BACKGROUND: In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC).
METHODS: A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring.
RESULTS: On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC.
CONCLUSIONS: Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.

BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.

OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO2), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions.
METHODS: Web of Science and Scopus were searched up to June 13, 2024. All records were screened by two independent reviewers. Differences in the coefficient of determination (R2) and Root Mean Square Error (RMSE) between best statistical and machine learning methods were compared across categories of methodological elements.
RESULTS: A total of 38 studies with 46 model comparisons (30 for NO2, 8 for UFPs and 8 for BC) were included. Linear non-regularized methods and Random Forest were most frequently used. Machine learning outperformed statistical models in 34 comparisons. Mean differences (95% confidence intervals) in R2 and RMSE between best machine learning and statistical models were 0.12 (0.08, 0.17) and 20% (11%, 29%) respectively. Tree-based methods performed best in 12 of 17 multi-model comparisons. Nonlinear or regularization regression methods were used in only 12 comparisons and provided similar performance to machine learning methods.
CONCLUSIONS: This systematic review suggests that machine learning methods, especially tree-based methods, may be superior to linear non-regularized methods for predicting ambient concentrations of NO2, UFPs and BC. Additional comparison studies using nonlinear, regularized and a wider array of machine learning methods are needed to confirm their relative performance. Future air pollution studies would also benefit from more explicit and standardized reporting of methodologies and results.

The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina (SP) extract and understand its molecular mechanisms. The results showed that the administration of 1 g/kg of SP increased the antioxidant activity by raising levels of catalase (CAT) and superoxide dismutase (SOD), while decreasing the levels of malonaldehyde (MDA) and protein carbonyl. A histological examination revealed reduced tumor occurrence, decreased estrogen receptor expression, suppressed cell proliferation, and promoted apoptosis in SP protected animals. In addition, SP disrupted the G2/M phase of the MCF-7 cell cycle, inducing apoptosis and reactive oxygen species (ROS) accumulation. It also enhanced intrinsic apoptosis in MCF-7 cells by upregulating cytochrome c, Bax, caspase-8, caspase-9, and caspase-7 proteins, while downregulating Bcl-2 production. The main compounds identified in the LC-MS/MS study of SP were 7-hydroxycoumarin derivatives of cinnamic acid, hinokinin, valeric acid, and α-linolenic acid. These substances specifically targeted three important proteins: ERK1/2 MAPK, PI3K-protein kinase B (AKT), and the epidermal growth factor receptor (EGFR). Network analysis and molecular docking indicated a significant binding affinity between SP and these proteins. This was verified by Western blot analysis that revealed decreased protein levels of p-EGFR, p-ERK1/2, and p-AKT following SP administration. SP was finally reported to suppress MCF-7 cell growth and induce apoptosis by modulating the PI3K/AKT/EGFR and MAPK signaling pathways suggesting EGFR as a potential target of SP in breast cancer (BC) treatment.

The present study aimed to clarify the prognostic role of the pre-treatment neutrophil-to-lymphocyte ratio (NLR) for the response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC). Due to conflicting results in currently available data, the specific focus of the present study was on evaluating the associations between the pre-treatment NLR and the rate of achieving a pathological complete response (pCR) and survival outcomes. For the present study, data from a cohort of 465 consecutive patients with LABC who underwent NAC at King Feisal Specialist Hospital and Research Center (Riyadh, Saudi Arabia) between 2005 and 2014 were obtained from a prospective BC database and analyzed. Patients were stratified into two groups based on an optimal NLR cut-off determined using the receiver operating characteristic curve. Logistic regression analyses were conducted to assess variables associated with pCR, and Cox regression analyses were used to assess variables associated with survival outcomes. The low pre-treatment NLR group (≤2.2) was found to exhibit a higher likelihood of achieving a pCR (odds ratio, 2.59; 95% CI, 1.52-4.38; P<0.001), along with higher 5-year disease-free survival (DFS) [75.8 vs. 64.9%; hazard ratio (HR), 0.69; 95% CI, 0.50-0.94; P=0.02] and 5-year overall survival (OS; 90.3 vs. 81.9; HR, 0.62; 95% CI, 0.39-0.98; P=0.04) rates compared with those in the high NLR group (>2.2). Sub-group analysis revealed that the observed significance in survival outcomes was driven by the triple-negative BC (TNBC) subgroup. Patients with residual TNBC disease and a high pre-treatment NLR were observed to have lower 5-year DFS (44.4 vs. 75.0%; P=0.02) and 5-year OS (55.9 vs. 84.5%; P=0.055) rates compared with those with residual TNBC disease and a low NLR. To conclude, data from the present study suggest that the pre-treatment NLR can serve as a viable independent prognostic factor for pCR following NAC in patients with LABC and for survival outcomes, particularly for patients with TNBC.

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual\'s metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Breast cancer (BC) is the second most common cause of deaths reported in women worldwide, and therefore there is a need to identify BC patients at an early stage as timely diagnosis would help in effective management and appropriate monitoring of patients. This will allow for proper patient monitoring and effective care. However, the absence of a particular biomarker for BC early diagnosis and surveillance makes it difficult to accomplish these objectives. miRNAs have been identified as master regulators of the molecular pathways that are emphasized in various tumors and that lead to the advancement of malignancies. Small, non-coding RNA molecules known as miRNAs target particular mRNAs to control the expression of genes. miRNAs dysregulation has been linked to the start and development of a number of human malignancies, including BC, since there is compelling evidence that miRNAs can function as tumor suppressor genes or oncogenes. The current level of knowledge on the role of miRNAs in BC diagnosis, prognosis, and treatment is presented in this review. miRNAs can regulate the tumorigenesis of BC through targeting PI3K pathway and can be used as prognostic or diagnostic biomarkers for BC therapy. Some miRNAs, like miR-9, miR-10b, and miR-17-5p, are becoming known as biomarkers of BC for diagnosis, prognosis, and therapeutic outcome prediction. Other miRNAs, like miR-30c, miR-187, and miR-339-5p, play significant roles in the regulation of hallmark functions of BC, including invasion, metastasis, proliferation, resting death, apoptosis, and genomic instability. Other miRNAs, such as miR-155 and miR-210, are circulating in bodily fluids and are therefore of interest as novel, conveniently accessible, reasonably priced, non-invasive methods for the customized care of patients with BC.

