Deletion of BTB and CNC homology 1 protects against Staphylococcus aureus-induced acute lung injury.

BTB and CNC homology 1 (BACH1) plays a crucial role in the pathogenesis of acute lung injury (ALI) caused by gram-negative bacteria. However, its exact mechanisms and roles in Staphylococcus aureus (SA)-induced ALI, a gram-positive bacterial infection, remain incompletely understood. In this study, we generated a BACH1-knockout mouse model (BACH1-/-) to investigate the role of BACH1 and its underlying mechanisms in regulating the development of sepsis-induced acute lung injury (ALI). Elevated levels of BACH1 were observed in both serum samples from septic patients and mouse models. Deletion of BACH1 alleviated ALI symptoms induced by sepsis. In bone marrow-derived macrophages, BACH1 deletion or knockdown suppressed NF-κB p65 phosphorylation and the induction of pro-inflammatory cytokines. Mechanistic studies demonstrated that BACH1 downregulated tumor necrosis factor-alpha-induced protein 3 (TNFAIP3) mRNA expression by binding to its promoter region. These findings uncover inhibiting BACH1 may be a promising therapeutic strategy for treating gram-positive bacteria-induced ALI.

BTB and CNC homology 1 deficiency disrupts intestinal IgA secretion through regulation of polymeric immunoglobulin receptor expression.

Immunoglobulin A (IgA)-mediated mucosal immunity is important for the host because it contributes to reducing infection risk and to establishing host-microbe symbiosis. BTB and CNC homology 1 (Bach1) is a transcriptional repressor with physiological and pathophysiological functions that are of particular interest for their relation to gastrointestinal diseases. However, Bach1 effects on IgA-mediated mucosal immunity remain unknown. For this study using Bach1-deficient (Bach1-/-) mice, we investigated the function of Bach1 in IgA-mediated mucosal immunity. Intestinal mucosa, feces, and plasma IgA were examined using immunosorbent assay. After cell suspensions were prepared from Peyer's patches and colonic lamina propria, they were examined using flow cytometry. The expression level of polymeric immunoglobulin receptor (pIgR), which plays an important role in the transepithelial transport of IgA, was evaluated using Western blotting, quantitative real-time PCR, and immunohistochemistry. Although no changes in the proportions of IgA-producing cells were observed, the amounts of IgA in the intestinal mucosa were increased in Bach1-/- mice. Furthermore, plasma IgA was increased in Bach1-/- mice, but fecal IgA was decreased, indicating that Bach1-/- mice have abnormal secretion of IgA into the intestinal lumen. In fact, Bach1 deficiency reduced pIgR expression in colonic mucosa at both the protein and mRNA levels. In the human intestinal epithelial cell line LS174T, suppression of Bach1 reduced pIgR mRNA stability. In contrast, the overexpression of Bach1 increased pIgR mRNA stability. These results demonstrate that Bach1 deficiency causes abnormal secretion of IgA into the intestinal lumen via suppression of pIgR expression.NEW & NOTEWORTHY The transcriptional repressor Bach1 has been implicated in diverse intestinal functions, but the effects of Bach1 on IgA-mediated mucosal immunity remain unclear. We demonstrate here that Bach1 deficiency causes abnormal secretion of IgA into the intestinal lumen, although the proportions of IgA-producing cells were not altered. Furthermore, Bach1 regulates the expression of pIgR, which plays an important role in the transepithelial transport of IgA, at the posttranscriptional level.

Thioredoxin facilitates hepatocellular carcinoma stemness and metastasis by increasing BACH1 stability to activate the AKT/mTOR pathway.

Thioredoxin (TXN) is essential for preserving balance and controlling the intracellular redox state. Most studies have focused on the function of TXN in redox reactions, which is critical for tumor progression. Here, we showed that TXN promotes hepatocellular carcinoma (HCC) stemness properties in a non-redox-dependent manner, which has rarely been reported in previous studies. TXN exhibited upregulated expression in human HCC specimens, which was associated with a poor prognosis. Functional studies showed that TXN promoted HCC stemness properties and facilitated HCC metastasis both in vitro and in vivo. Mechanistically, TXN promoted the stemness of HCC cells by interacting with BTB and CNC homology 1 (BACH1) and stabilized BACH1 expression by inhibiting its ubiquitination. BACH1 was positively correlated with TXN expression and was significantly upregulated in HCC. In addition, BACH1 promotes HCC stemness by activating the AKT/mammalian target of rapamycin (mTOR) pathway. Furthermore, we found that the specific inhibition of TXN in combination with lenvatinib in mice significantly improved the treatment of metastatic HCC. In summary, our data demonstrate that TXN plays a crucial role in HCC stemness and BACH1 plays an integral part in regulating this process by activating the AKT/mTOR pathway. Thus, TXN is a promising target for metastatic HCC therapy.

