Browse Articles

Evaluating the spatial pattern of water quality of the Nile River, Egypt, through multivariate analysis of chemical and biological indicators

Abstract The Nile River, known as the "giver of life," serves as Egypt’s main source of fresh water. A total of 28 sites along the Nile River were selected during the winter and summer of 2022 to illustrate spatial–temporal variation and indicate potential sources of pollution. The study showed that all chemical and physical parameters are within permissible limits. Based on the abiotic parameters, discriminant analysis (DA) classified the collected Nile sites into three groups (A, B, and C). Group A included the southern sites characterized by high transparency and low levels of electrical conductivity (EC), pH, dissolved oxygen (DO), biological oxygen demand (BOD), and chemical oxygen demand (COD). Group B included the middle sites and was characterized by the relatively high means of EC, total dissolved solid (TDS), pH, DO, BOD, and COD. Group C included the northern sites, which are characterized by low transparency and the highest value of nutrients and EC. A total of 113 phytoplankton species belonging to seven phyla were recorded, while a total of 52 zooplankton species were recorded. The lowest abundance and diversity of phytoplankton and zooplankton were detected at the southern sites, which increased gradually to attain the highest density and diversity in the northern sites. The submerged macrophyte species were recorded in the study area with low species diversity, and Myriophyllum spicatum was the dominant one. 98 epiphytic diatoms and 30 species of macroinvertebrates attached to macrophytes were recorded. The blood parameters of Oreochromis niloticus were within normal limits except for those collected from the Greater Cairo governorate sites in the north. The study showed an improvement in Nile water quality due to increased water levels and water discharged into the Nile.

Tandem metabolic reaction–based sensors unlock in vivo metabolomics

Mimicking metabolic pathways on electrodes enables in vivo metabolite monitoring for decoding metabolism. Conventional in vivo sensors cannot accommodate underlying complex reactions involving multiple enzymes and cofactors, addressing only a fraction of enzymatic reactions for few metabolites. We devised a single-wall-carbon-nanotube-electrode architecture supporting tandem metabolic pathway–like reactions linkable to oxidoreductase-based electrochemical analysis, making a vast majority of metabolites detectable in vivo. This architecture robustly integrates cofactors, self-mediates reactions at maximum enzyme capacity, and facilitates metabolite intermediation/detection and interference inactivation through multifunctional enzymatic use. Accordingly, we developed sensors targeting 12 metabolites, with 100-fold-enhanced signal-to-noise ratio and days-long stability. Leveraging these sensors, we monitored trace endogenous metabolites in sweat/saliva for noninvasive health monitoring, and a bacterial metabolite in the brain, marking a key milestone for unraveling gut microbiota–brain axis dynamics.

Experimental investigation on failure processes and characteristics of landslide dams with different inflow conditions

Understanding Nash epidemics

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or “bootstrap,” rational behavior.

Serum ferritin levels and risk of gestational diabetes mellitus: A cohort study

Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model

Erythroid progenitor cell–mediated spleen–tumor interaction deteriorates cancer immunity

Understanding both local and systemic immunity is essential to optimizing the effectiveness of immunotherapy. However, the dynamic alterations in systemic immunity during tumor development are yet to be clearly defined. Here, we identified a previously unrecognized connection that bridges the interaction between the spleen and tumor through erythroid progenitor cells (EPCs), which suppress tumor immunity and promote tumor progression. We performed the single-cell RNA-seq and RNA-seq to demonstrate the presence of EPCs and identify the characteristic and an immunomodulatory role of EPCs during tumor progression. These tumor-hijacked EPCs proliferate in situ in spleens and impaired systemic and local antitumor response through the interaction between tumor and spleen. Specifically, the splenic CD45 − EPCs secreted heparin-binding growth factor to regulate PD-L1-mediated immunosuppression of splenic CD45 + EPCs. Educated CD45 + EPCs from the spleen then migrated to the tumors via the CCL5/CCR5 axis, thereby weakening local antitumor immunity. Consequently, targeting EPCs not only revitalized antitumor immunity but also improved the anti-PD-L1 effect by promoting intratumoral T cell infiltration. Importantly, CD45 + EPCs are associated with immunosuppression and reduced survival in patients with head and neck squamous cell carcinoma. Collectively, these findings reveal the role of EPCs in orchestrating the interaction between the spleen and tumor, which could have significant implications for the development of more effective cancer immunotherapy.

