Miracle-dz/UniFAR-FTUs huggingface dataset:
Ipconazole Induces Oxidative Stress, Cell Death, and Proinflammation in SH-SY5Y Cells

Ipconazole is an antifungal agrochemical widely used in agriculture against seed diseases of rice, vegetables, and other crops; due to its easy accumulation in the environment, it poses a hazard to human, animal, and environmental health. Therefore, we investigated the cytotoxic effect of ipconazole on SH-SY5Y neuroblastoma cells using cell viability tests (MTT), ROS production, caspase3/7 activity, and molecular assays of the biomarkers of cell death (Bax, Casp3, APAF1, BNIP3, and Bcl2); inflammasome (NLRP3, Casp1, and IL1β); inflammation (NFκB, TNFα, and IL6); and antioxidants (NRF2, SOD, and GPx). SH-SY5Y cells were exposed to ipconazole (1, 5, 10, 20, 50, and 100 µM) for 24 h. The ipconazole, in a dose-dependent manner, reduced cell viability and produced an IC50 of 32.3 µM; it also produced an increase in ROS production and caspase3/7 enzyme activity in SH-SY5Y cells. In addition, ipconazole at 50 µM induced an overexpression of Bax, Casp3, APAF1, and BNIP3 (cell death genes); NLRP3, Casp1, and IL1B (inflammasome complex genes); and NFκB, TNFα, and IL6 (inflammation genes); it also reduced the expression of NRF2, SOD, and GPx (antioxidant genes). Our results show that ipconazole produces cytotoxic effects by reducing cell viability, generating oxidative stress, and inducing cell death in SH-SY5Y cells, so ipconazole exposure should be considered as a factor in the presentation of neurotoxicity or neurodegeneration.
Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.
A Mass Spectrometry Database for Sea Cucumber Triterpene Glycosides

Sea cucumber triterpene glycosides are a class of secondary metabolites that possess distinctive chemical structures and exhibit a variety of biological and pharmacological activities. The application of MS-based approaches for the study of triterpene glycosides allows rapid evaluation of the structural diversity of metabolites in complex mixtures. However, the identification of the detected triterpene glycosides can be challenging. The objective of this study is to establish the first spectral library containing the mass spectra of sea cucumber triterpene glycosides using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. The library contains the electrospray ionization tandem mass spectra and retention times of 191 triterpene glycosides previously isolated from 15 sea cucumber species and one starfish at the Laboratory of the Chemistry of Marine Natural Products of the G.B. Elyakov Pacific Institute of Bioorganic Chemistry. In addition, the chromatographic behavior and some structure-related neutral losses in tandem MS are discussed. The obtained data will accelerate the accurate dereplication of known triterpene glycosides and the annotation of novel compounds, as we demonstrated by the processing of LC-MS/MS data of Eupentacta fraudatrix extract.
Research on Integrated 3D Printing of Microfluidic Chips

Microfluidic chips have the advantages of miniaturization, integration, and portability, and are widely used in the early diagnosis of major diseases, personalized medical treatment, environmental detection, health quarantine, and other fields. The existing microfluidic chip manufacturing process is difficult to operate because of complex three-dimensional channels, complicated manufacturing steps, limited printing materials, the difficulty of operating the bonding process, and the need to purchase expensive new equipment. In this paper, an integrated molding method for microfluidic chips that integrates 3D printing and polymer dissolution technology is proposed. First, the channel mold of poly(vinyl alcohol) (PVA) or high impact polystyrene (HIPS) is dissolved to complete the manufacturing of the microfluidic chip channel. The integrated 3D-forming method of microfluidic chips proposed in this paper can manufacture microchannels inside the microfluidic chip, avoid the bonding process, and eliminate the need for rapid alignment of microchannels, material modification, and other operations, thus improving the stability of the process. Finally, by comparing the microchannels made by PVA and HIPS, it is concluded that the quality of the microchannels made by HIPS is obviously better than that made by PVA. This paper provides a new idea for the fabrication of microfluidic chips and the application of HIPS.
Interaction of Near-Infrared (NIR)-Light Responsive Probes with Lipid Membranes: A Combined Simulation and Experimental Study

