Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it’s cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 μg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 μg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.
Unusual Aspects of Charge Regulation in Flexible Weak Polyelectrolytes

This article reviews the state of the art of the studies on charge regulation (CR) effects in flexible weak polyelectrolytes (FWPE). The characteristic of FWPE is the strong coupling of ionization and conformational degrees of freedom. After introducing the necessary fundamental concepts, some unconventional aspects of the the physical chemistry of FWPE are discussed. These aspects are: (i) the extension of statistical mechanics techniques to include ionization equilibria and, in particular, the use of the recently proposed Site Binding-Rotational Isomeric State (SBRIS) model, which allows the calculation of ionization and conformational properties on the same foot; (ii) the recent progresses in the inclusion of proton equilibria in computer simulations; (iii) the possibility of mechanically induced CR in the stretching of FWPE; (iv) the non-trivial adsorption of FWPE on ionized surfaces with the same charge sign as the PE (the so-called “wrong side” of the isoelectric point); (v) the influence of macromolecular crowding on CR.
Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs

The accurate identification and classification of soybean mutant lines is essential for developing new plant varieties through mutation breeding. However, most existing studies have focused on the classification of soybean varieties. Distinguishing mutant lines solely by their seeds can be challenging due to their high genetic similarities. Therefore, in this paper, we designed a dual-branch convolutional neural network (CNN) composed of two identical single CNNs to fuse the image features of pods and seeds together to solve the soybean mutant line classification problem. Four single CNNs (AlexNet, GoogLeNet, ResNet18, and ResNet50) were used to extract features, and the output features were fused and input into the classifier for classification. The results demonstrate that dual-branch CNNs outperform single CNNs, with the dual-ResNet50 fusion framework achieving a 90.22 ± 0.19% classification rate. We also identified the most similar mutant lines and genetic relationships between certain soybean lines using a clustering tree and t-distributed stochastic neighbor embedding algorithm. Our study represents one of the primary efforts to combine various organs for the identification of soybean mutant lines. The findings of this investigation provide a new path to select potential lines for soybean mutation breeding and signify a meaningful advancement in the propagation of soybean mutant line recognition technology.
Effect of Heavy-Metal-Resistant PGPR Inoculants on Growth, Rhizosphere Microbiome and Remediation Potential of Miscanthus × giganteus in Zinc-Contaminated Soil

Microbial-assisted phytoremediation is considered a more effective approach to soil rehabilitation than the sole use of plants. Mycolicibacterium sp. Pb113 and Chitinophaga sp. Zn19, heavy-metal-resistant PGPR strains originally isolated from the rhizosphere of Miscanthus × giganteus, were used as inoculants of the host plant grown in control and zinc-contaminated (1650 mg/kg) soil in a 4-month pot experiment. The diversity and taxonomic structure of the rhizosphere microbiomes, assessed with metagenomic analysis of rhizosphere samples for the 16S rRNA gene, were studied. Principal coordinate analysis showed differences in the formation of the microbiomes, which was affected by zinc rather than by the inoculants. Bacterial taxa affected by zinc and the inoculants, and the taxa potentially involved in the promotion of plant growth as well as in assisted phytoremediation, were identified. Both inoculants promoted miscanthus growth, but only Chitinophaga sp. Zn19 contributed to significant Zn accumulation in the aboveground part of the plant. In this study, the positive effect of miscanthus inoculation with Mycolicibacterium spp. and Chitinophaga spp. was demonstrated for the first time. On the basis of our data, the bacterial strains studied may be recommended to improve the efficiency of M. × giganteus phytoremediation of zinc-contaminated soil.
Trends in Incidence, Survival and Mortality of Gastric Cancer in the United States: A Population-Based Study, 2001-2015

