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Characterization of transcriptomics during aging and genes required for lifespan in Drosophila intestine
Evaluation and optimization simulation of land suitability for village construction in the urban rural integration zone empirical test based on 49 administrative villages in Chuzhou city China
Unveiling Berberine analogues as potential inhibitors of Escherichia coli FtsZ through machine learning molecular docking and molecular dynamics approach
Abstract The bacterial cell division protein FtsZ, a crucial GTPase, plays a vital role in the formation of the contractile Z-ring, which is essential for bacterial cytokinesis. Consequently, inhibiting FtsZ could prevent the formation of proto-filaments and interfere with the cell division machinery. The remarkable conservation of FtsZ across diverse bacterial species makes it a promising drug target for combating drug resistance. In the present study, 1072 berberine analogues were screened for favorable pharmacokinetic properties. A total of 60 compounds that fulfilled the drug-likeliness criteria and were found to be non-toxic were selected for virtual screening against Escherichia coli FtsZ protein (PDB ID: 8GZY). Molecular docking revealed a strong binding affinity of ZINC000524729297 (− 8.73 kcal/mol) and ZINC000604405393 (and − 8.55 kcal/mol) with FtsZ by strong intermolecular hydrogen bonds and hydrophobic interactions. Subsequently, the docking profiles were validated through a 500 ns MD simulation and MMPBSA analysis of the FtsZ-ligand complexes. The analysis revealed the FtsZ- ZINC524729297 and FtsZ-ZINC000604405393 complexes had the lowest root-mean-square deviation with lowest binding energy and enhanced conformational stability in a dynamic environment. These findings suggest that ZINC524729297 and ZINC000604405393 are the potent lead compound that targets FtsZ and requires further experimental validation.
Characterisation of the Gillenia S-locus provides insight into evolution of the nonself-recognition self-incompatibility system in apple
Abstract Self-incompatibility (SI) in plants has evolved independently multiple times and S-RNase-based gametophytic self-incompatibility (GSI) is most common. The Rosaceae family possesses both self-recognition (Prunus) and nonself-recognition (Malus) GSI systems, and the latter is widespread in flowering plants. Gillenia trifoliata is a Rosaceae species related to Prunus and Malus, providing utility for understanding SI evolution. Gillenia is sister taxon to Malus, but unlike Malus, has not undergone polyploidisation. In addition, the common ancestor of Gillenia and Prunus is close to the origin of the subfamily. Using a highly contiguous Gillenia genome, orthologous regions to both Malus and Prunus S-loci were identified. Only the Prunus-like S-locus was highly polymorphic and had signatures of a functional S-locus including positive selection of the S-RNase. This suggests a self-recognition system controls SI in Gillenia, and the common ancestors of Gillenia and Prunus, and Gillenia and the apple tribe, likely had a self-recognition SI system. Comparative genomics between Gillenia and Malus suggest apple lost the self-recognition mechanism, and a nonself-recognition mechanism evolved independently from a rudimentary locus with at least one male S-determinant. Repetitive sequences in the Malus-like S-locus in Gillenia may facilitate illegitimate recombination, suggesting putative mechanisms of evolution of nonself-recognition S-loci.