Rapid passivation and aggregation of nanoscale zero-valent iron (nZVI) seriously limit its performance in the remediation of different contaminants from wastewater. To overcome such issues, in the present study, nano-palladium/iron (nPd/Fe) was simultaneously improved by biochar (BC) prepared from discarded peanut shells and green complexing agent sodium citrate (SC). For this purpose, a composite (SC-nPd/Fe@BC) was successfully synthesized to remove 2,4-dichlorophenol (2,4-DCP) from wastewater. In the SC-nPd/Fe@BC, BC acts as a carrier with dispersed nPd/Fe particles to effectively prevent its agglomeration, and increased the specific surface area of the composite, thereby improving the reactivity and stability of nPd/Fe. Characterization results demonstrated that the SC-nPd/Fe@BC composites were well dispersed, and the agglomeration was weakened. The formation of the passivation layer on the surface of the particles was inhibited, and the mechanism of SC and BC improving the reactivity of nPd/Fe was clarified. Different factors were found to influence the reductive dichlorination of 2,4-DCP, including Pd loading, Fe:C, SC addition, temperature, initial pH, and initial pollutant concentration. The dechlorination results revealed that the synergistic effect of the BC and SC made the removal efficiency and dechlorination rate of 2,4-DCP by SC-nPd/Fe@BC reached to 96.0 and 95.6%, respectively, which was better than that of nPd/Fe (removal: 46.2%, dechlorination: 45.3%). Kinetic studies explained that the dechlorination reaction of 2,4-DCP and the data were better represented by the pseudo-first-order kinetic model. The reaction rate constants followed the order of SC-nPd/Fe@BC (0.0264 min-1) > nPd/Fe@BC (0.0089 min-1) > SC-nPd/Fe (0.0081 min-1) > nPd/Fe (0.0043 min-1). Thus, SC-nPd/Fe@BC was capable of efficiently reducing 2,4-DCP and the dechlorination efficiency of BC and SC synergistically assisted composite on 2,4-DCP was much better than that of SC-nPd/Fe, nPd/Fe@BC and nPd/Fe. Findings suggested that SC-nPd/Fe@BC can be promising for efficient treatment of chlorinated pollutants.

Most current flexible electronic devices are based on petroleum materials that are difficult to degrade. The exploration of sustainable and eco-friendly materials has become a major focus in both the scientific and industrial communities. In this study, BC-Zn-BIM (bacterial cellulose-Zn-benzimidazole), a novel composite electrode material based on biodegradable BC was developed. Here, BC acted as a conductive medium involved in the conductive behavior of the composite material. We\'ve explored the charge transport mechanisms of BC-Zn-BIM by density functional theory (DFT) calculations, and applied it in the electrochemical detection of Bisphenol A (BPA). The results indicated that the oxygen-containing groups in BC and the nitrogen-containing heterocycles in BIM have a tendency to lose electrons, whereas zinc ions actively acquire electrons from these groups. This process promoted charge transfer within BC-Zn-BIM and endowed it with semiconductor-like properties, enhancing the electrocatalytic reaction of BPA. The detection limit of the electrochemical biosensor was 12 nM, and the sample recovery was 95.1%105.6%. This study clarified the mechanism of the higher electrical properties achieved in Zn-BIM complex grown in-situ on dielectric BC. This will further promote the development of low-cost, environmentally friendly flexible electronic devices.

BACKGROUND: Since 3D printing can be used to design implants according to the specific conditions of patients, it has become an emerging technology in tissue engineering and regenerative medicine. How to improve the mechanical, elastic and adhesion properties of 3D-printed photocrosslinked hydrogels is the focus of cartilage tissue repair and reconstruction research.
METHODS: We established a strategy for toughening hydrogels by mixing GelMA-DOPA (GD), which is prepared by coupling dopamine (DA) with GelMA, with HAMA, bacterial cellulose (BC) to produce composite hydrogels (HB-GD). HB-GD hydrogel scaffolds were characterized in vitro by scanning electron microscopy (SEM), Young\'s modulus, swelling property and rheological property tests. And biocompatibility and chondrogenic ability were tested by live/dead staining, DNA quantitative analysis and immunofluorescence staining. Combined with 3D bioprinting technology, mouse chondrocytes (ADTC5) were added to form a biological chain to construct an in vitro model, and the feasibility of the model for nasal cartilage regeneration was verified by cytology evaluation.
RESULTS: With the increase of GD concentration, the toughness of the composite hydrogel increased (47.0 ± 2.7 kPa (HB-5GD)-158 ± 3.2 kPa (HB-20GD)), and it had excellent swelling properties, rheological properties and printing properties. The HB-GD composite hydrogel promoted the proliferation and differentiation of ATDC5. Cells in 3D printed scaffolds had higher survival rates (> 95%) and better protein expression than the encapsulated cultures.
CONCLUSIONS: The HB-10GD hydrogel can be made into a porous scaffold with precise shape, good internal pore structure, high mechanical strength and good swelling rate through extrusion 3D printing.
METHODS: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.