We study the thermal Casimir effect in ideal Bose gases with spin-orbit (S-O) coupling of Rashba type below the critical temperature for Bose-Einstein condensation. In contrast to the standard situation involving no S-O coupling, the system exhibits long-ranged Casimir forces in both two and three dimensions (d = 2 and d = 3). We identify the relevant scaling variable involving the ratio D/nu of the separation between the confining walls D and the S-O coupling magnitude nu. We derive and discuss the corresponding scaling functions for the Casimir energy. In all the considered cases, the resulting Casimir force is attractive and the S-O coupling nu has impact on its magnitude. In d = 3 the exponent governing the decay of the Casimir force becomes modified by the presence of the S-O coupling, and its value depends on the orientation of the confining walls relative to the plane defined by the Rashba coupling. In d = 2 the obtained Casimir force displays a singular behavior in the limit of vanishing nu.

The classical two-compartment (2C) model is widely used to describe solute kinetics during hemodialysis, yet it often misses two key features of real data: the early concentration dip in the first dialysis hour and the long equilibration tail, especially in patients with diabetic kidney disease. Building on our previous 2C Monte Carlo work, we propose an extended three-compartment model with logistic exchange (3C-Logistic) that adds a slow tissue pool representing a slowly equilibrating tissue-associated kinetic compartment and allows the blood-tissue exchangeKcf(t)to vary over time according to a logistic law. The motivation is physiological but should be interpreted cautiously: the logistic time dependence is used as an apparent kinetic representation of delayed tissue- to-blood redistribution, for which progressive microvascular recruitment is one plausible, but not directly measured, mechanism. Using Monte Carlo parameter screening followed by local derivative-free refinement, we show that 3C-Logistic reduces systematic misfit in both the early and late parts of the session and yields a nine-parameter model-derived kinetic description, from which logistic-exchange and slow-pool descriptors(Kc1,Kc2,s,τ,Kfs,Vs)may help characterize diabetic-like and non-diabetic-like redistribution dynamics. We applied the framework to intradialytic kinetics of both urea and phosphate. For urea, the model reconciled the early trough and the long tail with physiologically plausible parameter ranges. For phosphate, fits generally indicated a stronger slow-pool contribution, consistent with slower equilibration from tissue stores. These parameters may serve as exploratory kinetic fingerprints of redistribution capacity, providing a mechanistically motivated, model-based rationale for future studies evaluating dialysis duration, ultrafiltration strategies, and post-dialysis assessments. By capturing the shared features of both solutes within one framework, 3C-Logistic may support the future development of session-by-session personalization strategies after external validation and could guide the design of non-invasive monitoring protocols.

We develop a theoretical framework based on the nonperturvative renormalization group (RG) in the one-particle irreducible (Wetterich) formulation to tackle the interplay of coupled fermionic and order-parameter fluctuations at metallic quantum critical points (QCPs) with ordering wavevector Q⃗=0⃗. We consistently treat the dynamical emergence of the Landau damping of the bosonic mode and non-Fermi liquid scaling of fermions upon lowering the cutoff scale. The loop integrals of the present theory involve only contributions from fluctuations above the cutoff scale, which protects the system from developing singular bosonic interactions. We emphasize the importance of the nontrivial relative scaling of the bosonic and fermionic cutoffs Λ and Λ_f, which we fix by analyzing the RG flow of the scale-dependent ordering wave-vector Q⃗_Λ. Upon neglecting Fermi self-energy in the loop integrals of the functional RG flow, we identify a non-Fermi liquid RG fixed point and recover the features obtained earlier within RPA-type approaches. In a subsequent step, we self-consistently include the scaling of the self-energy and the Yukawa coupling. We find a generic instability of the non-Fermi liquid RG fixed point. This implies, at least at this truncation level, absence of the QCP with Q⃗=0⃗ and development of a first-order phase transition or a phase characterized by Q⃗≠0⃗.