Background: Gastric cancer remains one of the leading causes of death, and a burden of public health in the United States (US). The aim of the study was to provide updates to gastric cancer estimates, and analyzed the long-term trends in incidence, survival, and mortality of gastric cancer in the US, which was helpful for the monitoring of the screening program and the prevention strategy. Methods: The incidence, and long-term trends in incidence, survival, and mortality of gastric cancer in the US from 2001 to 2015 were analyzed. The data were obtained from the Surveillance, Epidemiology, and End Results (SEER) Database. Age-adjusted incidence rates were calculated, joinpoint regression and age-period-cohort analyses were conducted. All statistical tests were 2-sided. Results: The overall age-adjusted incidence of gastric cancer decreased over the study period, with an annual percentage change (APC) of -1.4% (95% confidence interval [CI] = -1.1 to 13.3; P < 0.001). The incidence rates leveled off at an earlier age (< 45 years) and creased obviously with age. The age rate deviations increased sharply before the age of 47.5 years (age rate deviation = 0.92; 95% CI = 0.71 to 1.13). The 5-year mortality attributed to gastric cancer declined from 65.98% to 56.29% over the study period. The trend of 5-year mortality attributed to gastric cancer showed no significant fluctuation. The hazard ratio for 5-year all-cause death increased with cancer stage from 1.22 (95% CI = 1.13 to 1.33; P < 0.001) to 4.71 (95% CI = 4.40 to 5.06; P < 0.001). Conclusion: During the study period, the incidence decreased, while the survival rate increased slightly. Specially, the trend of 5-year mortality attributed to gastric cancer did not vary significantly. The data demonstrated that the prognosis of gastric cancer in the US remained challenging.
Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions

Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis.
The Role of Cosolvent–Water Interactions in Effects of the Media on Functionality of Enzymes: A Case Study of Photobacterium leiognathi Luciferase

Simple Summary Traditionally, enzymes have been studied in standardized buffers, but in real life, inside cells, they have to function under conditions far from “ideal”. The intracellular milieu is crowded by numerous molecules with various physical and chemical properties. What consequences does it have for enzymatic reactions? How do enzymes deal with disturbing neighbors? To examine this issue, we studied the mechanisms of influence of seven model substances with different molecular sizes on the reaction catalyzed by bacterial luciferase. This reaction passes through several stages and ends with light emission. We analyzed the responses rate in different stages to the addition of alcohols, saccharides, and biopolymers and found the counteraction of two effects. On the one hand, increased viscosity slows down many of the stages, both associative and dissociative. On the other hand, it is accompanied by an increase in the catalytic constant of luciferase, which compensates for kinetic disadvantages. We assumed that both effects could be caused by the same property of the added substances—their ability to interact with water. Our findings serve not only to shed light on the organization of the cellular metabolism, but also to improve enzyme-based biotechnologies using the recipes of nature. Abstract A complex heterogeneous intracellular environment seems to affect enzymatic catalysis by changing the mobility of biomolecules, their stability, and their conformational states, as well as by facilitating or hindering continuously occurring interactions. The evaluation and description of the influence of the cytoplasmic matrix components on enzymatic activity are problems that remain unsolved. In this work, we aimed to determine the mechanisms of action of two-component media with cosolvents of various molecular sizes on the complex multi-stage bioluminescent reaction catalyzed by bacterial luciferase. Kinetic and structural effects of ethylene glycol, glycerol, sorbitol, glucose, sucrose, dextran, and polyethylene glycol on bacterial luciferase were studied using stopped-flow and fluorescence spectroscopy techniques and molecular dynamics simulations. We have found that diffusion limitations in the presence of cosolvents promote the stabilization of flavin substrate and peroxyflavin intermediate of the reaction, but do not provide any advantages in bioluminescence quantum yield, because substrate binding is slowed down as well. The catalytic constant of bacterial luciferase has been found to be viscosity-independent and correlated with parameters of water–cosolvent interactions (Norrish constant, van der Waals interaction energies). Crowding agents, in contrast to low-molecular-weight cosolvents, had little effect on peroxyflavin intermediate decay and enzyme catalytic constant. We attributed specific kinetic effects to the preferential interaction of the cosolvents with enzyme surface and their penetration into the active site.
Periodontal Disease and Other Adverse Health Outcomes Share Risk Factors, including Dietary Factors and Vitamin D Status

