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Abstract Objective This study aimed to introduce a lytic bacteriophage against Staphylococcus saprophyticus from wastewater in Gorgan, northern Iran. Results The vB_SsapS-46 phage was isolated from urban wastewater and formed round and clear plaques on bacterial culture. It was visualized by electron microscopy and had a large head (approximately 106 nm) and a long tail (approximately 150 nm), indicating that it belongs to the Siphoviridae family. The host range of vB_SsapS-46 was determined using a spot test on 35 S. saprophyticus clinical isolates, and it was able to lyse 12 of the 35 clinical isolates (34%). Finally, the relationship between phage sensitivity and adherence genes was assessed, revealing no significant correlation between phage sensitivity and the frequency of adherence genes. The vB_SsapS-46 phage can be used alone or in a mixture in future studies to control urinary tract infections caused by this bacterium, especially in the elimination of drug-resistant pathogens.

Abstract Background Digital Polymerase Chain Reaction (dPCR) presents a promising approach for quantifying DNA and analyzing copy number variants, particularly in non-invasive prenatal testing. This method offers a streamlined and time-efficient procedure in contrast to the widely used next-generation sequencing for non-invasive prenatal testing. Studies have reported encouraging results for dPCR in detecting fetal autosomal aneuploidies. Consequently, this systematic review aimed to evaluate the effectiveness of dPCR in screening for trisomy 21, 18, and 13. Methods A systematic search was conducted in PubMed, Web of Sciences, and Embase for relevant articles published up to December 30, 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was utilized for the quality assessment of the included articles. Furthermore, a bivariate random-effect regression model was used to conduct a meta-analysis on the utility of dPCR for trisomy 21 screening. Results A total of 9 articles were included in this review, with all of them assessing the utility of dPCR in trisomy 21 screening, and 2 and 1 studies conducting additional analysis on the screening abilities of dPCR for trisomy 18 and 13, respectively. A bivariate random-effects model calculated pooled sensitivity and specificity with a 95% confidence interval (CI). Meta-analysis of 6 studies comparing trisomy-21 screening with karyotyping demonstrated dPCR's pooled sensitivity of 98% [95% CI: 94 -100] and specificity of 99% [95% CI: 99 -100]. While conducting a meta-analysis for trisomy 13 and 18 proved impractical, reported values for sensitivity and specificity were favorable. Conclusions These findings suggest that dPCR holds promise as an effective tool for non-invasive prenatal testing, presenting a less time-consuming and intricate alternative to next-generation sequencing. However, further research is necessary to evaluate dPCR's applicability in clinical settings and to delineate its specific advantages over next-generation sequencing. This study contributes valuable insights into the potential of dPCR for enhancing prenatal screening methodologies. Trial registration The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 7/3/2024, with a registration code of CRD42024517523.

Abstract Deep eutectic solvents (DES) are a generation of ionic liquids that benefit from low cost, good stability, and environmental-friendly features. In this research, a porous silica gel was impregnated with a eutectic Choline Chloride-Monoethanolamine solvent (ChCl-MEA) to greatly improve its CO2 capture performance. In the impregnation, the weight percentages of ChCl-MEA were used in the range of 10–60 wt% at a temperature of 25 °C. The effect of ChCl-MEA loading on the structural properties of the DES-modified silica samples was studied by BET, FTIR, and TGA analyses. Investigation of the CO2 adsorption performance at different operational conditions showed that the modified silica gel with 50 wt% ChCl-MEA (Silica-CM50) presents the highest CO2 capture capacity of 89.32 mg/g. In the kinetic modeling, the fractional order model with a correlation coefficient of 0.998 resulted in the best fit with the experimental data. In addition, the isotherm data for Silica-CM50 were well-fitted with the Dual site Langmuir isotherm model with a correlation coefficient of 0.999, representing two distinct sites for the adsorption process. Moreover, the thermodynamic parameters including Enthalpy, Entropy, and Gibbs free energy at 25 °C were obtained to be − 2.770, − 0.005 and − 1.162, respectively. The results showed the exothermic, spontaneous and feasibility of the adsorption process.

