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Loss of ATM causes R-loop–associated transcriptional dysregulation and attenuates the related response to DNA damage
Pex6 and ubiquitination regulate topological remodeling of the peroxisomal membrane protein Pex14
CEP55 promotes prostate cancer progression via TPX2-dependent activation of AURKA–PI3K–AKT signaling and inhibition of ferroptosis
Correction: Abstract 2280 Time To Be Knowledgeable 1n TTBK1
The cytoplasmic protein YedX is a potent inhibitor of CsgA amyloid assembly in E. coli
A conserved salt bridge network stabilizes the hepatic organic anion transporters OATP1B1 and OATP1B3
A new functional assay reveals that membrane binding is critical for overactivation of the phosphoinositide 3-kinase H1047R mutant
The amyloidogenic C-terminal region of TMEM106B modulates lipid membrane biophysical properties: Functional and pathological insights
Topologically associating domains define the 3D genome architecture of mouse totipotent-like stem cells
Artificial intelligence versus traditional approaches in multicomponent spectral analysis
Abstract This study explores the use of AI-assisted data handlingin spectrophotometric method development, providing a flexible and globally accessible alternative to traditional manual software algorithms.Quadriderm cream combines four active ingredients: Clioquinol (CLIO), Betamethasone (BETA), Tolnaftate (TOL), and Gentamicin (GEN) with the preservative Chlorocresol (CC). Building on our previous research on complex pharmaceutical mixtures with challenging ratios, this study applied established protocols for CLIO and GEN while focusing on the more analytically demanding ternary subsystem (TOL, BETA, and CC).The integration of AI-enhanced spectral handling and interpretation reduces operator-dependent variability and streamlines the analytical workflow. This includes generating calibration graphs and regression equations, as well as effectively handling scanned spectral data via consecutive prompts. Validation data such as accuracy and precision are assessed to ensure reliability. Furthermore, the system enables intelligent, simultaneous analysis of laboratory mixtures and pharmaceutical formulations, enhancing both efficiency and accuracy. The AI strategy, trained on spectral data supplied and monitored by the expertiseanalyst, can automatically predict optimal wavelengths with minimal interference, while manual handling strategy rely on analyst-driven selection. Two novel approaches were developed: the factorized derivative ratio extraction using double divisor (MAN-[DD- DDE])via Spectra Manager ® software and the automated double divisor derivative ratio (AUTO-[DD-DD]) via AI tools and for resolving ternary mixtures with severely overlapping UV spectra and comparing the results with those of(MAN-[DD- DD])at coincidence points. Linear working ranges were 0.5–5.0 µg/mL (TOL), 3.0–30.0 µg/mL (BETA), and 2.0–20.0 µg/mL (CC); LODs were 0.09, 0.09, and 0.26 µg/mL, respectively. AI-driven data processing strategy matched the accuracy and reproducibility of traditional strategy manipulation while reducing subjective steps and effort. Finally, the UV-spectrophotometric method for pharmaceutical cream analysis was evaluated using the MA Tool (2025) to assess sustainability across green, white, and AI-driven criteria. AI-assisted scoring via Microsoft Copilot enabled rapid, reproducible assessment, yielding a Whiteness Score of 60.9% and providing actionable recommendations for greener and more efficient workflows.
Allosteric targeting with antiviral nucleotide analogs allows fine-tuning of SAMHD1 dNTPase activity
Dynamic analysis and structural parameters optimization of reciprocating double-action cutter for ramie based on FEM
Activated protein C drives β-arrestin-2- and c-Src-dependent phosphorylation of Cav1 and modulates Cav1 association with PAR1 and GRK5
A two stage optimization model for sustainable location routing problem with capacity and time window constraints in smart parcel lockers
Abstract The rapid expansion of e-commerce has significantly increased the demand for sustainable delivery methods to mitigate urban congestion, emissions, and rising logistical costs. Addressing the challenges of last-mile delivery requires logistics providers to respond to operational demands and market dynamics with both urgency and efficiency. Smart parcel lockers, as a known solution, reduce the negative externalities of urban transportation while improving delivery performance. This research introduces a sustainable location-routing model for smart parcel lockers using a two-stage optimization approach. The model aims to minimize operational costs, fuel consumption, and CO₂ emissions while ensuring customer demand is met. Exact solution techniques are applied, and multiple scenarios are evaluated through extensive sensitivity analysis. The model is validated using a real-world case study in Tehran, Iran. In addition, metaheuristic algorithms, such as the Keshtel, Genetic, and Simulated Annealing methods, were benchmarked to evaluate model performance under varying problem scales, with the Keshtel algorithm showing superior scalability and runtime efficiency in large instances. Findings indicate that optimally positioned lockers combined with efficient routing can lead to substantial reductions in both transportation costs and environmental impacts. Practical implications for logistics managers include the integration of electric vehicles and renewable-powered lockers to further advance sustainability goals.