Physical Sciences

Abstract: Pancreatic cancer remains difficult to diagnose at an early stage, motivating the development of label-free methods capable of detecting biochemical changes in tissue, cells, and biofluids. Raman spectroscopy provides molecularly specific measurements, but their diagnostic interpretation increasingly relies on chemometric, machine-learning, and deep-learning methods. This review critically examines Raman-based studies related to pancreatic cancer, with particular attention to acquisition modalities, preprocessing, feature extraction, classification, and validation design. Across the reviewed literature, high internal classification performance is frequently accompanied by small numbers of independent biological units, spectral pseudoreplication, unclear patient- or specimenlevel partitioning, data-dependent processing outside training folds, and limited external validation. Differences among instruments and preprocessing pipelines further restrict direct comparison across studies. We therefore distinguish evidence of spectral separability within a dataset from evidence of diagnostic generalization to independent patients. Priorities for the field include transparent reporting, patient-grouped evaluation, training-only preprocessing and model selection, harmonized acquisition and processing procedures, and shared benchmark datasets with sequestered test sets. Raman spectroscopy remains a promising source of biochemical information for pancreatic cancer research, but clinical translation requires reproducible performance across independent patients, laboratories, and acquisition conditions.

Abstract: Planck units represent the fundamental scale at which physics, biology, and consciousness converge. This article develops the model in which time-invariant superfluid quantum space is the primary physical reality, composed of Planck quanta. Duration and past-future are emergent, not fundamental. Gravity is derived as a pushing effect of the variable energy density of this space, not as spacetime curvature. We introduce consciousness as a fundamental, non-created physical field, denoted \( \Phi_{C^n} \), composed of complex Planck quanta and characterized by Planck frequency. The wave function is not consciousness itself, but its mathematical description. This distinction provides a physical mechanism for the measurement problem that the Copenhagen interpretation lacks. In this model, wave function collapse occurs because the excitation of superfluid quantum space receives a coherent energy kick from the consciousness field \( \Phi_{C^n} \). This energy transfer promotes a virtual bio-photon to a real bio-photon with defined spin, enabling binary information transmission in brain microtubules. Gravity plays no role in quantum superposition. The consciousness field \( \Phi_{C^n} \) and the coherent bio-photon field play a decisive role, mediating coupling between consciousness and biological matter.

Abstract: Arterial pressure waveforms are shaped by vascular biomechanics and wave reflection, processes that become increasingly relevant in hypertension and vascular aging. This study aimed to investigate how heart rate (pump frequency) and baseline diastolic pressure modulate pressure waveform morphology and wave reflection in a controlled biophysical cardiovascular model. A biophysical model mimicking the aorta and its major branches was equipped with custom pressure sensors, enabling simultaneous recordings at multiple locations along the system. Measurements were performed at diastolic pressures of 60, 80, 100, and 120 mmHg and pump frequencies ranging from 24 to 120 bpm. The downstream tube length and reflection site configuration were systematically varied to evaluate the effects of distal boundary conditions. Increasing pump frequency resulted in higher pressure amplitudes and a steeper systolic upstroke (increased dp/dt). Peak pressure amplitude increased from approximately 105 mmHg at 24 bpm to 145 mmHg at 120 bpm. At higher frequencies and longer reflection pathways, forward and reflected wave components became more clearly separated, whereas branched distal networks produced complex multi-reflection patterns. Wave reflection in this experimental system is strongly dependent on heart rate, diastolic pressure, and distal tube configuration, providing mechanical insight into central pressure augmentation in hypertensive and aging-related conditions.

