, consumption, and well-being, and those in Taitung County had the lowest level of QALE and well-being. The regional spatial autocorrelation results show that a population’s health status and well-being are connected to residence. Conclusion Our estimation results show that risk of higher mortality rates in disadvantaged areas appears to be associated with well-being inequality, even with universal healthcare coverage. We suspect that related health intervention efforts, such as preventive and curative medical devotion, in Taiwan might not have effectively reached more rural residents, and thus recommend more work be undertaken to reduce mortality rates in these communities.
Neural Unsigned Distance Fields for Implicit Function Learning

In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Neural Network, thereby breaking previous barriers in resolution, and ability to represent diverse topologies. However, neural implicit representations are limited to closed surfaces, which divide the space into inside and outside. Many real world objects such as walls of a scene scanned by a sensor, clothing, or a car with inner structures are not closed. This constitutes a significant barrier, in terms of data pre-processing (objects need to be artificially closed creating artifacts), and the ability to output open surfaces. In this work, we propose Neural Distance Fields (NDF), a neural network based model which predicts the unsigned distance field for arbitrary 3D shapes given sparse point clouds. NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output. NDF allow to extract the surface as very dense point clouds and as meshes. We also show that NDF allow for surface normal calculation and can be rendered using a slight modification of sphere tracing. We find NDF can be used for multi-target regression (multiple outputs for one input) with techniques that have been exclusively used for rendering in graphics. Experiments on ShapeNet show that NDF, while simple, is the state-of-the art, and allows to reconstruct shapes with inner structures, such as the chairs inside a bus. Notably, we show that NDF are not restricted to 3D shapes, and can approximate more general open surfaces such as curves, manifolds, and functions. Code is available for research at this https URL.
Increased M1 Macrophages Infiltration Correlated With Poor Survival Outcomes and Radiation Response in Gliomas

Background: Gliomas are the malignance of a poor prognosis. The current WHO classification remains unable to predict survival outcomes accurately. Novel surrogates are highly required for improved stratification of patients and hence, allowing to delivery of the most appropriate treatment. Methods: Transcriptional profiles of 301 glioma cases on the platform of Chinese Glioma Genome Atlas (CGGA) were retrospectively studied. Results: Extracellular matrix (ECM) scores were established by integrating a panel of most featured gene-signatures, correlating well with pathological tumor stages. Linear regression analysis revealed that the ECM score corroborated with the infiltration status of monocytes, M0 and M1 macrophages. Furthermore, the WHO stage II-IV dependent abundance of those 3 immune cells was determined. Univariate and multivariate analysis of clinicopathological characteristics in the GBM cohort identified M1 enrichment score as an independent risk factor. A high abundance of M1 macrophages was associated with poor survival outcomes and radiotherapy response in IDH-wildtype GBM. Conclusions: Our study demonstrated that M1 macrophages correlated with WHO grades and predicted robustly for the survival performance for GBM patients. Increased infiltration of M1 macrophages was associated with a poor radiation response for IDH-wildtype GBM. Together, it will facilitate more precise stratifications of glioma patients based on molecular and immunological surrogates.
In vitro Effect of Silver Nanoparticles on Blastocystis hominis

Introduction This study aims to assess the efficacy of silver nanoparticles (Ag Nps) alone and combined with metronidazole (Ag Nps + MTZ) as potential alternative therapeutic agents for Blastocystis hominis. Methods The parasites were challenged with Ag Nps, Ag Nps + MTZ and MTZ. To assess the efficacy of drugs, counting of viable parasites was done after 1, 2, and 3 hours of adding the drugs. Results Blastocystis hominis count was reduced by 20.72%, 28.23%, and 18.92% after one hour of adding Ag Nps, Ag Nps + MTZ, and MTZ, respectively. Cysts count was further reduced by 51.49%, 61.61%, and 40.78% after 2 hours and by 71.69%, 79.67%, and 62.65% after 3 hours of adding the drugs in the same order, respectively. Conclusion There was a statistically significant difference (P<0.05) in the in vitro growth inhibition of the parasite over the different time intervals when using the tested drugs against the control drug.
How Does Chronic Intermittent Hypoxia Influence Upper Airway Stability in Rats?