We employ the derivative expansion of the nonperturbative renormalization group to address the phenomenon of anisotropic scale invariance and the associated functional fixed points, also known as Lifshitz points, in systems characterized by a scalar order parameter. We demonstrate the existence of the Lifshitz fixed point featuring a non-classical value of the anisotropy exponent θ<1/2 and provide estimates for values of a set of critical exponents in the physically most relevant case of the three-dimensional uniaxial Lifshitz point (d,m)=(3,1), m denoting the anisotropy index. We compare our predictions with existing estimates from perturbative expansions around dimensionality d=4+1/2 as well as those from the 1/N expansion.

This study investigates the effects of platinum nanoparticles (Pt NPs) on breast cancer cell lines (MCF7 and MDA-MB-231) under proton beam irradiation, with a focus on nanoparticle accumulation behavior, cytotoxicity and radiosensitization. Platinum NPs used in this research exhibited a uniform, flower-like morphology with an average size of 26.2 ± 3.9 nm and showed no agglomeration. Cytotoxicity assays revealed that Pt NPs induced time- and concentration-dependent decrease in cell viability, with significant toxic effects observed at concentrations ≥ 0.375 mM. Moreover, holotomographic imaging demonstrated dynamic nanoparticle accumulation in both cell lines, initially localizing at the membrane and progressively internalizing over time. Importantly, no specific accumulation site preference was detected. Quantitative analysis showed that Pt NP uptake by MCF7 cells was approximately twice that of MDA-MB-231 cells, with simulation models aligning closely with experimental data (86 % and 90 % fit, respectively). Proton irradiation studies indicated a marked radiosensitizing effect of Pt NPs, with cell viability reduced by up to 85 % at higher NP concentrations and radiation doses (10 Gy). These findings underscore the potential of flower-like Pt NPs as effective radiosensitizers in proton therapy, owing to their high cellular uptake, surface reactivity and cytotoxicity.

This study presents PCA AutoExplorer, an open-source tool for automated identification of three-component Principal Component subspaces (hereafter referred to as "PCA triplets") that maximize class separation in clinical vibrational spectroscopy. The algorithm exhaustively evaluates all Principal Component triplets, combining Mahalanobis distance (unsupervised) and Linear Discriminant Analysis accuracy (supervised) to rank subspaces and compare preprocessing modes and spectral ranges. It introduces a marker strength plot - summing absolute loadings from Principal Components in top triplets - and integrated Principal Component loading heatmaps to globally prioritize diagnostic bands. Class separation is additionally visualized with 2D/3D t-distributed stochastic neighbor embedding. The method was validated on spectra acquired from dried blood serum samples of acute coronary syndrome patients and controls: Fourier Transform Infrared Spectroscopy (n=364) and Raman Spectroscopy (n=254), across 800-1800 and 2700-3500 cm-1, in intensity, first derivative, and second derivative modes. For 50 Principal Components, 19 600 triplets were assessed, with significance determined via permutation testing. Raman second derivative most often yielded the highest separation, while Fourier Transform Infrared Spectroscopy favored intensity or first derivative depending on spectral range. Top subspaces frequently involved higher-order Principal Components (e.g., PC1-PC4-PC26), achieving Mahalanobis distances up to ∼2.03 and Linear Discriminant Analysis accuracies of 96%-100% (p<0.001). Marker strength highlighted diagnostic bands such as 1522 cm-1 (Raman Spectroscopy, first derivative), 1649 cm-1 (Fourier Transform Infrared Spectroscopy, second derivative), 2951 cm-1 (Raman Spectroscopy, second derivative), and 3479 cm-1 (Fourier Transform Infrared Spectroscopy, second derivative). T-distributed stochastic neighbor embedding projections confirmed strong separation and revealed within-group heterogeneity. PCA AutoExplorer provides a reproducible, statistically rigorous framework for identifying diagnostically relevant Principal Component subspaces and prioritizing spectral biomarkers, enhancing the reliability of biomarker discovery in clinical vibrational spectroscopy and adaptable to other omics-related spectral analyses.