Precision-milled simulated curved root canals in bovine dentine for the assessment of chemo-mechanical root canal preparation.

AIM: The aim was to develop a standardized curved root canal model in bovine dentine and to assess whether that natural substrate would behave differently from the resin in standard plastic training blocks when prepared chemo-mechanically. The impact of substrate microhardness on simulated canal transportation was considered. METHODOLOGY: High-precision computer numerical control (CNC) milling was used to recreate a simulated root canal from a resin training block (Endo Training Bloc J-Shape, size 15) in longitudinally sectioned, dis- and re-assembled bovine incisor roots. Optical overlays obtained from 10 resin blocks were used to identify an average canal and program the CNC milling apparatus accordingly. Resin and dentine microhardness were measured. Simulated root canals in resin training blocks and their bovine counterparts were then instrumented at 37°C using Reciproc R25 instruments (VDW) with water or 17% EDTA (n = 10). Open-access image processing software was used to superimpose and analyse pre- and postoperative images obtained with a digital microscope. Centering ratios were averaged to indicate canal transportation. The effects of substrate and irrigant on canal transportation were assessed by two-way anova. RESULTS: Superimposed images showed that resin blocks under investigation varied considerably in terms of simulated canal length and curvature, whilst the milled canals were highly similar. The microhardness of dentine was more than three times higher than that of the resin. Conversely, canal transportation was considerably greater in dentine compared to resin, and in dentine had a tendency to be increased by EDTA. There was a strong effect of substrate on canal transportation (p < .001), no overall effect of irrigant, and a marginally significant interaction between irrigant and substrate (p = .077). CONCLUSIONS: CNC milling allows to create standardized simulated curved root canals in bovine dentine. These models may be useful to test and compare materials and concepts of chemo-mechanical root canal instrumentation. Microhardness is a bulk feature that does not predict the response to chemo-mechanical instrumentation of a composite material such as dentine.

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines.

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA).

An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining.

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Tert-butyl hydroperoxide induces ferroptosis of bone mesenchymal stem cells by repressing the prominin2/BACH1/ROS axis.

Ferroptosis has been proven critical for survival following bone marrow mesenchymal stem cells (BMSCs) explantation. Suppression of ferroptosis in BMSCs will be a valid tactic to elevate the therapeutic potential of engrafted BMSCs. Prominin2 is a pentaspanin protein involved in mediating iron efflux and thus modulates resistance to ferroptosis, but its role in tert-butyl hydroperoxide (TBHP)-induced BMSCs ferroptosis remains elusive. We examined the biological effect of prominin2 in vitro and in vivo by using cell proliferation assay, iron assay, reactive oxygen species (ROS) examination, malondialdehyde assay, glutathione (GSH) examination, Western blot, quantitative reverse transcription-PCR, immunofluorescence staining assay, gene expression inhibition and activation, co-immunoprecipitation (CO-IP) assay, radiographic analysis, and histopathological analysis. Our study demonstrated that prominin2 activity was impaired in TBHP-induced BMSCs ferroptosis. We found that PROM2 (encoding the protein prominin2) activation delayed the onset of ferroptosis and PROM2 knockdown deteriorated the course of ferroptosis. CO-IP, Western blot, and immunofluorescence demonstrated that prominin2 exerts antiferroptosis effects by inhibiting BTB and CNC homology 1 (BACH1) that promotes ROS generation, and thus exerts potent antioxidant effects in oxidative stress (OS)-induced BMSCs ferroptosis, including elevating BMSCs' survival rate and enhancing GSH contents. BMSCs with PROM2 overexpression also partially delayed the progression of intervertebral disk degeneration in vivo, as illustrated by less loss of disk height and lower histological scores. Our findings revealed a mechanism that the prominin2/BACH1/ROS axis participates in BMSCs ferroptosis and the strengthening of this axis is promising to maintain BMSCs' survival after explantation.NEW & NOTEWORTHY We found that prominin2 might be a potential biomarker and is expected to be utilized to augment engrafted bone marrow mesenchymal stem cells (BMSCs) survival rate. The prominin2/BTB and CNC homology 1 (BACH1)/reactive oxygen species (ROS) axis, which participates in the regulation of BMSCs ferroptosis induced by tert-butyl hydroperoxide (TBHP), is uncovered in our study. The therapeutic targeting of the prominin2/BACH1/ROS axis components is promising to elevate the survival of transplanted BMSCs in clinical practice.

Heat shock-induced heme oxygenase-1 expression in a mouse hepatoma cell line is dependent on HSF1 and modified by NRF2 and BACH1.