Background Obstructive sleep apnea (OSA) is characterized by repetitive episodes of upper airway collapse during sleep. The contraction of upper airway dilator muscles plays a crucial role in maintaining UA patency. Chronic intermittent hypoxia (CIH) is the most important pathophysiological process of OSA. Exposure to CIH induced not only the damage of dilator muscles but also the plasticity of the muscles. This study aimed to dynamically assess the influence of CIH on the upper airway. Methods The experiments were performed on 44 rats. They were randomly divided into a normoxia (NO) group (n=22) and CIH group (n=22). In each group (n=6, respectively), EMG, transcranial magnetic stimulation (TMS) response, and critical pressure (Pcrit) value were recorded on day 0 (the day before exposure), and the 7th, 14th, 21st, and 28th day of air/CIH exposure. For each group, 16 rats were used for transmission electron microscopy observations on day 0, and the 7th, 14th and 28th day of air/CIH exposure (n=4 for every time point). Results Compared to the NO group at the same point, the CIH group showed a damaged ultrastructure of genioglossus, increased activity of genioglossus corticomotor area, and increased Pcrit of the upper airway from the 7th to the 28th day of CIH. Increased EMG activity occurred at the 14th day of CIH and lasted for 2 weeks. Conclusion The elevated genioglossus corticomotor excitability in response to the CIH could not counterbalance the damage effect of CIH on upper airway dilator muscles, which ultimately increased the collapsibility of the upper airway.
Genetic and Functional Characterization of HIV-1 Vpu from HIV-1-Infected North Indian Population

Acquired immunodeficiency syndrome is a pandemic disease due to increased variability in causative agent in global distribution; it is attributed to various complications in developing the vaccine, namely, error-prone reverse transcriptase, rapid replication, and high recombination rate. Vpu downmodulates CD4 in infected cells, and it targets the newly synthesized CD4 molecules from the endoplasmic reticulum. The aim of this study was to identify the level of genetic changes in the Vpu gene from HIV-1-infected North Indian individuals and determine the functional relevance with respect to the CD4 downregulation potential of this protein. Genomic DNA was isolated from peripheral blood mononuclear cells, and the Vpu gene was polymerase chain reaction amplified with specific primers followed by cloning, sequencing, and sequence analyses using bioinformatic tools for predicting HIV-1 subtypes, recombination events, conservation of domains, and phosphorylation sites. Among all Vpu variants, three of the variants having serine substitution (serine-52 and serine-56 conversion to isoleucine; S52I and S56I) had lost their functional β-TrcP binding motif. However, the specific determinants for CD4 (V20, W22, S23) and BST-2 (A11, A15, I17, and A19) binding remained highly conserved. The data obtained with Vpu mutants recommend that the serine residue substitutions in cytoplasmic domain distress the CD4 downregulation activity of Vpu. These events are likely to have implications for viral pathogenesis and vaccine formulations.
Analysis of Differential Expression Proteins of Paclitaxel-Treated Lung Adenocarcinoma Cell A549 Using Tandem Mass Tag-Based Quantitative Proteomics