For nearly a century, researchers have associated periodontal disease (PD) with risks of other adverse health outcomes such as cardiovascular disease, diabetes mellitus, and respiratory diseases, as well as adverse pregnancy outcomes. Those findings have led to the hypothesis that PD causes those adverse health outcomes either by increasing systemic inflammation or by the action of periodontopathic bacteria. However, experiments largely failed to support that hypothesis. Instead, the association is casual, not causal, and is due to shared underlying modifiable risk factors, including smoking, diet, obesity, low levels of physical activity, and low vitamin D status. Diabetes mellitus is also considered a risk factor for PD, whereas red and processed meat are the most important dietary risk factors for diabetes. Because PD generally develops before other adverse health outcomes, a diagnosis of PD can alert patients that they could reduce the risk of adverse health outcomes with lifestyle changes. In addition, type 2 diabetes mellitus can often be reversed rapidly by adopting an anti-inflammatory, nonhyperinsulinemic diet that emphasizes healthful, whole plant-based foods. This review describes the evidence that proinflammatory and prohyperinsulinemia diets and low vitamin D status are important risk factors for PD and other adverse health outcomes. We also make recommendations regarding dietary patterns, food groups, and serum 25-hydroxyvitamin D concentrations. Oral health professionals should routinely inform patients with PD that they could reduce their risk of severe PD as well as the risks of many other adverse health outcomes by making appropriate lifestyle changes.
MPL gene mutation is a possible risk factor for thrombosis in patients with essential thrombocythemia in Japan

ABSTRACT Objectives Since MPL mutation is a rare driver gene mutation found in a small number of essential thrombocythemia (ET) patients, the clinical characteristics of patients with MPL mutations and their association with thrombotic events have not yet been elucidated in Japan. Methods We enrolled 579 Japanese ET patients based on the diagnostic criteria of the WHO classification 2017 and compared clinical characteristics of MPL-mutated patients (n = 22; 3.8%) to JAK2V617F-mutated (n = 299; 51.6%), CALR-mutated (n = 144; 24.9%), and triple-negative (TN) (n = 114; 19.7%) patients. Results Thrombosis during follow up was observed in 4 out of 22 (18.2%) in the MPL-mutated group, which was the highest among all driver gene mutation groups (JAK2V617F-mutated, 8.7%; CALR-mutated, 3.5%; TN,1.8%). The MPL- and JAK2V617F-mutated groups had worse thrombosis-free survival (TFS) than the CALR-mutated (p = 0.043) and TN groups (p = 0.006). Univariable analysis revealed that a history of thrombosis was a possible risk factor for thrombosis among MPL-mutated patients (hazard ratio: 9.572, p = 0.032). Conclusions MPL-mutated ET patients should require more intensive management to prevent recurrence of thrombosis.
Indicators to monitor the status of the tree of life

Following the failure to fully achieve any of the 20 Aichi biodiversity targets, the future of biodiversity rests in the balance. The Convention on Biological Diversity's Kunming–Montreal Global Biodiversity Framework (GBF) presents the opportunity to preserve nature's contributions to people (NCPs) for current and future generations by conserving biodiversity and averting extinctions. There is a need to safeguard the tree of life—the unique and shared evolutionary history of life on Earth—to maintain the benefits it bestows into the future. Two indicators have been adopted within the GBF to monitor progress toward safeguarding the tree of life: the phylogenetic diversity (PD) indicator and the evolutionarily distinct and globally endangered (EDGE) index. We applied both to the world's mammals, birds, and cycads to show their utility at the global and national scale. The PD indicator can be used to monitor the overall conservation status of large parts of the evolutionary tree of life, a measure of biodiversity's capacity to maintain NCPs for future generations. The EDGE index is used to monitor the performance of efforts to conserve the most distinctive species. The risk to PD of birds, cycads, and mammals increased, and mammals exhibited the greatest relative increase in threatened PD over time. These trends appeared robust to the choice of extinction risk weighting. EDGE species had predominantly worsening extinction risk. A greater proportion of EDGE mammals (12%) had increased extinction risk compared with threatened mammals in general (7%). By strengthening commitments to safeguarding the tree of life, biodiversity loss can be reduced and thus nature's capacity to provide benefits to humanity now and in the future can be preserved.
Development of a Discriminative Dissolution Method, Using In-Silico Tool for Hydrochlorothiazide and Valsartan Tablets

Hydrochlorothiazide (HTZ) and Valsartan (VAL) are poorly soluble drugs in BCS classes IV and II. This study aimed to develop a method to assess the dissolution profile of tablets containing HTZ (12.5 mg) and VAL (160 mg) as a fixed-dose combination, using in silico tools to evaluate products marketed in Brazil and Peru. Firstly, in vitro dissolution tests were performed using a fractional factorial design 33−1. Then, DDDPlus™ was used to carry out experimental design assays of a complete factorial design 33. Data from the first stage were used to obtain calibration constants for in silico simulations. The factors used in both designs were formulation, sinker use, and rotation speed. Finally, effects and factor interaction assessment was evaluated based on a statistical analysis of the dissolution efficiency (DE) obtained from simulations. Thus, the established final conditions of the dissolution method were 900 mL of phosphate buffer pH 6.8, 75 rpm of rotation speed, and sinker use to prevent formulation floating. The reference product stood out because of its higher DE than other formulations. It was concluded that the proposed method, in addition to ensuring total HTZ and VAL release from formulations, has adequate discriminative power.
Effect of Laser Beam Profile on Thermal Transfer, Fluid Flow and Solidification Parameters during Laser-Based Directed Energy Deposition of Inconel 718