Pediatric Inflammatory Multisystem Syndrome (PIMS-TS), associated with SARS-CoV-2 infection, is a severe complication after COVID-19 in children. It is caused by the immune reaction to SARS-CoV-2, and usually appears three to six weeks after the infection. Unfortunately, PIMS causes non-specific symptoms, which makes its diagnosis and treatment difficult. In this paper, we propose Fourier Transform InfraRed spectrometry (FTIR) to identify chemical changes in blood serum of children induced by PIMS and caused by subsequent treatment of the syndrome. The results suggest that although the Principal Component Analysis (PCA) of FTIR data did not allow for differentiation of healthy children and children with PIMS before and after the treatment, the implementation of Support Vector Machine (SVM) showed that the accuracy of the FTIR region between 800 cm− 1 and 1800 cm− 1 in PIMS detection is as high as 92% with a sensitivity of 100%. The difference in the chemical compositions of sera from the control group and the children after the treatment was detected in 54%, indicating that the treatment was effective. Indeed, the obtained medical data clearly showed a decrease of C-reactive protein (CRP) and Procalcitonin (PCT) concentration in serum after the treatment. The decision tree showed that peak 1455 cm− 1 could be used as a potential FTIR PIMS marker. Importantly, FTIR data correlates well with medical parameters, however the correlation differs with respect to the groups before and after the treatment.

We revisit the structure of the phase diagram of the two-component mean-field Bose mixture at finite temperatures, considering both the cases of attractive and repulsive interspecies interactions. In particular, we analyze the evolution of the phase diagram upon driving the system towards collapse and point out its distinctive features in this limit. We provide analytical insights into the global structure of the phase diagram and the properties of the phase transitions between the normal phase and the phases involving Bose-Einstein condensates. Inter alia we analytically demonstrate that for sufficiently weak interspecies interactions a12 the system generically exhibits a line of quadruple points but has no triple nor tricritical points in the phase diagram spanned by the chemical potentials mu 1, mu 2 and temperature T. In contrast, for sufficiently large, positive values of a12, the system displays both triple and tricritical points but no quadruple points. As pointed out in recent studies, in addition to the phase transitions involving condensation, the mixture may be driven through a liquid-gas type transition, and we clarify the conditions for its occurrence. We finally discuss the impact of interaction- and mass-imbalance on the phase diagram of the mixture.

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, yet diagnosis still relies primarily on invasive bone-marrow procedures and advanced laboratory assays. Non-invasive, rapid, and cost-effective tools remain an unmet need. Fourier-transform infrared (FTIR) spectroscopy has shown promise for detecting cancer-associated biochemical changes in biofluids and cells. Methods: Serum from pediatric ALL patients and controls (n = 103; ALL = 45, controls = 58: healthy = 14, hematology controls = 44 with anemia, thrombocytopenia, leukopenia, and pancytopenia) was analyzed using FTIR. Spectra (800–1800, 2800–3500 cm−1) were preprocessed with baseline correction, derivative filtering, and normalization. Group differences were assessed statistically, and logistic regression with stratified 10-fold cross-validation was applied; Receiver operating characteristic (ROC)\precision–recall (PR) analyses were based on out-of-fold predictions. Results: Distinct spectral alterations were observed between ALL and controls. Leukemia samples showed higher amide I (~1640 cm−1) and amide II (~1545 cm−1) absorbance, lower lipid-related bands (~1450, ~2920 cm−1), and increased nucleic-acid–associated signals (~1080 cm−1). Differences were significant (q < 0.05) with moderate effect sizes. Logistic regression achieved area under the curve (AUC) ≈ 0.80 with sensitivity ~0.73–0.84 across practical decision thresholds (0.50 → 0.30) and higher recall attainable at the expense of specificity. Principal component analysis (PCA)\hierarchical cluster analysis (HCA) indicated partial but consistent group separation, aligning with supervised performance. Conclusions: Serum FTIR spectroscopy shows promise for distinguishing pediatric ALL from controls by reflecting disease-related metabolic changes. The technique is rapid, label-free, and requires only small serum volumes. Our findings represent proof-of-concept, and validation in larger, multi-center studies is needed before clinical implementation can be considered.