The induction mechanism of heme oxygenase-1 (HO-1) by heat shock (HS) is still unknown. Here, we discovered that HS activates the HO-1 expression in a mouse hepatoma cell line (Hepa 1-6). Knockdown experiments showed that the HS-induced HO-1 expression was dependent on HS factor 1 (HSF1). A chromatin immunoprecipitation (ChIP) assay demonstrated that the HS-activated HSF1 bound to the HS elements (HSEs) in the upstream enhancer 1 region (E1). Unexpectedly, HS also facilitates the BTB and CNC homology 1 (BACH1) binding to the Maf recognition elements (MAREs) in E1. We examined the effects of a catalytically inactive CRISPR-associated 9 nucleases (dCas9) with short guide RNAs (sgRNAs), and demonstrated that the HSF1 binding to HSEs in E1 was indispensable for the HS-induced HO-1 expression. Heme treatment (HA) dissociates BACH1 from MAREs and facilitated the binding of nuclear factor-erythroid-2-related factor 2 (NRF2) to MAREs. Following treatment with both HS and HA, the HO-1 induction and the HSF1 binding to HSEs in E1 were most notably observed. These results indicate that the HS-induced HO-1 expression is dependent on the HSF1 binding to HSEs in E1, although modulated by the BACH1 and NRF2 binding to MAREs within the same E1.

Harmonized Life-Cycle Inventories of Nanocellulose and Its Application in Composites.

Cellulose nanocrystals (CNC) and nanofibers (CNF) have been broadly studied as renewable nanomaterials for various applications, including additives in cement and plastics composites. Herein, life cycle inventories for 18 previously examined processes are harmonized, and the impacts of CNC and CNF production are compared with a particular focus on GHG emissions. Findings show wide variations in GHG emissions between process designs, from 1.8-1100 kg CO2-eq/kg nanocellulose. Mechanical and enzymatic processes are identified as the lowest GHG emission methods to produce CNCs and CNFs. For most processes, energy consumption and chemical use are the primary sources of emissions. However, on a mass basis, for all examined production methods and impact categories (except CO emissions), CNC and CNF production emissions are higher than Portland cement and, in most cases, are higher than polylactic acid. This work highlights the need to carefully consider process design to prevent potential high emissions from CNCs and CNF production despite their renewable feedstock, and results show the magnitude of conventional material that must be offset through improved performance for these materials to be environmentally favorable.

Transcription factors, cap 'n' collar isoform C regulates the expression of CYP450 genes involving in insecticides susceptibility in Locusta migratoria.

BACKGROUND: The cap 'n' collar (Cnc) belongs to the Basic Leucine Zipper (bZIP) transcription factor super family. Cap 'n' collar isoform C (CncC) is highly conserved in the animal kingdom. CncC contributes to the regulation of growth, development, and aging and takes part in the maintenance of homeostasis and the defense against endogenous and environmental stress. Insect CncC participates in the regulation of various kinds of stress-responsive genes and is involved in the development of insecticide resistance. RESULTS: In this study, one full-length CncC sequence of Locusta migratoria was identified and characterized. Upon RNAi silencing of LmCncC, insecticide bioassays showed that LmCncC played an essential role in deltamethrin and imidacloprid susceptibility. To fully investigate the downstream genes regulated by LmCncC and further identify the LmCncC-regulated genes involved in deltamethrin and imidacloprid susceptibility, a comparative transcriptome was constructed. Thirty-five up-regulated genes and 73 down-regulated genes were screened from dsLmCncC-knockdown individuals. We selected 22 LmCncC-regulated genes and verified their gene expression levels using RT-qPCR. Finally, six LmCYP450 genes belonging to the CYP6 family were selected as candidate detoxification genes, and LmCYP6FD1 and LmCYP6FE1 were further validated as detoxification genes of insecticides via RNAi, insecticide bioassays, and metabolite identification. CONCLUSIONS: Our data suggest that the locust CncC gene is associated with deltamethrin and imidacloprid susceptibility via the regulation of LmCYP6FD1 and LmCYP6FE1, respectively.

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds.

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Estimation of Tool Life in the Milling Process-Testing Regression Models.

The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools.

Compact Wideband Groove Gap Waveguide Bandpass Filters Manufactured with 3D Printing and CNC Milling Techniques.

This paper presents for the first time a compact wideband bandpass filter in groove gap waveguide (GGW) technology. The structure is obtained by including metallic pins along the central part of the GGW bottom plate according to an n-order Chebyshev stepped impedance synthesis method. The bandpass response is achieved by combining the high-pass characteristic of the GGW and the low-pass behavior of the metallic pins, which act as impedance inverters. This simple structure together with the rigorous design technique allows for a reduction in the manufacturing complexity for the realization of high-performance filters. These capabilities are verified by designing a fifth-order GGW Chebyshev bandpass filter with a bandwidth BW = 3.7 GHz and return loss RL = 20 dB in the frequency range of the WR-75 standard, and by implementing it using computer numerical control (CNC) machining and three-dimensional (3D) printing techniques. Three prototypes have been manufactured: one using a computer numerical control (CNC) milling machine and two others by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized from a silver vacuum thermal evaporation deposition technique, while for the other a spray coating system has been used. The three prototypes have shown a good agreement between the measured and simulated S-parameters, with insertion losses better than IL = 1.2 dB. Reduced size and high-performance frequency responses with respect to other GGW bandpass filters were obtained. These wideband GGW filter prototypes could have a great potential for future emerging satellite communications systems.

