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Oral mucositis (OM) is one of the most frequent adverse events of high-dose conditioning chemotherapy with melphalan prior to autologous hematopoietic stem cell transplantation (AHSCT). It significantly reduces the patients’ quality of life. One of the preventive strategies for OM is cryotherapy. We retrospectively analyzed whether commercially available ice-cream could prevent OM during the melphalan infusion. We retrospectively analyzed 74 patients after AHSCT to see whether there is any correlation between OM and cryotherapy (ice-cream), melphalan dose (140 mg/m 2 or 200 mg/m 2 ). The incidence of OM in our study inversely correlated with cryotherapy in the form of ice-cream. Out of 74 patients receiving conditioning chemotherapy with high-dose melphalan, 52 received cryotherapy. Fifteen patients in the cryotherapy group (28.84%) developed OM, whereas 13 patients (59.09%) developed it in the group without cryotherapy. In a multiple linear regression test cryotherapy remained a significant protective factor against OM ( p  = 0.02) We have also seen the relationship between melphalan dose with OM ( p  < 0.005). Cryotherapy in the form of ice-cream is associated with a lower rate of OM and, therefore, could potentially be used as a cost-effective, less burdensome, and easy to implement method in prevention of oral mucositis.

In this paper, a study that describes the mathematical analysis of a mechanical gripping system with a lifting-tong-type mechanism is presented. The study involves the geometrical analysis of the investigated mechanism. What distinguishes this work from other studies in the specialized literature is the way the analysis of the mechanism under study is carried out. Specifically, the working methodology proposes the analysis of the entire mechanism and not its decomposition into structural groups, thus obtaining complex mathematical equations. By using values, the mathematically obtained results were able to describe the movement of the mechanism’s components, as well as the variations in their velocities. To verify the correctness of the results, a simulation was carried out using the Linkage simulation software.

In recent years, the introduction of chimeric antigen receptor (CAR) T-cell therapies into clinics has been a breakthrough in treating relapsed or refractory malignancies in hematology and oncology. To date, Food and Drug Administration (FDA) has approved six CAR-T therapies for specific non-Hodgkin lymphomas, B-cell acute lymphoblastic leukemia, and multiple myeloma. All registered treatments and most clinical trials are based on so-called 2nd generation CARs, which consist of an extracellular antigen-binding region, one costimulatory domain, and a CD3z signaling domain. Unfortunately, despite remarkable overall treatment outcomes, a relatively high percentage of patients do not benefit from CAR-T therapy (overall response rate varies between 50 and 100%, with following relapse rates as high as 66% due to limited durability of the response). Moreover, it is associated with adverse effects such as cytokine release syndrome and neurotoxicity. Advances in immunology and molecular engineering have facilitated the construction of the next generation of CAR-T cells equipped with various molecular mechanisms. These include additional costimulatory domains (3rd generation), safety switches, immune-checkpoint modulation, cytokine expression, or knockout of therapy-interfering molecules, to name just a few. Implementation of next-generation CAR T-cells may allow overcoming current limitations of CAR-T therapies, decreasing unwanted side effects, and targeting other hematological malignancies. Accordingly, some clinical trials are currently evaluating the safety and efficacy of novel CAR-T therapies. This review describes the CAR-T cell constructs concerning the clinical application, summarizes completed and ongoing clinical trials of next-generation CAR-T therapies, and presents future perspectives.

Laser cutting of steel is among the most advanced and competitive technologies in metal processing, widely employed in the automotive, aerospace, construction, and electronics industries, as well as in the production of precision components. Its advantages over traditional methods stem from its high precision, cutting speed, and ability to process complex geometries without requiring any subsequent finishing operations. This study presents research on the influence of fiber laser control parameters on the surface quality of S355J2C+N steel cuts. The primary objective is to identify which laser cutting parameters significantly affect the surface roughness of the cut. The response surface methodology (RSM) was employed for modeling. The Sq roughness parameter was selected as the primary metric to assess surface quality. The investigated process control parameters affecting surface roughness included feed rate, focal point position, and peak power. A 2-level split-plot experimental design was used for data analysis, with the focal point position designated as a hard-to-change factor due to the mechanical complexity of optical realignment. A mathematical model of the cutting process was generated. Based on analysis of variance (ANOVA), the model was found to fit the experimental data with an accuracy exceeding 98%. The results indicated that achieving low surface roughness (Sq) requires cutting with the lowest possible feed rate, lowest focal point position, and minimal peak power. The use of RSM facilitates the optimization of control parameter selection, contributing to improved surface quality and process efficiency.

The paper sheds light on the process of creating and validating the digital twin of bridges, emphasizing the crucial role of load testing, BIM models, and FEM models. At first, the paper presents a comprehensive definition of the digital twin concept, outlining its core principles and features. Then, the framework for implementing the digital twin concept in bridge facilities is discussed, highlighting its potential applications and benefits. One of the crucial components highlighted is the role of load testing in the validation and updating of the FEM model for further use in the digital twin framework. Load testing is emphasized as a key step in ensuring the accuracy and reliability of the digital twin, as it allows the validation and refinement of its models. To illustrate the practical application and issues during tuning and validating the FEM model, the paper provides an example of a real bridge. It shows how a BIM model is utilized to generate a computational FEM model. The results of the load tests carried out on the bridge are discussed, demonstrating the importance of the data obtained from these tests in calibrating the FEM model, which forms a critical part of the digital twin framework.