Abstract This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

Introduction: The utility of ChatGPT in research is ambiguous, serving as both a boon and bane and presenting challenges and opportunities. Accordingly, this study aimed to delineate themerits and demerits of ChatGPT within the realm of research.Methods: A meta-synthesis methodology involving a seven-step process was adopted to pursue the research objectives. Data were collected from comprehensive studies across specialized research databases, such as Science Direct, Springer, Eric, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar, within the time frame spanning from 2022 to 2023. A total of 57 articles were meticulously chosen for analysis through judgmental sampling. Subsequently, key concepts were distilled from these articles and categorized using the thematic analysis approach while considering Karl Llewellyn’s octagonal model from 1963.Results: The outcomes underwent scrutiny employing the thematic analysis strategy devised by Wolcott. The results about the capabilities and constraints of ChatGPT encompass eight dimensions, including formulating the research problem, reviewing relevant literature, selecting an appropriate research design, defining the population and choosing the sample, collecting data, analyzing data, interpreting data, and discussing and drawing conclusions. The credibility of these dimensions was substantiated through adherence to the criteria established by Lincoln and Guba. Conclusion: ChatGPT manifests myriad potentialities and constraints within the research purview based on the results. In light of the expeditious progression of artificial intelligence acrossmultifarious domains, integrating this technological paradigm in research becomes an inexorable imperative, and excluding its use in research is untenable. Thus, policymakers and higher education strategists should devise policies that harness ChatGPT’s potential in research endeavors, engendering many opportunities.

This paper studies advanced mathematical methods like sin-cos and sinh-cosh approachesto find precise solutions by considering inter-modal dispersion and spatio-temporal dynamics with kerrlaw nonlinearity in the Resonant shrödinger equation. These methods are useful for solving nonlinearpartial differential equations. To obtain the ordinary differential equation for the traveling wave solution,we initially deal with the general partial differential equation (PDE). Then, a series of optical solitonsolutions, including cusp and dark solitons, are derived for the Resonant shrödinger equation using thiseffective approach.

Abstract Introduction This study investigated the possible relationship between the Apo lipoprotein A1 /high-density lipoprotein cholesterol (ApoA1/HDL-C) ratio and coronary artery disease (CAD) in patients with type 2 diabetes (T2D). Methods This was a matched case-control study of 482 patients with T2D in two groups of CAD and (n = 241) non-CAD (n = 241). The patients were classified into four quartiles according to the ApoA1/HDL-C ratio, and multivariate logistic regression analysis was performed to assess the relationship between ApoA1/HDL-C and CAD. ROC analysis was also conducted. Results This study showed that the ApoA1/HDL-C ratio has an independent association with CAD in individuals with T2D. The CAD group exhibited a significantly higher ApoA1/HDL-C ratio than those without CAD (p-value = 0.004). Moreover, the risk of CAD increased significantly across the ApoA1/HDL-C ratio quartiles, with the highest odds in the fourth quartile. The second quartile showed an odds ratio (OR) of 2.03 (p-value = 0.048) compared to the first. Moving to the third quartile, the OR increased to 2.23 (p-value = 0.023). The highest OR was noted in the fourth, reaching 3.41 (p-value = 0.001). Employing a cut-off value of 2.66 and an area under the curve (AUC) of 0.885, the ApoA1/HDL-C ratio predicts CAD among patients with T2D with a sensitivity of 75% and a specificity of 91% (p-value

Abstract Burning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor. Sustainable catalysts should have high performance, good activity, easy separation from reaction cells, and regenerability. In this study, to solve the mentioned problem NaOH@Graphene oxide-Fe3O4 as a magnetic catalyst was used for the first time to generate biodiesel from waste cooking oil. The crystal structure, functional groups, surface area and morphology of catalyst were studied by XRD, FTIR, BET, and FESEM techniques. The response surface methodology based central composite design (RSM-CCD) was used for biodiesel production via ultrasonic technique. The maximum biodiesel yield was 95.88% in the following operation: 10.52:1 molar ratio of methanol to oil, a catalyst weight of 3.76 wt%, a voltage of 49.58 kHz, and a time of 33.29 min. The physiochemical characterization of biodiesel was based to ASTM standard. The magnetic catalyst was high standstill to free fatty acid due to the five cycle’s regeneration. The kinetic study results possess good agreement with first-order kinetics as well as the activation energy and Arrhenius constant are 49.2 kJ/min and 16.47 * 1010 min−1, respectively.