Fault Root Cause Tracking of the Mechanical Components of CNC Lathes Based on Information Transmission.

This study proposes a new method for the immediate fault warning and fault root tracing of CNC lathes. Here, the information acquisition scheme was formulated based on the analysis of the coupling relationship between the mechanical parts of CNC lathes. Once the collected status signals were de-noised and coarse-grained, transfer entropy theory was introduced to calculate the net entropy of information transfer between the mechanical parts, after which the information transfer model was constructed. The sliding window method was used to determine the probability threshold interval of the net information transfer entropy between the lathe mechanical parts under different processing modes. Therefore, the transition critical point was determined according to the information entropy, and the fault development process was clarified. By analyzing the information transfer changes between the parts, fault early warning and fault root tracking on the CNC lathe were realized. The proposed method realizes the digitalization and intelligentization of fault diagnosis and has the advantages of timely and efficient diagnosis. Finally, the effectiveness of the proposed method is verified by a numerical control lathe tool processing experiment.

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control.

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the "invariance" of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.

Biomedical engineering aspects of nanocellulose: a review.

Cellulose is one of the most abundant renewable biopolymer in nature and is present as major constituent in both plant cell walls as well as synthesized by some microorganisms as extracellular products. In both the systems, cellulose self-assembles into a hierarchical ordered architecture to form micro to nano-fibrillated structures, on basis of which it is classified into various forms. Nanocellulose (NCs) exist as rod-shaped highly crystalline cellulose nanocrystals to high aspect ratio cellulose nanofibers, micro-fibrillated cellulose and bacterial cellulose (BC), depending upon the origin, structural and morphological properties. Moreover, NCs have been processed into diversified products ranging from composite films, coatings, hydrogels, aerogels, xerogels, organogels, rheological modifiers, optically active birefringent colored films using traditional-to-advanced manufacturing techniques. With such versatility in structure-property, NCs have profound application in areas of healthcare, packaging, cosmetics, energy, food, electronics, bioremediation, and biomedicine with promising commercial potential. Herein this review, we highlight the recent advancements in synthesis, fabrication, processing of NCs, with strategic chemical modification routes to tailor its properties for targeted biomedical applications. We also study the basic mechanism and models for biosynthesis of cellulose in both plant and microbial systems and understand the structural insights of NC polymorphism. The kinetics study for both enzymatic/chemical modifications of NCs and microbial growth behavior of BC under various reactor configurations are studied. The challenges associated with the commercial aspects as well as industrial scale production of pristine and functionalized NCs to meet the growing demands of market are discussed and prospective strategies to mitigate them are described. Finally, post chemical modification evaluation of biological and inherent properties of NC are important to determine their efficacy for development of various products and technologies directed for biomedical applications.

Evaluation of the CNC® prosthetic system in recurrent breast cancer patients with chemotherapy-induced alopecia: a pilot study.

BACKGROUND: Chemotherapy-induced alopecia (CIA), although generally reversible, is felt as extremely distressing by patients with breast cancer. A certified medical device (Capelli Naturali a Contatto®-CNC®) was produced to provide patients with a personalized scalp prosthesis, reproducing the patient's original hair, resistant to any type of everyday or sporting activity, and hairdressing. AIMS: The present study aimed to evaluate the impact of the CNC® device on the patient's perception of their body image, psychological wellbeing, satisfaction, strengths and weakness of the CNC® device. METHOD: A pilot study was carried out on 21 patients affected by CIA due to recurrent breast cancer. A mixed quantitative/qualitative method was used, including administering a questionnaire and a focus group. RESULTS: Based on the Body Image Scale, body image perception improved after 3 and 6 months using the device in the 20 patients who answered the questionnaire. No significant change over time emerged for the six dimensions investigated by the Italian version of the Psychological Well-Being Scale. The thematic analysis of the focus groups showed six themes: definition of the prosthetic device, acceptance of the proposal, experience with the conventional wig, strengths, weaknesses, economic issues. CONCLUSION: Compared to the previous experience of CIA and the standard wig, the use of the CNC® device improved everyday life and may be proposed to women undergoing chemotherapy and expecting alopecia to prevent discomfort, social embarrassment, and compromised body image.