Biophysica

Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data have made traditional analysis methods difficult to scale. Artificial intelligence (AI) has emerged as a practical tool to address these challenges by providing new methods for interpreting and predicting biological behavior. Recent studies have demonstrated potential in several areas, including image-based analysis of cell and nuclear morphology, traction force microscopy (TFM), cell segmentation, motility analysis, and the detection of cancer biomarkers. Within this context, we review AI applications that either incorporate mechanical inputs/outputs directly or infer mechanobiologically relevant information from cellular and nuclear structure. This study summarizes progress in four key domains: AI/ML-based cell morphology studies, cancer biomarker identification, cell segmentation, and prediction of traction forces and motility. We also discuss the advantages and limitations of integrating AI/ML into mechanobiological research. Finally, we highlight future directions, including physics-informed and hybrid AI approaches, multimodal data integration, generative strategies, and opportunities for computational biophysics-aligned applications.

Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.

A rapid, colorimetric sensor for histamine detection is presented using citrate-stabilized gold nanoparticles enhanced with Cu²⁺ coordination. The sensing mechanism involves dual recognition: protonated histamine first adsorbs electrostatically onto AuNP surfaces at pH 5.5, followed by Cu²⁺-mediated coordination between imidazole rings that induces interparticle coupling, resulting in a characteristic shift of the localized surface plasmon resonance from 520 to 620 nm. The optical response, measured as the absorbance ratio A₆₂₀/A₅₂₀, exhibits excellent linearity over the range of 1.25–10 μM with a detection limit of 0.95 μM and total assay time under 30 min. The dual-recognition mechanism provides high selectivity for histamine over structural analogs, including L-histidine, imidazole, and L-lysine. The metal ion-mediated colorimetric approach described here achieves sub-micromolar sensitivity in simple buffer solutions, which is comparable to the histamine level used in in vitro cell assays and food-related studies. Thus, the present system is best viewed as a mechanistic model that can inform the design of future biosensing and analytical methods, rather than as a fully optimized sensor for direct clinical measurements in complex biofluids.