Abstract This research investigates the enhancement of CO2 adsorption capacity through the use of modified activated carbon (AC) with LiOH, focusing on operational conditions and adsorbent properties. Response Surface Methodology (RSM) is employed to optimize process parameters for maximizing CO2 adsorption capacity. The study considers temperature, pressure, LiOH concentration for modification, and adsorbent weight as independent variables across five levels. Analysis of Variance reveals that LiOH concentration, adsorbent quantity, pressure, and temperature significantly influence CO2 adsorption. Optimal values for temperature (30°C), pressure (9 bar), LiOH concentration (0.5 mol/L), and adsorbent weight (0.5 g) result in a maximal CO2 adsorption capacity of 154.90 mg/g. Equilibrium adsorption capacity is utilized for modeling, with the Freundlich model proving suitable for CO2 adsorption on LiOH-AC. Kinetic modeling indicates the second-order model's suitability for temperatures of 30 °C and 50 °C, while the Elovich model fits temperatures of 70 °C and 90 °C. Thermodynamic modeling at the optimized conditions (303 K and 6 bar) yields ∆H, ∆S, and ∆G values of adsorption as 12.258 kJ/mol, − 0.017 kJ/mol·K, and − 7.031 kJ/mol, respectively. Furthermore, structural considerations of AC are discussed alongside modeling and simulation, presenting the adsorption rate of CO2 and the binding energy index based on Density Functional Theory (DFT).

Abstract This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Abstract Objective Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts. Method A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach. Results Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts. Conclusions The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Abstract This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology (FinTech), the Internet of Things, and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities; (2) Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoin’s good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors.

Abstract Background Obesity and mental health issues are two of the most prevalent global public health issues for a significant portion of people. The purpose of this study was to investigate the relationship between obesity indicators and mental health in Tehran-dwelling Iranian adults. Methods We conducted a cross-sectional study on healthy Iranian adults using a convenience sampling technique. The short form of the Depression Anxiety and Stress Scale (DASS-21) was used to measure the outcome, and independent variables included body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), body adiposity index (BAI), and a body shape index (ABSI). The relationship between obesity and mental health was investigated using a multivariate logistic regression model. The non-linear dose-response relationships were evaluated using restricted cubic splines (RCS) with three knots. The Benjamini-Hochberg procedure was used to adjust for multiple testing. Results In our study of 434 participants, females made up 52% of the participants, with a mean age of 38.57 years. In all, 54.6%, 53.9%, and 56.6% were classified as having anxiety, depression, and stress respectively. Logistic regression analysis showed that the odds of mental health components including anxiety, depression, or stress was not significantly different across the tertiles of the obesity indicators. We observed a significant dose-response relationship between BAI and ABSI and the risk of anxiety (PBenjamini-Hochberg 0.028 > Pdose-response 0.023) and stress (PBenjamini-Hochberg 0.028 > Pdose-response 0.003) but not depression (PBenjamini-Hochberg 0.014

Abstract Background Fetal movement monitoring is one of the strategies used to assess the fetus’s health. Until now, most studies focused on the decreased fetal movement and neonatal outcome, although this systematic review and meta-analysis is designed to assess the association between increased fetal movements (IFM) with perinatal outcomes. Method The electronic databases including PubMed, Scopus, Web of Science, and EMBASE were systematically searched for studies investigating the perinatal outcome of women with increased fetal movements from inception to July 2023. Following that, a random-effect meta-analysis model was used to obtain the combined diagnostic and predictive parameters including perinatal mortality (still birth and early neonatal mortality), operative delivery, Apgar score, neonatal resuscitation at birth and NICU Admission. Results After the initial screening, seven studies examining the association between increased third trimester fetal movement and various perinatal outcomes were included. Meta-analysis revealed a significant reduction in the risk of cesarean delivery among patients with IFM compared to controls, suggesting a potential protective effect during childbirth. However, no statistically significant difference was observed in birth weight, small or large for gestational age births, neonatal intensive care unit admission, maternal age, umbilical cord around the neck, gestational diabetes mellitus, and hypertension, indicating that IFM may not be a major predictor of adverse perinatal outcomes or maternal conditions. Notably, IFM was significantly associated with a higher likelihood of labor induction. Conclusion The findings suggest that IFM may have a protective effect against cesarean delivery. Additionally, IFM does not appear to be significantly associated with maternal age, umbilical cord around the neck, gestational diabetes mellitus and hypertension. However, the observed significant association with labor induction warrants further investigation.