We address the possibility of realizing Bose-Einstein condensation as a first -order phase transition by admixture of particles of different species. To this aim we perform a comprehensive analysis of phase diagrams of two -component mixtures of bosons at finite temperatures. As a prototype model, we analyze a binary mixture of Bose particles interacting via an infinite -range (Kac-scaled) two -body potential. We obtain a rich phase diagram, where the transition between the normal and Bose -Einstein -condensed phases may be either continuous or first order. The phase diagram hosts lines of triple points, tricritical points, and quadruple points. We address the structure of the phase diagram depending on the relative magnitudes of the inter- and intraspecies interaction couplings. In addition, even for purely repulsive interactions, we identify a first -order liquid -gas -type transition between noncondensed phases characterized by different particle concentrations. In the obtained phase diagram, a surface of such first -order transitions terminates with a line of critical points.

Herein, it is demonstrated that the toxic effect of gold nanoparticles (Au NPs) on three different cancer cell lines (U-118 and LN-299 glioblastoma and HCT-116 colon) depends on their absorption dynamics by cells, related to the shapes of the NPs. This hypothesis is confirmed by showing that i) based on refractive index (RI) values, typical for cell components and gold nanoparticles, it is possible to show the absorption dynamics and accumulation locations of the latter ones inside and outside of the cells. Moreover, ii) the saturation of the accumulated Au NPs volume in the cells depends on the nanoparticle shape and is reached in the shortest time for star-shaped Au NPs (AuS NPs) and in the longest time for spherical Au NPs (AuSph NPs) and on the cancer cells, where the longest and the shortest saturation are noticed for HCT-116 and LN-229 cells, respectively. A physical model of Au NPs absorption dynamics is proposed, where the diameter and shape of the Au NPs are used as parameters. The obtained theoretical data are consistent with experimental data in 85-98%. Based on RI values, typical for cell components and gold nanoparticles (Au NPs), it is possible to show the absorption kinetics and accumulation locations of the latter ones inside and outside of the cells. The saturation of the accumulated Au NPs volume in the cells, depends on the nanoparticle shape. Based on the volumetric RI value of Au NPs and cellular structures a physical model of Au NPs absorption kinetics is proposed. image

Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation of glioblastoma G4 (GBM) and two types of meningiomas (atypical - MAtyp and angiomatous - MAng) were done using Fourier Transform InfraRed (FTIR) spectroscopy, combined with statistical, multivariate, machine learning and rate of spectrum changes methods. The positions of all analyzed peaks differed between GBM and meningiomas. However, for two types of meningiomas, only shift of peaks corresponding to CH2 bending vibrations, symmetric stretching vibrations of CH2, amide A, amide I, C=O lipids vibrations, asymmetric stretching vibrations of CH3 were observed. Principal Component Analysis showed clear differentiation between GBM and the meningiomas. Decision tree clearly showed that wavenumbers corresponding to C=O lipids vibrations provided the highest differentiation between GBM and meningiomas tissues, while amide I for two types of meningiomas. The accuracy and specificity of the results for GBM and meningiomas were more than 90%, while for MAtyp and MAng, these parameters were around 80%.

We reassess the structure of the effective action and quantum critical singularities of two-dimensional Fermi systems characterized by the ordering wave vector (Q) over right arrow = (0) over right arrow. By employing infrared cutoffs on all the massless degrees of freedom, we derive a generalized form of the Hertz action, which does not suffer from problems of singular effective interactions. We demonstrate that the Wilsonian momentum-shell renormalization group (RG) theory capturing the infrared scaling should be formulated keeping (Q) over right arrow as a flowing, scale-dependent quantity. At the quantum critical point, scaling controlled by the dynamical exponent z = 3 is overshadowed by a broad scaling regime characterized by a lower value of z approximate to 2. This, in particular, offers an explanation of the results of quantum Monte Carlo simulations pertinent to the electronic nematic quantum critical point.