Cancer is considered a major societal challenge for the next decade worldwide. Developing strategies for simultaneous diagnosis and treatment has been considered a promising tool for fighting cancer. For this, the development of nanomaterials incorporating prototypic near-infrared (NIR)-light responsive probes, such as heptamethine cyanines, has been showing very promising results. The heptamethine cyanine-incorporating nanomaterials can be used for a tumor’s visualization and, upon interaction with NIR light, can also produce a photothermal/photodynamic effect with a high spatio-temporal resolution and minimal side effects, leading to an improved therapeutic outcome. In this work, we studied the interaction of 12 NIR-light responsive probes with lipid membrane models by molecular dynamics simulations. We performed a detailed characterization of the location, orientation, and local perturbation effects of these molecules on the lipid bilayer. Based on this information, the probes were divided into two groups, predicting a lower and higher perturbation of the lipid bilayer. From each group, one molecule was selected for testing in a membrane leakage assay. The experimental data validate the hypothesis that molecules with charged substituents, which function as two polar anchors for the aqueous phase while spanning the membrane thickness, are more likely to disturb the membrane by the formation of defects and pores, increasing the membrane leakage. The obtained results are expected to contribute to the selection of the most suitable molecules for the desired application or eventually guiding the design of probe modifications for achieving an optimal interaction with tumor cell membranes.
Gold Price Prediction Using the ARIMA and LSTM Models

For some investors who are interested in investing for the long term, gold is one of the promising options because the price of gold has recently continued to increase. In the current condition, gold investors generally use instinct and guesswork in investing in gold because there is a benchmark gold price based on world market prices. Many empirical studies identify factors that affect gold prices to forecast them. Factual and econometric analysis recommend different informative factors. This study investigates the influence of gold prices and five supporting variables in the form of economic indicators, namely crude oil price, federal funds effective rate, consumer price index, effective exchange rate and S&P 500 stock market index between 2002 and 2022. Models were built using ARIMA and LSTM methods, evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). With a dataset allocation of 80% for training data and 20% for testing data, the comparison of actual gold prices with the predicted values of each model shows that LSTM has the best performance compared to the ARIMA (0,1,1) model where the LSTM model has an RMSE value of 8.124 and a MAPE value of 0.023. The models also show that economic indicators affect the ounce price of gold.
An Improved DV-Hop Localization Algorithm Against Wormhole Attack in WSN

Wormhole attack is one of the most serious attacks on DV-Hop localization algorithm. Previous solutions usually require hardware support, or a higher ratio of beacon nodes and very complex mathematical operations. In this paper, we analyze the effect of the wormhole attack on DV-Hop algorithm, which mainly destroys the network topological structure and shrinks hop count between nodes and expands the localization error. We propose a modified DV-Hop method without additional hardware support against wormhole attacks. Through analyzing the characteristics of the neighboring nodes set of nodes attacked, this method detects wormhole attack, then locates nodes by using an improved DV-Hop algorithm. We show that the scheme can detect wormhole attacker adequately, and the obtained localization accuracy is even better than that of the DV-Hop method without wormhole attacks in most cases.
Tuberculosis Knowledge, Attitudes, and Practice in Middle- and Low-Income Countries: A Systematic Review

Tuberculosis (TB) is the leading cause of death from an infectious agent in the world. Most tuberculosis cases are concentrated in low- and middle-income countries. The aim of this study is to better understand tuberculosis-related knowledge about TB disease, prevention, treatment and sources of information, attitudes towards TB patients and their stigmatization and prevention, diagnosis and treatment practices in the general population of middle- and low-income countries, with a high tuberculosis burden, and provide evidence for policy development and decision-making. A systematic review of 30 studies was performed. Studies reporting on knowledge, attitudes, and practices surveys were selected for systematic review through database searching. Population knowledge about TB signs and symptoms, prevention practices, and treatment means was found inadequate. Stigmatization is frequent, and the reactions to possible diagnoses are negative. Access to health services is limited due to difficulties in transportation, distance, and economic cost. Deficiencies in knowledge and TB health-seeking practices were present regardless of the living area, gender, or country; however, it seems that there is a frequent association between less knowledge about TB and a lower socioeconomic and educational level. This study revealed gaps in knowledge, attitude, and practices in focused in middle- and low-income countries. Policymakers could take into account the evidence provided by the KAP surveys and adapt their strategies based on the identified gaps, promoting innovative approaches and empowering the communities as key stakeholders. It is necessary to develop education programs on symptoms, preventive practices, and treatment for TB, to reduce transmission and stigmatization. It becomes also necessary to provide communities with innovative healthcare solutions to reduce their barriers to access to diagnosis and treatment.
Psychological impact of COVID‐19 pandemic and lockdown among the population involved in tourism sector in Lakeside of Pokhara: A qualitative study