Exploring differences in signaling pathways of the circadian clock and neuromodulators in obstructive sleep apnea
PKM2 modulates chemotherapy sensitivity by regulating autophagy and predicts the prognosis and immunity in pancancer
Control of three-level quadratic DC-DC boost converters for energy systems using various technique-based MPPT methods
Machine learning-based prediction of heating values in municipal solid waste
Global burden of hypertensive heart disease and attributable risk factors, 1990–2021: insights from the global burden of disease study 2021
A deep learning-based multimodal medical imaging model for breast cancer screening
Multiscale deformed attention networks for white blood cell detection
Substituents introduction of methyl and methoxy functional groups on resveratrol stabilizes mTOR binding for autophagic cell death induction
Abstract The regulation of the mammalian target of rapamycin (mTOR) protein by cancer cells can lead to uncontrol of cancer cell growth and cancer therapy resistance. The drug discovery of the anticancer agent 5-(3-hydroxy-4-methoxyphenethyl)-2-methoxy-3-methylphenol (SM-3), a derivative of resveratrol by substituting a methyl group at the hydroxy group of ring A and adding a methoxy group at the para position of ring B, shows promising potential for targeting autophagy to induce cell death and suppress cancer stem cells (CSCs) through the inhibition of the mTOR protein. In human lung cancer cells, SM-3 showed greater efficacy, with lower IC50 values of 72.74 ± 0.13, 67.66 ± 0.10, and 43.24 ± 0.11 µM in A549, H292, and H460 cells, respectively, compared to the parent compound, Resveratrol (Res). Moreover, the selectivity index (SI) values for BEAS2B cells compared to tumor cells treated with SM-3 were 10.99, 11.81, and 18.49 for A549, H292, and H460 cell lines, respectively. Therefore, SM-3 treatment led to reduced proliferation rates and colony formation in lung cancer cells. In our study, spheroids treated with SM-3 showed a higher proportion of dead spheroids compared to those treated with Res. Additionally, SM-3 treatment resulted in decreased expression of stem cell markers (CD133, CD44, and ALDH1A1) and transcription factors (OCT4, NANOG, and SOX2) in spheroids and organoids from human lung cancer cells by inhibiting the mTOR/pAkt pathway. SM-3 was also found to induce autophagic cell death, as indicated by Monodansylcadaverine staining, acidic vesicle formation, and the conversion of LC3BI to LC3BII. Using MM/GBSA calculations, SM-3 exhibited a stronger binding affinity (-25.09 kcal/mol) compared to Res (-18.85 kcal/mol). SM-3 also displayed greater stability during the entire simulation, maintaining lower RMSD values of 2–3 Å even after 80 ns. In summary, the introduction of methyl and methoxy functional groups on Res to create SM-3 effectively suppressed cancer spheroids and organoids formation in lung cancer cells by targeting the upstream mTOR/pAkt pathway.
Genetic diversity of Ancylostoma ceylanicum and first molecular detection of Ancylostoma braziliense in stray dogs from Sarawak, Malaysia
Abstract Hookworms are blood-sucking intestinal parasites that can cause anaemia and protein loss in humans. Ancylostoma ceylanicum, a zoonotic hookworm species of dogs, is the second most common cause of human hookworm infections. With the increasing anthelmintic resistance risks and the uncontrolled stray dog population in Sarawak Borneo, East Malaysia, understanding the genetic structure of A. ceylanicum is crucial for tracking mutation patterns and assessing zoonotic transmission risks. This study determined the prevalence and genetic diversity of dog hookworm species using microscopy, PCR and sequencing, revealing A. ceylanicum (43.6%; 89/204), followed by mixed infections of A. ceylanicum and A. braziliense (9.3%; 19/204), single infections of A. caninum (6.3%; 13/204), and A. braziliense (1.4%; 3/204) in stray dogs in East Malaysia (Sarawak Borneo). Phylogenetic analysis of the cytochrome oxidase subunit 1 (COX1) gene showed that A. ceylanicum from Sarawak Borneo clustered across all major clades, indicating high genetic divergence and admixture. Haplotype analysis revealed that the Malaysian A. ceylanicum population highly mirrors those in Cambodia and Thailand, suggesting significant gene flow across Southeast Asia, while regional disparities exist compared to other countries. These findings provide critical epidemiological insights for hookworm control strategies, including stray dog management and potential adjustments to mass drug administration programs. The high genetic connectivity of A. ceylanicum population across borders underscores the need for enhanced surveillance, One Health approaches, and monitoring anthelminthic resistance to mitigate the risk of zoonotic transmission.
Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs
Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. Initially, statistical methods were employed to analyze the input data before applying machine learning techniques. These models take into consideration the mobility ratio (M), reservoir permeability variation (V), water-oil production ratio (WOR), and initial water saturation (SWi). 1054 datasets were utilized to develop machine-learning models. ANN-based correlation was developed to estimate the overall oil recovery of waterflooding. The ANN proposed model achieves a high coefficient of determination (R2) of 0.999 and a low root-mean-square error (RMSE) of 0.0063 on the validation dataset. On the other hand, the other machine learning models like RF, K-NN, and SVM achieve accurate estimation of overall oil recovery (R), where the coefficients of determination (R2) values are 0.97, 0.95, and 0.80 and the RMSE scores are 0.0282, 0.0405, and 0.0629 on the validation dataset, respectively. The innovative application of such ML models demonstrates significant improvements in prediction accuracy and reliability, offering a robust solution for optimizing oil recovery processes. These machine learning models provide the industry and research with efficient and economical tools for accurately estimating oil recovery in waterflooding operations within heterogeneous reservoirs.