VHL ameliorates arecoline-induced oral submucosal fibrosis by promoting HDAC6 ubiquitination and blocking NF-κB pathway

S-Nitrosylation of CRTC1 in Alzheimer’s disease impairs CREB-dependent gene expression induced by neuronal activity

cAMP response element-binding protein (CREB)-regulated transcription coactivator 1 (CRTC1) plays an important role in synaptic plasticity, learning, and long-term memory formation through the regulation of neuronal activity-dependent gene expression, and CRTC1 dysregulation is implicated in Alzheimer’s disease (AD). Here, we show that increased S-nitrosylation of CRTC1 (forming SNO-CRTC1), as seen in cell-based, animal-based, and human-induced pluripotent stem cell (hiPSC)-derived cerebrocortical neuron-based AD models, disrupts its binding with CREB and diminishes the activity-dependent gene expression mediated by the CRTC1/CREB pathway. We identified Cys216 of CRTC1 as the primary target of S-nitrosylation by nitric oxide (NO)-related species. Using CRISPR/Cas9 techniques, we mutated Cys216 to Ala in hiPSC-derived cerebrocortical neurons bearing one allele of the APP Swe mutation (AD-hiPSC neurons). Introduction of this nonnitrosylatable CRTC1 mutant rescued defects in AD-hiPSC neurons, including decreased neurite length and increased neuronal cell death. Additionally, expression of nonnitrosylatable CRTC1 in vivo in the hippocampus rescued synaptic plasticity in the form of long-term potentiation in 5XFAD mice. Taken together, these results demonstrate that formation of SNO-CRTC1 contributes to the pathogenesis of AD by attenuating the neuronal activity-dependent CREB transcriptional pathway, and suggests a therapeutic target for AD.

Analysis of stress responses in medical students during simulated pericardiocentesis training using virtual reality and 3D-printed mannequin

Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products

Multiscale toughening mechanisms in biomimetic tendon-like hydrogels

Mimicking hierarchical structures found in nature, such as nacre and tendon, has led to remarkable successes in the creation of biomimetic materials with exceptional properties. The depth of knowledge derived from nature extends far beyond mere trial-and-error fabrication by providing deep insights into the toughening mechanisms that are integral to natural materials. A key challenge is understanding how these toughening mechanisms can be effectively translated into biomimetic materials. Here, we characterize the multiscale mechanical behavior of tendon-like fibrous hydrogels, unraveling the intricate toughening mechanisms at play across multiple scales—from dynamic molecular interactions and nanoscale fibril sliding, to anisotropic microscale characteristics and macroscopic performance—using a combination of experimental and simulation approaches. Additionally, we address the open question of how hierarchical structures exhibit mechanical properties at different scales, demonstrating that hydrogels, fibrils, and chains take up successively lower levels of strain in a ratio of 11.5:3.2:2. This work establishes a comprehensive framework for exploring nature-inspired materials, marking a significant step forward in the advancement of biomimetic technology.

Rheumatoid arthritis and risk of pancreatitis: a nationwide cohort study

Photon-drag photovoltaic effects and quantum geometric nature

The bulk photovoltaic effect (BPVE) generates a direct current photocurrent under uniform irradiation and is a nonlinear optical effect traditionally studied in noncentrosymmetric materials. The two main origins of BPVE are the shift and injection currents, arising from transitions in electron position and electron velocity during optical excitation, respectively. Recently, it was proposed that photon-drag effects could unlock BPVE in centrosymmetric materials. However, experimental progress remains limited. In this work, we provide a comprehensive theoretical analysis of photon-drag effects inducing BPVE (photon-drag BPVE). Notably, we find that photon-drag BPVE can be directly linked to quantum geometric tensors. Additionally, we propose that photon-drag shift currents can be fully isolated from other current contributions in nonmagnetic centrosymmetric materials. We apply our theory explicitly to the 2D topological insulator 1 T ′ -WTe 2 . Furthermore, we investigate photon-drag BPVE in a centrosymmetric magnetic Weyl semimetal, where we demonstrate that linearly polarized light generates photon-drag shift currents.

Unusual presentations of myasthenia gravis and misdiagnosis

Essential oils and Lactobacillus metabolites as alternative antibiofilm agents against foodborne bacteria and molecular analysis of biofilm regulatory genes

Abstract The formation of biofilm by foodborne pathogens increases the risk of foodborne diseases, resulting in major health risks. Research on strategies for eliminating biofilm formation by foodborne pathogens is urgently needed. Therefore, the objective of this study was to construct a new technique for controlling foodborne bacteria and inhibiting the biosynthesis of biofilm via using natural products. The essential orange oil (EOO) and cell-free filtrate of Lactobacillus pentosus RS2 were used as antibacterial and antibiofilm agents against B. cereus RS1, the strongest biofilm-forming strain. The mixture of cell-free filtrate (CFF) and EOO (CFF/EOO) was the best antibiofilm agent under all tested conditions. The minimal inhibitory concentration (MIC) test revealed that 400 μl ml−1 CFF and 16 μl ml−1 EOO completely inhibited the growth of B. cereus. The treatment of three commercial surfaces with CFF/EOO resulted in a high reduction in biofilm synthesis, with adhesion percentages of 33.3, 36.3, and 40.8% on stainless steel, aluminum foil, and aluminum, respectively. The aluminum surface had the greatest adhesion with B. cereus RS1 among the three tested surfaces. These results were confirmed by expression analysis of three essential coding genes, sinR, calY, and spo0A, participating in biofilm formation in B. cereus. The biofilm-negative regulator gene sinR was overexpressed, whereas the biofilm-positive regulator genes calY and spo0A were down-expressed in B. cereus RS1 after treatment with antibiofilm agents, compared with those in the untreated sample. This study revealed that CFF/EOO was more effective at activating sinR (2.099 ± 0.167-fold increase) and suppressing calY and spo0A (0.314 ± 0.058 and0.238 ± 0.04-fold decrease, respectively) compared to control. This result confirmed the biochemical estimation of biofilm formation in B. cereus after treatment with all the experimental agents. The EOO and CFF of L. pentosus RS2 can be used as strong antibacterial and antibiofilm agents against foodborne bacteria. These products reduced the biofilm formation on trade surfaces affecting the expression of three essential biofilm regulatory genes. This study considered novel research concerning the potential antibiofilm activity of EOO combined with CFF of L. pentosus and the molecular analysis of genes regulating biofilm production under stress of CFF/EOO.