Abstract: The rapid scaling of large language models (LLMs) and sparse mixture-of-experts (MoE) architectures reframes a foundational question about intelligence: must intelligent systems rely on large-scale, serial, and explicit computation, or does natural intelligence employ a faster, lower-level response mechanism than neuronal networks and explicit cognition? We introduce the Physical Fast Network Theory (PFNT) as a falsifiable, computational model. PFNT does not assume that consciousness is quantum, nor that a Planck-scale structure exists in the brain. Its central, testable claim is that biological intelligent systems may possess a hidden, fast, sparse, and dynamically routed information-processing network whose response time scale falls below what current macroscopic neuroimaging can directly resolve, with neurons and neural networks acting as an amplified, biological organization of that lower-level physical response. PFNT inherits ideas from brain-as-mixture-of-experts, dynamic neural routing, the freeenergy principle, neural Darwinism, and physical intelligence, but advances the question from how the brain computes to why the brain completes state selection on such a rapid physical time scale. We formulate a hierarchical model from fundamental physical interaction to hidden fast network, biological response, neurons, brain-MoE, intuition/emotion, and explicit consciousness, and propose to estimate the existence probability of such a network through early neural signals, latent-variable modeling, Bayesian model comparison, and accumulation of evidence. We explicitly state that there is currently no evidence of a Planck-scale computational network in the human brain, nor evidence that post-mortem brain tissue preserves usable quantum information. PFNT should therefore be regarded as a hypothesis and research program, not an established physical theory. The central question is not to search for quantum consciousness, but to determine whether a faster, dynamically sparse, behavior-predictive response network exists prior to conventional explicit neural computation. We emphasize the distinction between falsifiability and direct measurability. PFNT is falsifiable in the sense that specific predictions can be tested and potentially refuted, even though the hypothesized fast network is inherently not directly observable. The modelcomparison framework converts this into a well-posed question: whether a latent fast variable improves out-of-sample prediction over a classical baseline. The claim is therefore open to disconfirmation, and we give explicit falsification conditions in Section 7.3.

Abstract: Introduction: Low-field nuclear magnetic resonance (NMR)-based stimulation is an emerging non-invasive biophysical approach for tissue modulation, penetrating deeper than optical or mechanical techniques. However, the mechanisms linking physical exposure to biological response remain unclear.Areas covered: This review examines the physical principles of low-field NMR-based stimulation, focusing on cyclic adiabatic passage, longitudinal relaxation time (T1), and how field parameters and tissue relaxation properties may shape the distribution of the perturbation. Low-field NMR-based stimulation studies were identified in PubMed and Ovid/Embase from inception to 28 December 2025; other literature through targeted searches and the authors’ domain knowledge. Clinical studies suggest a favorable safety profile and improvements in pain, function and quality of life in several musculoskeletal indications, although between-group superiority was inconsistent. Experimental studies indicate modulation of inflammatory signaling, mitochondrial function, metabolism and redox-sensitive pathways. Four mechanistic questions are emphasized: how relaxation-weighted perturbations reach intracellular signaling; how a spin-level perturbation could trigger biochemical change; what determines specificity; and how weak perturbations are amplified.Expert opinion: Low-field NMR-based stimulation constitutes a physically definable exposure regime, but an NMR-specific biological transduction mechanism has not yet been established. Progress requires rigorously controlled, parameter-resolved experiments capable of supporting or rejecting resonance-specific interpretations.

Abstract: Protein-based biosensors exploit the extraordinary selectivity of proteins toward specific ligands—other proteins, ions, electromagnetic signals—often surpassing engineered recognition systems. Ligand binding induces structural rearrangements that alter a protein’s electrical and dielectric properties, and in electrical biosensors these changes, particularly in conductance, are what allow the target analyte to be quantified. Yet despite decades of effort, no unified strategy exists for defining and calculating protein permittivity (κ), a quantity central to interpreting such signals. Here we present a computationally inexpensive framework for evaluating protein permittivity, built on a network representation of the protein derived from its three-dimensional topology, in which each amino acid’s permittivity is assigned continuously between a dry, intrinsic value and the bulk solvent value according to its coordination number. Applied to 60 structurally diverse proteins, the model reveals a robust relationship between effective permittivity and a simple topological compactness index. As a proof of concept, we compute the electrical impedance of apo-azurin and of the protein NP_888769.1 under two electrode-contact configurations, finding a measurable, geometry-dependent response that vanishes under a uniform dielectric constant—an experimentally testable signature of internal dielectric structure that invites a re-examination of what protein permittivity experiments actually measure.