The COVID‐19 pandemic has exerted a substantial influence on every sector of people's lives worldwide, including Nepal. The tourism industry is not exceptional. Lakeside Pokhara is one of the country's major tourist hubs and relies both on national and international visitors. The people residing in this area who depend on tourism‐related businesses to regulate their daily living faced numerous stressors and psychological impacts due to the pandemic. This study aimed to explore the COVID‐19 pandemic‐related stressors and their psychological impact among people dependent on the tourism business in the Lakeside of Pokhara, located in the Gandaki Province of Nepal.
Characterization of mechanosensitive MSL gene family expression in Zea mays aerial and subterranean brace roots

Plants must be able to sense and respond to mechanical stresses encountered throughout their lifespan. The MscS-Like (MSL) family of mechanosensitive ion channels is one mechanism to perceive mechanical stresses. In maize, brace roots emerge from stem nodes above the soil and some remain aerial while some grow into the soil. We tested the hypothesis that MSL gene expression is higher in subterranean brace roots compared to those that remain aerial. However, there was no difference in MSL expression between the two environments. This work sets the foundation for a deeper understanding of MSL gene expression and function in maize.
Spring-Powered Needle-Free Injection of Triamcinolone Acetonide and 5-Fluorouracil for Keloid Treatment

Introduction Keloid is an abnormal fibroproliferative healing response characterized by excessive and invasive tissue growth beyond the wound boundaries. The conventional treatment involves injecting drugs such as triamcinolone acetonide (TA), 5-fluorouracil (5-FU), or their combination intralesionally. However, the pain associated with injections often leads to low patient compliance and treatment failure. The spring-powered needle-free injector (NFI) provides an affordable alternative option for drug delivery with reduced pain. Case This case report presents a 69-year-old female patient with a keloid treated using a spring-powered needle-free injector (NFI) for drug delivery. The keloid was assessed using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). The patient’s pain level was measured using the Numeric Pain Rating Scale (NPRS). TA and 5-FU mixed with lidocaine were loaded into the NFI and injected at a dose of 0.1 mL/cm2. The treatment was repeated twice a week. After four sessions, the keloid flattened by 0.5 cm, VSS score decreased from 11 to 10, and POSAS scores decreased from 49 to 43 (observer) and from 50 to 37 (patient). The NPRS during each procedure was 1, indicating minimal pain. Discussion The spring-powered NFI is a simple and cost-effective device that operates based on Hooke’s law, producing a high-pressure fluid jet for effective skin penetration. The NFI demonstrated effectiveness in treating keloid lesions, resulting in visible improvement after four treatments. Conclusion The spring-powered NFI offers an affordable and painless alternative to keloid treatment.
Stability and bifurcation analysis of some fluid flow model of TCP behavior

This note focuses on the stability analysis of some classes of nonlinear time-delay models, encountered as fluid models for TCP/AQM network. By combining analytical and numerical tools, the attractors of these models, as well as the local and global behavior of the solutions are studied. Among others, the presence of a chaotic attractor is shown, which supports the proposition that TCP itself as a deterministic process can cause or contribute to chaotic behavior in a network. The main goals of the paper are firstly to provide qualitative and quantitative information on the dynamics of the models under consideration, and secondly to illustrate the capabilities of computational tools for stability and bifurcation analysis of delay differential equations to analyze fluid flow models.
Estimating contact network properties by integrating multiple data sources associated with infectious diseases

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS‐CoV‐2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.
A high‐efficiency gene silencing in plants using two‐hit asymmetrical artificial MicroRNAs