Two series of imidazole-2(3H)-thiones inspired by naturally occurring lepidiline alkaloids, bearing either one or two benzyl-type substituents located at the N(1)/N(3) atoms, respectively, were prepared and examined in reactions with in situ generated C-trifluoromethyl-N-aryl nitrile imines. N,N-Dibenzylated imidazole-2-thiones served exclusively as C=S dipolarophiles to afford hitherto unknown CF3-functionalized spiro [1,3,4-thiadiazole-5,2′-imidazole] derivatives formed through the (3+2)-cycloaddition pathway. In contrast, the enolizable N-monobenzylated imidazole-2-thiones provided acyclic products, i.e., hydrazonothioates, resulting from nucleophilic addition of the respective en(thio)late onto the C-termini of the 1,3-dipole. The presented results extend the scope of both fluorinated products available via trapping of the in situ generated CF3-nitrile imines as well as synthetic analogues of lepidilines. In addition, spectroscopic analysis of the obtained products and the known related systems revealed 13C NMR chemical shifts attributed to the C-(CF3) atom as useful probes to differentiate the open-chain hydrazonothioates (δ = 112–120), 2,2-diaryl/dialkyl-2,3-dihydro-1,3,4-thiadiazoles (δ = 130–145), and more strained spiro-1,3,4-thiadiazole derivatives (δ = 166–170) reported herein.

Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. A novel framework based on a combined model and data-driven approach is described, consisting of the establishment of the finite element (FE) model, its updating according to load testing results, and data augmentation to facilitate the training of selected physics-informed regression models. The article discusses the design of the Fiber Bragg Grating (FBG) sensor-based Bridge Weigh-in-Motion (BWIM) system, specifically focusing on several supervised regression models of different architectures. The current work proposes the use of the updated FE model to generate training data and evaluate the accuracy of regression models with the possible exclusion of selected input features enabled by the structural specificity of a bridge. The data were sourced from the SHM system installed on a network arch bridge in Wolin, Poland. It confirmed the possibility of establishing the BWIM system based on strain measurements, characterized by a reduced number of sensors and a satisfactory level of accuracy in the estimation of loads, achieved by exploiting the network arch bridge structural specificity.

This study investigates the Fe22Ni16Co19Mn12Cr16P15 alloy designed to enhance glass-forming ability. The alloy was synthesized by arc melting and examined using infrared thermography, differential scanning calorimetry (DSC), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Thermographic measurements revealed a temperature arrest at ~1007 K associated with eutectic crystallization, accompanied by contraction visible as a flattened ingot surface. DSC confirmed the dominant eutectic transformation (−170.7 J/g). Compared with the previously studied Fe22Ni16Co19Mn12Cr16P15 alloy, this composition showed a simplified transformation sequence and a larger eutectic fraction. DSC of melt-spun ribbons demonstrated a three-step crystallization (659 K, 699 K, 735–773 K, completion ~820 K) with a total enthalpy of 180.4 J/g. The broad crystallization interval (ΔTc ≈ 161 K) indicates enhanced thermal stability compared with simpler Ni–P or Fe–Ni–P–C alloys. SEM/EDS observations revealed eutectic colonies with predominantly rod-like morphology and chemical partitioning in inter-colony regions, favoring precipitation of transition metal phosphides. XRD confirmed four crystalline phases (Fe–Ni, CrCoP, Ni3P, MnNiP) in ingots, while ribbons exhibited a fully amorphous structure. These findings demonstrate that Fe22Ni16Co19Mn12Cr16P15 possesses good glass-forming ability but forms multiple phosphides under slower cooling. Precise cooling control is thus essential for tailoring its amorphous or crystalline state.

A solvent-free two-step synthesis of polyfunctionalized pyrazoles under ball-milling mechanochemical conditions was developed. The protocol comprises (3 + 2)-cycloaddition of in situ generated nitrile imines and chalcones, followed by oxidation of the initially formed 5-acylpyrazolines with activated MnO2. The second step proceeds via an exclusive deacylative pathway, to give a series of 1,4-diarylpyrazoles functionalized with a fluorinated (CF3) or non-fluorinated (Ph, COOEt, Ac) substituent at C(3) of the heterocyclic ring. In contrast, MnO2-mediated oxidation of a model isomeric 4-acylpyrazoline proceeded with low chemoselectivity, leading to fully substituted pyrazole as a major product formed via dehydrogenative aromatization. The presented approach extends the scope of the known methods carried out in organic solvents and enables the preparation of polyfunctionalized pyrazoles, which are of general interest in medicine and material sciences.

Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void formation and structural irregularities, which can lead to failures during the prestressing process. Ground Penetrating Radar (GPR) emerges as a pivotal non-destructive testing method for diagnosing such complex prestressed structures. Utilizing high-frequency electromagnetic waves, GPR accurately detects and maps anomalies within hardened concrete, enabling precise identification of defect locations and their dimensions. The detailed imaging provided by GPR facilitates the development of targeted repair strategies and allows for the exclusion of concrete voids through selective invasive inspections in designated boreholes. This study presents the use of GPR for the investigation of anomalies and damage in prestressing tendons of a newly built concrete bridge. It underscores the critical role of GPR in enhancing the diagnostic and maintenance programs for prestressed bridge structures, thereby improving their overall integrity and longevity.