Performance analysis and prediction of asphalt pavement containing municipal solid waste incineration bottom ash aggregate based on MEPDG

Abstract Municipal solid waste incinerator (MSWI) bottom ash (BA) is the main product of municipal solid waste after being burned. MSWI bottom ash aggregate (BAA) which is made from processed BA can be used in road engineering due to its strength and gradation. And it can be provide more choices for road engineering aggregates and relieve the demands for natural aggregates in road engineering construction. In order to verify the difference between BA asphalt pavement and ordinary asphalt pavement in engineering practice, the Mechanistic-Empirical Pavement Design Guide (MEPDG) was used to analyze and predict the road performance of BA asphalt pavement, such as rutting, fatigue cracking, temperature cracking, and pavement smoothness. The results showed that the incorporation of MSWI BA had a great impact on the rutting depth of the pavement, but was less affected by temperature changes and had little effect on the fatigue cracking and smoothness of the pavement. Overall, MSWI BA did not degrade the long-term performance of asphalt pavements and is suitable for replacing part of the natural aggregates for road construction.

Physics-informed deep learning for stochastic particle dynamics estimation

Single-particle tracking has enabled quantitative studies of complex systems, providing nanometer localization precision and millisecond temporal resolution in heterogeneous environments. However, at micro- or nanometer scales, probe dynamics become inherently stochastic due to Brownian motion and complex interactions, leading to varied diffusion behaviors. Typically, analysis of such trajectory data involves certain moving-window operation and assumes the existence of some pseudo–steady states, particularly when evaluating predefined parameters or specific types of diffusion modes. Here, we introduce the stochastic particle-informed neural network (SPINN), a physics-informed deep learning framework that integrates stochastic differential equations to model and infer particle diffusion dynamics. The SPINN autonomously explores parameter spaces and distinguishes between deterministic and stochastic components with single-frame resolution. Using the anomalous diffusion dataset, we validated SPINN’s ability to reduce frame-to-frame variability while preserving key statistical correlations, allowing for accurate characterization of different stochastic processes. When applied to the diffusion of single gold nanorods in hydrogels, the SPINN revealed enhanced microrheological properties during hydrogel gelation and uncovered interfacial dynamics during dextran/tetra-PEG liquid–liquid phase separation. By improving the temporal resolution of stochastic dynamics, the SPINN facilitates the estimation and prediction of complex diffusion behaviors, offering insights into underlying physical mechanisms at mesoscopic scales.

Concealment of Parkinsons disease prevalence and impact on health and quality of life

High-resolution structures of Myosin-IC reveal a unique actin-binding orientation, ADP release pathway, and power stroke trajectory

Myosin-IC (myo1c) is a class-I myosin that supports transport and remodeling of the plasma membrane and membrane-bound vesicles. Like other members of the myosin family, its biochemical kinetics are altered in response to changes in mechanical loads that resist the power stroke. However, myo1c is unique in that the primary force-sensitive kinetic transition is the isomerization that follows ATP binding, not ADP release as in other slow myosins. Myo1c also powers actin gliding along curved paths, propelling actin filaments in leftward circles. To understand the origins of this unique force-sensing and motile behavior, we solved actin-bound myo1c cryo-EM structures in the presence and absence of ADP. Our structures reveal that in contrast with other myosins, the myo1c lever arm swing is skewed, partly due to a different actin interface that reorients the motor domain on actin. The structures also reveal unique nucleotide-dependent behavior of both the nucleotide pocket as well as an element called the N-terminal extension (NTE). We incorporate these observations into a model that explains why force primarily regulates ATP binding in myo1c, rather than ADP release as in other myosins. Integrating our cryo-EM data with available crystallography structures allows the modeling of full-length myo1c during force generation, supplying insights into its role in membrane remodeling. These results highlight how relatively minor sequence differences in members of the myosin superfamily can significantly alter power stroke geometry and force-sensing properties, with important implications for biological function.