Abstract: The radical pair mechanism (RPM) struggles to explain the biological effects of weak magnetic fields due to rapid spin decoherence. To address this, we propose a mechanism based on the quantum behavior of a nanoscopic molecular rotator (approximately 1 nm in size) modeling an amino acid residue in a biosynthesizing enzyme. Using the Liouville–von Neumann equation with chemical kinetics and phenomenological decoherence, we show that MF effects arise from modulation of the rotator’s quantum interference pattern rather than from spin prohibition. Our results demonstrate that effects of several tens of percent occur in very weak magnetic fields (below 0.1 mT), are relatively robust to decoherence, and that while temperature promotes decoherence, it also enables the formation of a magnetic field-sensitive interference pattern. Under realistic parameters, the angular distribution of the rotator’s probability density rotates by several degrees in the geomagnetic field. The rotator mechanism implies that evolution has adapted organisms to use the geomagnetic field for nearly error-free biosynthesis; under hypomagnetic fields, biosynthetic errors increase. Unlike the RPM, this mechanism achieves high sensitivity by operating via magnetic field-induced phase changes rather than magnetic moment energy, thereby circumventing the standard quantum limit. Although tentative and lacking direct experimental evidence, the quantum rotator mechanism provides a biophysically transparent interpretation of hypomagnetic field effects, magnetic storms, and animal magnetic navigation, opening new avenues for research and medical applications.

Abstract: In the last decade significant progress has been made in understanding the crucial role of melanin in the energy production in cells and tissues (accounting for up to 90 % of the total in organisms) [1]. This requires rethinking the central role of glucose and ATP which instead are thought to play a primary role in building biomass [2]. The mechanism by which melanin absorbs a wide range of the electromagnetic (em) spectrum (from FIR to far UV and ionizing radiations helping organisms to cope with exposure to X- or γ rays [3]) and dissociates water molecules (releasing oxygen, hydrogen and free energy), has been extensively verified to occur in biological systems [4]. This mechanism has also been reproduced in laboratory and showed valuable applications in water revitalization and purification treatments [5]. In terms of bioenergetics, these findings highlight the importance for mammals (including humans) of sunlight exposure (which provides the full em spectrum) while maintaining adequate hydration to maintain a good homeostasis and optimal health. This process of hydrolysis and oxygen production through melanin and light had been proposed by Arturo Solìs Herrera and thus termed human photosynthesis [1]. However, the proposed dissociation of water molecules (with an initial electron transition at around 7 eV and ionization threshold at 12.62 eV) is not explainable within the still semi-classical vision of quantum mechanics (QM), especially when triggered by photons with much lower energy). In contrast, a description of water and biological matter in terms of Quantum Field Theory (QFT) that takes into account the key role of coherence and the interplay with hydrophilic surfaces provides a clear and physically sound picture [6]. In this paper we would propose a possible semi-quantitative theoretical framework for this fundamental biological process, which appears to underpin most metabolism in heterotrophic organisms, by interpreting water and living matter in terms of Quantum Electro-Dynamics(QED) [7]. This will allow us to make important progress in biochemistry and medicine too.