Background Paclitaxel is widely used in the treatment of cancer and has a good effect in the treatment of non-small cell lung cancer. The combination of TMT proteomics and bioinformatics is used to systematically analyze the molecular mechanism of paclitaxel in the treatment of lung adenocarcinoma A549 cell, which is helpful to screen new therapeutic targets. Methods MTT assay was used to analyze the inhibitory effect of paclitaxel on the proliferation of A549 cells. The proteins were identified by TMT quantitative proteomics and the differential expression proteins (DEPs) database was constructed. The DEPs were enriched by Gene Ontology (GO) and KEGG pathway annotation. Based on the information in the STRING database, find the interaction between DEPs, and the protein–protein interaction (PPI) networks of DEPs were constructed and analyzed by using the Cytoscape software. According to the PPI network results, select the hub proteins from DEPs for WB verification. Results A total of 5449 proteins were identified in A549 by TMT proteomics. Compared with the control group, 281 DEPs were significantly up-regulated and 218 were significantly down-regulated after paclitaxel treatment. GO functional analysis, we found that the main functions of these DEPs are binding, catalytic activity, molecular function regulator and so on. They are mainly involved in cellular process, metabolic process, biological regulation and so on. KEGG analysis showed that the three most significant signal transduction pathways of DEPs enrichment were DNA replication, steroid biosynthesis, oxidative phosphorylation. In PPI network, there are 294 nodes among which CDK1, MCM2-5 and PCNA are located at the center of proteins interaction. WB analysis confirmed that the expression of CDK1 was significantly down-regulated, consistent with the TMT results. Conclusion Paclitaxel significantly increased the expression of tubulin, binding tubulin to promote A549 cell death. In addition, paclitaxel significantly inhibited the expression of hub proteins, DNA replication and cell cycle pathways, thus killing lung adenocarcinoma cell A549. These findings will enhance the understanding of the mechanism of paclitaxel in the treatment of lung adenocarcinoma cell A549 and provide new valuable targets.
A Physical Layer, Zero-Round-Trip-Time, Multifactor Authentication Protocol

Lightweight physical layer security schemes that have recently attracted a lot of attention include physical unclonable functions (PUFs), RF fingerprinting / proximity based authentication and secret key generation (SKG) from wireless fading coefficients. In this paper, we propose a fast, privacy-preserving, zero-round-trip-time (0-RTT), multi-factor authentication protocol, that for the first time brings all these elements together, i.e., PUFs, proximity estimation and SKG. We use Kalman filters to extract proximity estimates from real measurements of received signal strength (RSS) in an indoor environment to provide soft fingerprints for node authentication. By leveraging node mobility, a multitude of such fingerprints are extracted to provide resistance to impersonation type of attacks e.g., a false base station. Upon removal of the proximity fingerprints, the residual measurements are then used as an entropy source for the distillation of symmetric keys and subsequently used as resumption secrets in a 0-RTT fast authentication protocol. Both schemes are incorporated in a challenge-response PUF-based mutual authentication protocol, shown to be secure through formal proofs using Burrows, Abadi, and Needham (BAN) and Mao and Boyd (MB) logic, as well as the Tamarin-prover. Our protocol showcases that in future networks purely physical layer security solutions are tangible and can provide an alternative to public key infrastructure in specific scenarios.
Exploring the Factors Affecting Bitter Melon Peptide Intake Behavior: A Health Belief Model Perspective

Purpose Diabetes mellitus (DM) patients need to control their blood sugar level in order to achieve a good quality of life. This study was conducted using the health belief model (HBM), to explore the factors behind the bitter melon peptide (BMP) intake behavior and the role of self-efficacy in the model. Materials and Methods The subjects were type 2 diabetes mellitus patients in Taiwan. A structured questionnaire was adopted from the theory of health belief model and modified specifically for this study as an instrument to survey 292 DM patients, of whom 51.03% were female, 75.68% were married, and 49.32% were aged 40 to 64 years old. The data were analyzed using t-tests, one-way ANOVA and regression. Results Perceived susceptibility was the most sensitive in the response to the various demographic factors, whereas perceived barrier was the least sensitive. The HBM explained 38.0% of BMP intake behavior. Perceived benefits (β= 0.357) and perceived susceptibility (β= 0.348) were the major predictors. Self-efficacy mediated the relationship between perceived benefits and BMP intake behavior, as well as increased the variance explained to 51.30%. Conclusion The perceived benefits of taking BMP and perceived susceptibility to DM complications were the two major drivers acting on BMP intake behavior. The power of perceived benefits was mediated by self-efficacy in driving DM patients to take BMP regularly. Several ways of affecting perceived susceptibility and perceived benefits were suggested.