Summary MicroRNAs (miRNAs) are small non‐coding RNA molecules that play a crucial role in gene regulation. They are produced through an enzyme‐guided process called dicing and have an asymmetrical structure with two nucleotide overhangs at the 3′ ends. Artificial microRNAs (amiRNAs or amiRs) are designed to mimic the structure of miRNAs and can be used to silence specific genes of interest. Traditionally, amiRNAs are designed based on an endogenous miRNA precursor with certain mismatches at specific positions to increase their efficiency. In this study, the authors modified the highly expressed miR168a in Arabidopsis thaliana by replacing the single miR168 stem‐loop/duplex with tandem asymmetrical amiRNA duplexes that follow the statistical rules of miRNA secondary structures. These tandem amiRNA duplexes, called “two‐hit” amiRNAs, were shown to have a higher efficiency in silencing GFP and endogenous PDS reporter genes compared to traditional “one‐hit” amiRNAs. The authors also demonstrated the effectiveness of “two‐hit” amiRNAs in silencing genes involved in miRNA, tasiRNA, and hormone signalling pathways, individually or in families. Importantly, “two‐hit” amiRNAs were also able to over‐express endogenous miRNAs for their functions. The authors compare “two‐hit” amiRNA technology with CRISPR/Cas9 and provide a web‐based amiRNA designer for easy design and wide application in plants and even animals.
Fully converged plane-wave-based self-consistent G W calculations of periodic solids

The GW approximation is a well-known method to obtain the quasiparticle and spectral properties of systems ranging from molecules to solids. In practice, GW calculations are often employed with many different approximations and truncations. In this work, we describe the implementation of a fully self-consistent GW approach based on the solution of the Dyson equation using plane wave basis set. Algorithmic, numerical and technical details of the self-consistent GW approach are presented. The fully self-consistent GW calculations are performed for GaAs, ZnO and CdS including semicores in the pseudopotentials. No further approximations and truncations apart from the truncation on the plane wave basis set are made in our implementation of the GW calculation. After adopting a special potential technique, a ~ 100 Ryd energy cutoff can be used without the loss of accuracy. We found that the self-consistent GW (sc- GW ) significantly overestimates the bulk band gaps, and this overestimation is likely due to the underestimation of the macroscopic dielectric constants. On the other hand, the sc- GW predicts accurately the d -state positions, mostly likely because the d -state screening does not sensitively depend on the macroscopic dielectric constant. Our work indicates the needs to include the high-order vertex term in order for the many-body perturbation theory to accurately predict the semiconductor band gaps. It also sheds some lights on why, in some cases, the G 0 W 0 bulk calculation is more accurate than the fully self-consistent GW calculation, because the initial density functional theory has a better dielectric constant compared to experiments.
A systematic review of ecological momentary assessment studies of appetite and affect in the experience of temptations and lapses during weight loss dieting

Dietary temptations and lapses challenge control over eating and act as barriers toward successful weight loss. These are difficult to assess in laboratory settings or with retrospective measures as they occur momentarily and driven by the current environment. A better understanding of how these experiences unfold within real‐world dieting attempts could help inform strategies to increase the capacity to cope with the changes in appetitive and affective factors that surround these experiences. We performed a narrative synthesis on the empirical evidence of appetitive and affective outcomes measured using ecological momentary assessment (EMA) during dieting in individuals with obesity and their association with dietary temptations and lapses. A search of three databases (Scopus, Medline, and PsycInfo) identified 10 studies. Within‐person changes in appetite and affect accompany temptations and lapses and are observable in the moments precipitating a lapse. Lapsing in response to these may be mediated through the strength of a temptation. Negative abstinence‐violation effects occur following a lapse, which negatively impact self‐attitudes. Engagement in coping strategies during temptations is effective for preventing lapses. These findings indicate that monitoring changes in sensations during dieting could help identify the crucial moments when coping strategies are most effective for aiding with dietary adherence.
Laser irradiation of WE54 surface through simulated body fluid layer

Mg alloys are superior to other metallic biomaterials for temporary bio-application, owing to their biodegradability and biocompatibility features. However, the high reactivity and the rapid corrosion rate of Mg alloys are major barriers for their usage in bio-applications. In this paper, laser surface modification method is used to improve the surface properties of WE54. A 500 W pulse Nd: YAG laser beam is focused on the specimen through a 2 mm layer of simulated body fluid. As a result, a new microstructure and a Ca/P layer are obtained in a single process, which is conductive for the cell attachment.
Do nerve conduction studies or ultrasound imaging correlate more closely with subjective symptom severity in carpal tunnel syndrome?

Previous studies have reported weak correlations between neurophysiological measurements and subjective severity of symptoms in carpal tunnel syndrome, with Pearson r ≤ 0.26. We hypothesize that this resulted in part from patient‐to‐patient variability in the assessment of subjective severity using tools such as the Boston Carpal Tunnel Questionnaire. To compensate for this, we aimed to assess within‐patient differences in symptom and test result severity.