Abstract: Glutathione S-transferases (GSTs) plays pivotal roles in plant growth, abiotic stress responses, detoxification of xenobiotics, and maintenance of redox homeostasis. In this study, transcriptomic analysis was employed to identify differentially expressed genes (DEGs) in pea of DX27 under low temperature stress(-4°C) at 3, 6, and 12 h. GO and KEGG enrichment analyses of global transcriptomic data further revealed that DEGs were significantly enriched in oxidation-reduction processes, stress responses, glutathione metabolism, and secondary metabolite biosynthesis—functional categories. A total of 52 PsatGST genes were identified and classified into nine subfamilies based on phylogenetic relationships. Chromosomal localization revealed a non-random distribution across seven chromosomes. Promoter cis-element analysis indicated that PsatGST genes harbor diverse regulatory elements associated with light responsiveness, hormone signaling (auxin, abscisic acid, gibberellin, salicylic acid, and MeJA), and abiotic/biotic stress responses (low-temperature, wounding, and anaerobic induction). PsatGSTU18 and PsatGSTF2 expression analysis showing pronounced up-regulation at 12 h under low temperature stress (-4°C) at 3, 6, and 12 h. These findings provide a foundational framework for understanding the evolutionary history and functional diversification of the PsatGSTs gene family and offer valuable candidate genes for breeding stress-tolerant pea varieties.

Abstract: Caffeine (1,3,7-trimethylxanthine) is a widely consumed psychoactive drug and neurostimulant, yet its molecular organization and permeation behavior in lipid membranes not fully understood. In the present study, we employ microseconds-long, united-atom Molecular Dynamics simulations to investigate caffeine interactions with a solvated DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine) bilayer at its fluid phase. Caffeine molecules initially aggregate in the aqueous phase to form ordered stackings, which successively permeate into the membrane. The stacked assemblies gradually dissolve, reaching a stable dispersed state where caffeine molecules preferentially reside near the headgroup–acyl chain interface and orient parallel to the lipid acyl chains, consistent with previous experimental and simulation studies. Simulations initiated with caffeine in the membrane core converge to the same equilibrium state, indicating a preferred localization at the interface region. Density profiles and caffeine distributions suggest that direct translocation across the membrane is unlikely. Caffeine partitioning transiently reduces slightly bilayer thickness and increases the membrane surface area, while enhancing acyl chain stiffness near the hydrophilic part of the membrane. These observed trends are reproducible over different system sizes and independent simulations. Overall, this study provides atomic-level insights into the caffeine permeation process, including its effect on the lipid bilayer structure.

Abstract: Spatial self-organized patterns are ubiquitous features of vegetation ecosystems, and cyclic nontransitive competition serves as a crucial intrinsic mechanism for sustaining biodiversity. However, existing studies lack cross-scale comparative analyses of vegetation territorial competition based on multiple models. This study combines lattice models and continuous differential equation models to investigate the territorial occupation dynamics and spatial evolution of vegetation communities driven by cyclic competition. The results demonstrate that cyclic competition acts as a core mechanism maintaining vegetation biodiversity, which enables the self-organization of stable spiral wave patterns in space and supports the long-term dynamic coexistence of multiple species. Discrete and continuous models exhibit highly consistent macroscopic dynamical behaviors, which reveal the intrinsic dynamical characteristics of cyclic competitive systems. By integrating microscopic lattice simulation and macroscopic differential equation analysis, this study verifies that spiral waves represent a highly robust species coexistence mode and clarifies the coupled regulatory effects of species richness and stochasticity on system evolution. The findings further deepen the understanding of the complexity of vegetation ecosystems and provide important theoretical references for subsequent theoretical derivation and field observational research on vegetation community competition and evolution.

