Proteus mirabilis is a major opportunistic Gram-negative pathogen causing complicated urinary tract infections (UTIs), particularly in catheterized patients. Although several antibiotics, including streptomycin, erythromycin, and sparfloxacin, remain effective against some infections, the growing spread of multidrug-resistant P. mirabilis strains has created an urgent need to identify new therapeutic targets and treatment options. Using a subtractive proteomics approach, this study identified potential therapeutic targets in P. mirabilis. Among the 417 essential pathogen-specific proteins, 11 were identified as druggable, virulence-associated, and antibiotic resistance-related proteins, making them promising novel therapeutic targets against P. mirabilis. Among them, nine are enzymatic proteins involved in crucial biological pathways such as glycan and amino acid biosynthesis, energy metabolism, cellular signaling, and genetic information processing. The protein-protein interactions (PPIs) and hub gene analysis revealed arabinose-5-phosphate isomerase (API) as a primary target; structure-guided virtual screening identified five hits, ferric derisomaltose, hyaluronic acid, acarbose, rutin, and dihydrostreptomycin that exhibit binding affinities ranging from −13.540 to −10.616 kcal/mol with P. mirabilis arabinose-5-phosphate isomerase (PmAPI), suggesting their potential as anti-P. mirabilis agents towards infectious disease control. The density functional theory (DFT) studies and molecular dynamics (MD) simulations interaction analysis on PmAPI with potent hits clearly showed reactivity of the hits and stable binding with PmAPI during the 500-ns MD simulation.
Obesity, hyperglycemia, and aging are major risk factors for dementia-related disorders, particularly Alzheimer’s disease. We develop a time-fractional reaction–diffusion mathematical framework integrating nine interacting biological compartments governed by Michaelis–Menten kinetics and Hill functions to characterize the nonlinear interplay between metabolic dysfunction and neurodegeneration. Rigorous analysis establishes boundedness, stability, and the existence of a global attractor describing long-term system behavior. To enhance biological realism and predictive capability, we implement fractional physics-informed neural networks that embed fractional partial differential equations within deep learning architectures. This approach captures memory-dependent and spatiotemporal diffusion dynamics characteristic of neurological disorders while enabling estimation of time-varying parameters, fractional orders, and latent states. Global sensitivity analysis using partial-rank correlation coefficients identifies key drivers of tau pathology, insulin dysregulation, and dementia progression. Simulations reveal heterogeneous diffusion patterns linking obesity to accelerated neurodegenerative processes. An optimal control framework incorporating behavioral interventions and personalized anti-amyloid therapies is proposed to delay obesity-induced cognitive decline. The model highlights the critical roles of neuroinflammation and blood–brain barrier permeability in disease acceleration. The proposed FPI-NN framework achieves high predictive accuracy, with ℛ^2 values of 0.95, 0.93, 0.92, and 0.92 for Y_1 , W_β , W_τ , and V_d , respectively. The corresponding RMSE values are 0.078, 0.094, 0.098, and 0.099, while the MAE values are 0.065, 0.079, 0.082, and 0.084, respectively. The MAPE values are 6.50% , 7.90% , 8.20% , and 8.40% , respectively. Cross-validation further confirms the stability and robustness of the proposed framework. This work provides a reproducible and interpretable computational platform for modeling fractional-order neurodegenerative dynamics and optimizing therapeutic strategies in obesity-associated dementia.
Dynamic light scattering was used to analyze nanoscale and mesoscopic protein-associated objects formed in lysozyme-NaCl solutions under crystallization conditions. The study focuses on the sequence of particle-size populations detected after salt addition and on their relation to the initial state of the protein solution. NaCl reproducibly induced a small population with hydrodynamic diameters of 5.6-6.5 nm, which is consistent with nanoscale oligomeric lysozyme assemblies and with previous SAXS/SANS evidence for dimers and octamers in crystallization solutions. Mesoscopic particles in the tens-to-hundreds of nanometers range and large submicron-to-micron aggregates appeared at selected stages of the experiments. The samples that produced crystals showed different mesoscopic-particle dynamics, whereas a sample with similar early DLS behavior did not yield crystals. These observations suggest that, in this limited series, the evolution of protein-associated nano- and mesoscopic objects was related to the initial particle-size distribution and the full temporal trajectory rather than to any single DLS peak. The DLS data are interpreted qualitatively, since intensity-weighted distributions do not provide direct number or mass fractions of the detected populations. The results characterize a multiscale and nonstationary self-organization process in a concentrated protein solution, involving a small nanoscale population consistent with oligomeric precursor clusters, mesoscopic aggregated states, and large sedimenting objects. This work may be useful for understanding soft biological nanoobjects and mesoscopic protein assemblies formed during salt-induced association and crystallization-related restructuring of concentrated protein solutions.
Plants account for the vast majority of the biomass on land. They first appear in the fossil record many millions of years ago. Some researchers have proposed that the obvious success of plant-life indicates that plants are "intelligent." Plant intelligence has been strongly challenged, however, because plants do not possess brain-like tissues similar to electrical neurons, axons, and synapses. The following paper argues that a plant leaf is more properly understood as being a kind of analog computer called a "crossbar array." The leaf's computational facility arises from how leaf stomata respond to environmental water vapor, light, carbon dioxide, and heat. Though counterintuitive, a leaf's success is enhanced by the phenomenon of "stomatal oscillations."
In many plant cells, there are two types of mitochondrial motion: directed and wiggling. While the former is mediated by F-actin and microtubules, the latter is not. The fact that mitochondria migrate via wiggling suggests the existence of other mechanisms of motion aside from those related to the cytoskeleton and protein motors. In this work, it is assumed that wiggling mitochondria are active Brownian particles, self-propelled bodies whose motion at low Reynolds number is affected by noise. The proposed mechanism of motion is microswimming, where a wiggling mitochondrion is driven by a cycle of shape changes resembling a peristaltic wave travelling along its body. The peristaltic wave is modelled on a two-sphere swimmer under the far-field approximation, yielding expressions for the kinetic and dynamic variables involved, as well as for the factors determining its interaction with chloroplasts. The calculations show that the microswimmer can reach reported speeds with small size deformations and explain the observed high percentage of wiggling mitochondria captured by chloroplasts. Using the hydrodynamic results enables the application of a theoretical probabilistic model, including active and passive noise, which fits well with experimental results on speed distribution and trajectories of wiggling mitochondria. Taken together, the results explain the main mitochondrial wiggling characteristics observed in experiments, thus suggesting the feasibility of microswimming as a mechanism for mitochondrial wiggling.