The profile of the laser beam plays a significant role in determining the heat input on the deposition surface, further affecting the molten pool dynamics during laser-based directed energy deposition. The evolution of molten pool under two types of laser beam, super-Gaussian beam (SGB) and Gaussian beam (GB), was simulated using a three-dimensional numerical model. Two basic physical processes, the laser–powder interaction and the molten pool dynamics, were considered in the model. The deposition surface of the molten pool was calculated using the Arbitrary Lagrangian Eulerian moving mesh approach. Several dimensionless numbers were used to explain the underlying physical phenomena under different laser beams. Moreover, the solidification parameters were calculated using the thermal history at the solidification front. It is found that the peak temperature and liquid velocity in the molten pool under the SGB case were lower compared with those for the GB case. Dimensionless numbers analysis indicated that the fluid flow played a more pronounced role in heat transfer compared to conduction, especially in the GB case. The cooling rate was higher for the SGB case, indicating that the grain size could be finer compared with that for the GB case. Finally, the reliability of the numerical simulation was verified by comparing the computed and experimental clad geometry. The work provides a theoretical basis for understanding the thermal behavior and solidification characteristics under different laser input profile during directed energy deposition.
The Evolution of Structural Defects under Irradiation in W by Molecular Dynamics Simulation

Tungsten (W) can be used in plasma-facing components in a fusion reactor because of its excellent radiation resistance. Some studies have found that nanocrystalline metals with a high density of grain boundary show a higher ability to resist radiation damage compared to conventional coarse-grained materials. However, the interaction mechanism between grain boundary and defect is still unclear. In the present study, molecular dynamics simulations were carried out to explore the difference of defect evolution in single-crystal and bicrystal W, while the effects of temperature and the energy of the primary knocked atom (PKA) were taken into account. The irradiation process was simulated at the temperature range of 300 to 1500 K, and the PKA energy varied from 1 to 15 keV. The results show that the generation of defects is more sensitive to the energy of PKA than temperature; the number of defects increases at the thermal spike stage with the increase of the PKA energy, but the correlation with temperature is not strong. The presence of the grain boundary prevented the recombination of interstitial atoms and vacancies during the collision cascades, and the vacancies were more likely to form large clusters than interstitial atoms in the bicrystal models. This can be ascribed to the strong segregation tendency of the interstitial atoms to grain boundaries. The simulations provide useful information for understanding the role of grain boundaries in the evolution of irradiated structural defects.
Fermented Lettuce Extract Induces Immune Responses through Polarization of Macrophages into the Pro-Inflammatory M1-Subtype

It has been reported that lettuce and its bioactive compounds enhance the host immune system by acting as immune modulators. This study aimed to identify the immunological effect of fermented lettuce extract (FLE) on macrophages. To evaluate the efficacy of FLE in enhancing macrophage function, we measured and compared the levels of macrophage activation-related markers in FLE- and lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Treatment with FLE activated RAW 264.7 macrophages, increased their phagocytic ability, and increased the production of nitric oxide (NO) and pro-inflammatory cytokine levels—similar to LPS. The effects of FLE on M1/M2 macrophage polarization were investigated by determining M1 and M2 macrophage transcript markers in mouse peritoneal macrophages. The FLE-related treatment of peritoneal macrophages enhanced the expression of M1 markers but reduced IL-4 treatment-induced M2 markers. After the generation of tumor-associated macrophages (TAMs), alterations in the levels of M1 and M2 macrophage markers were measured after treatment with FLE. The FLE-related treatment of TAMs increased the expression and production of pro-inflammatory cytokines and also led to the enhanced apoptosis of pancreatic cancer cells. These findings suggest that FLE may be useful for macrophage-targeted cancer therapy because of its ability to regulate the activation and polarization of macrophages in the tumor microenvironment.