Abstract: Complex diseases challenge one of the oldest assumptions in medicine: that illness can be reduced to a single cause. Instead, increasing evidence suggests that many pathologies emerge from the collective dynamics of components interacting across molecular, cellular, physiological, behavioral, and ecological scales. Thus, we revisit the fundamental question of what a disease is through the lens of complex systems theory. In particular, we argue that diseases are better understood as emergent dynamical states of living systems that arise from the breakdown, reorganization, or destabilization of regulatory networks. Within this framework, mathematical models can describe health and disease as alternative attractors in a multidimensional state space, and disease onset often reflects critical transitions driven by stress, perturbation, or loss of resilience. Therefore, concepts from nonlinear dynamics, network theory, ecology, and statistical physics (such as bifurcations, hysteresis, phase transitions, and multistability) provide a unifying language to describe phenomena as diverse as patient comorbidity, psychiatric disorders, cancer progression, epidemic spreading, or neurodegeneration. We also discuss how multiscale models can bridge molecular mechanisms with organism-level behavior to reveal universal principles of complex diseases. This perspective implies that the future of medicine may depend on understanding not only the components of biological systems, but also the laws governing their collective organization, which could open new avenues for prediction, prevention, and control.

Abstract: Traumatic brain injury (TBI) triggers complex molecular responses that remain incompletely understood at the structural level, where it's highly connected to neurodegeneration and synaptic dysfunction. Growing evidence implicates intrinsically disordered proteins (IDPs), and proteins with intrinsically disordered regions (IDRs), as key regulators of stress-responsive signaling. In this study, we represent an in silico study of the 24 TBI-relevant proteins, such as MAPT, SERF2, SERBP1, BEX3, TDP43, NFL, C9orf16, C9orf58, APP, NRN1, NEFM, SYNGAP1, SNAP25, DLG4, APOC2, HCLS1, HMGB1, FUS, EPHA4, SEMA4D, S100B, PLEK, CAMK2A, and SNCA, integrating analysis of the predisposition for intrinsic disorder and liquid–liquid phase separation (LLPS) through RIDAO, FuzDrop, and AlphaFold platforms. We demonstrate that more than 90% of residues in SERF2, BEX3, MAPT, SERBP1, HMGB1, SNCA, and FUS are predicted as disordered. The amino acid sequences of NEFM, HCLS1, C9orf16, NEFL, C9orf58, SNAP25, SYNGAP1, S100B, and TDP43 contain between 50% and 90% of disordered residues, and the disorder contents of APP, APOC2, and DLG4 are 47.53%, 45.54% and 33.29%. Only five proteins (SEMA4D, PLEK, CAMK2A, EPHA4, and NRN1) have less than 30% disordered residues. The high prevalence of disorder in TBI-associated proteins correlates with their strong propensity for spontaneous liquid-liquid phase separation (LLPS). In fact, 15 proteins (FUS, SYNGAP1, MAPT, NEFM, SERBP1, HCLS1, SERF2, BEX3, C9orf16, TDP43, HMGB1, APP, NEF, DLG4, and SNCA) are expected to act as droplet drivers, and five more proteins (SEMA4D, C9orf58, SNAP25, CAMK2A, and EPHA4) can serve as droplet clients. The gene ontology analysis emphasized that the 24 TBI-related proteins (TBIome) are functionally associated with synaptic regulation, RNA metabolism, cytoskeletal dynamics, and mitochondrial stress response. We also show that human proteins interacting the members of TBIome are on average a bit more disordered than the human brain proteins in general. This extended TBI interactome is functionally connected to cytoplasmic translation, translation, synaptic vesicle cycle, modulation of chemical synaptic transmission, regulation of neurotransmitter levels, regulation of synaptic plasticity, neurotransmitter secretion, synaptic vesicle exocytosis, protein localization to synapse, and trans-synaptic signaling. Many of these proteins have strong LLPS potential as well. Therefore, high intrinsic disorder propensity and strong LLPS potential represent shared structural and functional features of proteins linked to the TBI-related pathophysiology. Collectively, these findings support a model in which disorder-based mechanisms contribute to post-traumatic molecular reprogramming, which underlie the pathological roles of TBI-related proteins.

Abstract: Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition, and how both polymer chemistry and salt identity, more than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric and immunoassays), and contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels, and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA approved isoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates high-grade prostate cancer from benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA^® improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive characteristics within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA.