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore, there is a need for new methods that will clearly indicate the marker qualifying the patient for surgery. In this article we used Fourier Transform InfraRed Attenuated Total Reflectance (FTIR-ATR) spectra of serum collected from healthy and patients suffering from ACS, which had surgery were analyzed by machine learning and Principal Component Analysis (PCA) to determine chemical differences and spectroscopy marker of ACS. PCA demonstrated clearly differentiation between serum collected from healthy and non-healthy patients. Obtained results showed that in serum collected from ACS patients, higher absorbances of PO2-stretching symmetric, CH2 and CH3 symmetric and asymmetric and amide I vibrations were noticed than in control group. Moreover, lack of peak at 1106 cm-1 was observed in spectrum of serum from non-control group. As a result of spectral shifts analysis was found that the most important role in distinguishing between healthy and unhealthy patients is played by FTIR ranges caused by vibrations of PO2- phospholipids, amides III, II and C--O lipid vibrations. Continuing, peaks at 1636 cm- 1 and 2963 cm- 1 were proposed as a potential spectroscopy markers of ACS. Finally, accuracy of obtained results higher than 90 % suggested, that FTIR-ATR can be used as an additional diagnostic tool in ACS qualifying for surgery.

We consider the spin-orbit-coupled Bose gas with repulsive mean-field interparticle interactions. We analyze the phase diagram of the system varying the temperature T > 0, the chemical potentials, and interparticle and spin-orbit interaction couplings. Our results indicate that, for Rashba- and Weyl-type spin-orbit couplings, condensates featuring an ordering wave vector (Q) over right arrow not equal (0) over right arrow are fragile with respect to thermal fluctuations and at T > 0 the only stable thermodynamic phases involving the Bose-Einstein condensate (BEC) are those of uniform type with (Q) over right arrow = (0) over right arrow. On the other hand, the presence of the spin-orbit coupling stabilizes the (Q) over right arrow = (0) over right arrow BEC state at any dimensionality d > 1 and modifies either the order or the universality class of the corresponding phase transition. We emphasize the singular nature of the limit of vanishing spin-orbit interaction coupling v, sizable shifts of the phase boundaries upon varying v, and the role of the relative magnitudes of the interparticle interaction couplings for the character of the condensation transition.

Background: Glioblastoma is among the most malignant brain cancer with an average survival rate measured in months. In neurosurgical practice, it is considered impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue. Methods: Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma.Results: The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm-1 and a shift of peaks at 1675 cm-1 and 1637 cm-1. Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of beta-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm-1 and 1056 cm-1 as well as between 1564 cm-1 and 1588 cm-1 are characteristic of glioblastoma.Conclusion: Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation.

The system in consideration is a singular simple cubic cell of spins $$s = \frac{1}{2}$$ doped in centre with additional spin $$S = 1$$ . The structure is located in external magnetic field and is undergoing dissipative, Markovian evolution. The analysis of system time evolution is focused on the entanglement evaluation and determination of its behaviour over time. We found out the distinct effect of doping the structure with additional spin $$S = 1$$ in the time evolution of the bipartite entanglement in $$\vert W \rangle $$ state. We also distinguished the raising and lowering spin interaction with environment and determined its impact on decoherence. Furthermore, we have shown that the entanglement between spins is in the form of a damped superposition of Gaussian functions. Its pulsed nature results from the unitary evolution in spin structure, while the damping is caused by the influence of the environment.

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and β-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Background and Objective: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca. Methods: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evalu-ate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and EL ISA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery ( n = 26) and healthy ( n = 44) were comrpised in this study. Results: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm -1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm -1, as well as between 2700 and 30 0 0 cm -1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm -1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm. Conclusions: The obtained results suggest, that Raman shifts at 1302 and 1306 cm -1 could be spectro-scopic markers of gastric cancer.(c) 2023 Elsevier B.V. All rights reserved.