Abstract: We review a theoretical and experimental programme aimed at understanding two intimately related fundamental phenomena in biophysics: (i) the classical analogue of Fröhlich phonon condensation in macromolecules driven out of thermal equilibrium, and (ii) the consequent activation of long-range resonant electrodynamic intermolecular forces. Both phenomena are underpinned by explicit Hamiltonian models. The first is derived by applying the time-dependent variational principle (TDVP) to the quantum Wu--Austin model, producing a fully classical Hamiltonian in action-angle variables whose nonlinear rate equations exhibit a nonequilibrium phase transition, the channelling of supplied energy into the lowest-frequency collective mode. The second is grounded in a classical electrodynamic Hamiltonian for two coupled oscillating dipoles whose normal-mode structure predicts long-range (\( \sim 1/r^3 \)) resonant interactions, absent at thermal equilibrium but activated by out-of-equilibrium collective oscillations. We also discuss a complementary Hamiltonian approach that connects Fröhlich's rate equations directly to Hamilton's equations of motion, clarifying the role of bath-mediated nonlinear coupling and the conditions for strong condensation at room temperature. In addition, the TDVP is applied to a Davydov--Holstein--Fröhlich Hamiltonian describing electron--phonon motion along the backbone of a specific DNA sequence and its cognate restriction enzyme EcoRI: the time-domain Fourier cross-spectrum of the resulting electron currents exhibits a sharp co-resonance peak for the canonical recognition sequence that disappears upon randomisation, providing a sequence-specific electrodynamic signature of DNA--protein recognition. Experimental evidence from THz near-field spectroscopy, fluorescence correlation spectroscopy, and direct observation of protein clustering is reviewed in relation to these theoretical predictions. The results establish a coherent physical picture suggesting that metabolic energy supply can play a role in driving macromolecules into coherently oscillating states that activate selective, distance-reaching electrodynamic forces capable of contributing to the organisation of biochemical reactions in living matter.

Abstract: Given that red blood cells (RBCs) are the most abundant cells in blood, their morphology and mechanics strongly affect blood rheology. Furthermore, changes in the physiological functions and health status of an organism can also affect RBC mechanics. Therefore, understanding the mechanical properties of RBCs holds substantial research value in the biomedical field. Optical tweezers (OT) technology has become a crucial method for measuring and analyzing the mechanical properties of RBCs, owing to their unique advantages such as non-contact manipulation and piconewton-level force sensitivity. This review first outlines the basic mechanical properties of RBCs, the mechanical sensing principles of optical tweezers, and their basic manipulation modes. It then focuses on the measurement and application of key mechanical parameters, such as the deformation index and shear modulus. Furthermore, the review also covers the integration of optical tweezers with Raman spectroscopy, fluorescence, and microfluidics. These combined approaches allow for the simultaneous acquisition of mechanical and molecular data, dynamic monitoring of mechanical state changes, and analysis of external stimuli and physiological mechanisms, thereby supporting disease diagnosis, drug efficacy evaluation, as well as artificial blood quality assessment.

Abstract: The pervasive allometric scaling laws in biology, most notably Kleiber’s law (B ∝ M^3/4), conflict with the predictions of classical Euclidean dimensional analysis (B ∝ M^2/3). While the West-Brown-Enquist (WBE) model resolved this paradox using hierarchical fractal networks, and Barenblatt’s incomplete similarity formalized the fractional exponents, a rigorous symmetry framework connecting the two has been lacking. In this paper, we reconstruct dimensional analysis from the perspective of Lie group theory, demonstrating that incomplete similarity corresponds to a deformed scaling Lie group parameterized by anomalous dimensions. We show that the internal fractal network breaks the isotropic Euclidean scaling symmetry. Crucially, we formulate natural selection and physical optimization as a constrained optimization problem on the Lie group parameters. Maximizing the throughput exponent subject to the physical bounds of fractal dimensions uniquely selects the anomalous parameters, rigorously yielding the 3/4-power law. Substituting these optima back into the Lie group action reveals an algebraic dimensional promotion: the broken symmetry is restored, but the effective group is isomorphic to a 4D Euclidean scaling group. This provides a rigorous algebraic foundation for the “fourth dimension of life,” establishing allometric scaling as the universal geometric invariant of optimized resource-distribution networks.

Abstract: Gastrointestinal (GI) cancers account for a quarter of all cancer cases worldwide and are responsible for a third of cancer deaths. One of the characteristic features of GI tissue is its multilayered structure, which in addition to multiple scattering, complicates optical-spectral analysis. The risk of lymph node metastasis in GI cancer is primarily related to the depth of tumor invasion. The use of spectroscopic diagnostics and photodynamic therapy for the detection and treatment of GI cancer is a rapidly developing field. The method proposed in this paper for layer-by-layer optical properties assessment, suitable for real-time clinical application to the walls of hollow organs, allows for both determining the depth of tumor invasion into the GI organ wall and calculating the absorbed dose layer-by-layer. This paper proposes a method for recording spectral data in two geometries, diffuse reflectance and transmission, using light delivery from both the external and internal surfaces of the gastrointestinal tract wall. Layer-by-layer assessment of optical properties was performed using a developed algorithm based on the inverse adding-doubling method with initial optical properties values ​​determined using the modified two-stream Kubelka-Munk model with the accuracy equal to 86±13%. The method was approbated in clinical conditions.  Based on the results of the work, the developed method for assessing the optical properties of multilayered biological tissues exhibited sufficient speed and accuracy for in vivo application to personalize laser-induced therapy by correction of the laser dose.

Abstract: We study the effects of weak magnetic fields (around 2 mT) on the growth of Staphylococcus aureus (S. aureus) in the presence of a few sweeteners (monosaccharides, disaccharides, sugar alcohols, and consumer-grade sweeteners). Bacterial growth rates were compared in various magnetic fields at room temperature. Bacterial growth was estimated using optical absorbance measurements at various wavelengths, and pH values were manually estimated using pH strips. Absorbance was measured at 492 nm and 630 nm, which are wavelengths comparable to the size of a cell of S. aureus after division. This comparability plays a vital role in the scale of measured absorbance values. The results imply that bacterial growth may be reduced due to acidic byproducts formed by metabolizing sugars or sugar alcohols, as an increasingly acidic solution is less ideal for bacterial growth. Magnetic fields were also found to have a minor effect on pH estimates. These results reveal potential effects on microorganisms in the presence of sugars and sugar alcohols in addition to weak magnetic fields, demonstrating the contribution of various environmental conditions with increasing prevalence in the modern day.

Abstract: Microtubules contain ordered aromatic amino-acid networks whose optical excitations have been proposed to support non-trivial energy-transfer dynamics. Here, we examined whether bound tryptamine ligands can perturb the excitonic structure of the tubulin tryptophan network. A virtual screen of 294 tryptamines was performed across seven known binding regions of the tubulin heterodimer using AutoDock Vina. From this screen, top-ranked tryptamine ligands were carried forward for excited-state analysis. Geometry optimization and time-dependent density functional theory (TD-DFT) calculations were used to obtain vertical excitation energies and transition dipole moments for the ligand-bound states in the ultraviolet range. These ligand properties were then incorporated into a tight-binding Hamiltonian describing the tubulin tryptophan excitation network in order to evaluate changes in exciton energies and eigenvector delocalization. The calculations indicate that tryptamine binding can modify the excitonic landscape of tubulin in a ligand-dependent manner, with the magnitude of the perturbation governed by excitation wavelength, transition dipole strength, and spatial orientation relative to the intrinsic tryptophan network. These results support the possibility that aromatic ligands may provide a chemically tunable route to altering the optical response of tubulin and motivate future experimental tests of ligand-dependent modulation of microtubule photophysics.