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Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites. These identified odorants primarily fell into the categories of ketones and ethers, and originated mainly from biological production. To obtain the odor threshold of these odorants, we trained an ML model for odor threshold prediction, which displayed good performance with accuracy of 79%. Further, an odor threshold-based prioritization approach was developed to rank the identified odorants. 2-Methylisoborneol and nonanal were identified as the main odorants contributing to water odor issues at the four sampling sites. This study provides an accessible method for accurate and quick determination of key odorants in source water, aiding in odor control and improved water quality management. Water odor episodes have been persistent and significant issues worldwide, posing severe challenges to water treatment plants. Unpleasant odors in aquatic environments are predominantly caused by the occurrence of a wide range of volatile organic chemicals (VOCs). Given the vast number of newly-detected VOCs, experimental identification of the key odorants becomes difficult, making water odor issues complex to control. Herein, we propose a novel approach integrating nontarget analysis with machine learning models to accurate and quick determine the key odorants in waterbodies. We use the approach to analyze four samples with odor issues in Changsha, and prioritized the potential odorants. [Display omitted] • Nontarget analysis and machine learning were integrated for odor risk assessment. • GC×GC-TOFMS was employed to identify the odorous compounds in water. • Machine learning models were developed to accurately predict odor thresholds. • 2-MIB and nonanal were prioritized as the potential key odorants. [ABSTRACT FROM AUTHOR]

Reactions of reactive halogen species (Cl•, Br•, and Cl 2 •–) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values. We tested five algorithms, namely, random forest, neural network, XGBoost, support vector machine (SVM), and multilinear regression, using molecular descriptors (MDs) and molecular fingerprints (MFs) as inputs. The optimal algorithms were MD-XGBoost for Cl• and Br•, and MF-SVM for Cl 2 •–, respectively, with R2 test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl 2 •– more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl 2 •–, whereas MDs are more suitable for Cl• and Br•. Furthermore, we interpreted the models using SHAP analysis, and the results indicated that our models accurately predicted k values both statistically and mechanistically. Our models provide useful tools for obtaining unknown k values and help researchers understand the inherent relationships between the models. [Display omitted] • Reactivity of Cl•/Br•/Cl 2 •– with TrOCs is predicted using machine learning models. • MFs are better inputs for Cl 2 •–, whereas MDs are more suitable for Cl• and Br•. • The importance of the MFs/MDs are interpreted and sorted using SHAP analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • OH and CH 3 C(O)OO were identified as the main reactive species in UV/PAA process. • CH 3 C(O)OO exhibited lower reaction rates but higher concentration compared to OH. • RAF was proposed as the main initial reaction mechanism in UV/PAA process. • CH 3 C(O)OO possessed higher selectivity than OH towards TrOCs. Peracetic acid (PAA) is a promising peroxyacid to combine with ultraviolet (UV) to eliminate trace amounts of organic contaminants (TrOCs), with growing importance in water treatment. In spite of a large number of research on UV/PAA process, the underlying mechanism still require further investigation, due to the difficulties in analyzing the carbon-centered radicals. Here we demonstrated the crucial roles of the carbon-centered radicals (namely CH 3 C(O)OO), via electron paramagnetic resonance (EPR) and kinetic models, with concentration of 4 orders magnitude higher than that of OH. Then, the theoretical calculation using density functional theory (DFT) showed that CH 3 C(O)OO and OH tended to attack phenol mostly through the route of radical adduct formation. The generated CH 3 C(O)OO and OH adducts may undergo further reactions to produce catechol and hydroquinone. Further, the environmental applicability of UV/PAA process for TrOC abatement was evaluated, including the effects of pH, PAA dosage, water constituents, as well as the variation of acute toxicity. Different from the OH-dominated process, kinetic analysis revealed that CH 3 C(O)OO was almost immune to the presence of water constituents, leading to the outperformance of UV/PAA process in actual water samples. Our present study could inform the application of UV/PAA process in water and wastewater treatment. [ABSTRACT FROM AUTHOR]

FSTL1 is a protein coding gene associated with cell signaling pathway regulation and the progression of a variety of disorders. In this study, we hypothesized that FSTL1 increases oncogenesis in breast cancer by enhancing stemness and chemoresistance. RT-PCR and IHC revealed significantly higher FSTL1 mRNA and protein levels in TNBC than in non-TNBC specimens and in breast cancer cell lines. We then found that FSTL1 levels were significantly increased in chemoresistant cells. LIVE/DEAD, MTT cell viability and colony formation assays did in fact demonstrate that FSTL1 is required for CDDP and DOX chemoresistance in breast cancer cell lines. FSTL1 overexpression caused significant elevation of stem cell biomarkers, as well as breast cancer cell proliferation. To determine whether the Wnt/β-catenin signaling pathway is involved in the observed effects of FSTL1, we assessed levels of pathway target. TOP/FOP flash, colony formation, and tumor sphere formation assays indicated that FSTL1 activates Wnt/β-catenin signaling through integrin β3. We then sought to identify a microRNA (miRNA) that regulates FSTL1 activity. Luciferase assays demonstrated that miR-137 reduces FSTL1 mRNA and protein levels. Ultimately, our findings indicate that there is an miR-137/FSTL1/integrin β3/Wnt/β-catenin signaling axis in breast cancer cells that regulates stemness and chemoresistance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Impact of molecular characteristics on coagulation was discussed. • Tannins and lignins-like components were readier to be coagulated by PFS. • Reactions between PFS and NOM/AOM were analyzed in molecular-level. • Coagulation is confirmed to be efficient in removing DBP precursors. Coagulation is generally considered as the key process to remove dissolved organic matters in water treatment plants, of which natural organic matter (NOM) and algal organic matter (AOM) are two commonly seen species. However, the removal efficiency of NOM and AOM by coagulation varies a lot. Here we unraveled the possible reasons for the different performance on NOM and AOM removal from molecule-level insights. With van Krevelen diagram, we observed that removed components in NOM and AOM were located in similar areas (high O/C and low H/C), indicating that polyferric sulfate (PFS) coagulation is prone to remove oxygen-containing and unsaturated components. Further, the results showed that NOM components are mainly composed of C, H, and O, with high O/C and low H/C, which therefore are easier to be removed (51%). However, ∼50% AOM components are N-containing, which mainly located in areas of low O/C and high H/C, leading to the relatively poor coagulation performance (19%). With further data processing of the Fourier transform ion cyclotron resonance (FT-ICR) results, we found that molecules with higher oxidized state or more carboxyl groups were preferentially removed by PFS coagulation. This study reveals the PFS coagulation mechanism of NOM and AOM, and provides theoretical foundations for enhanced coagulation in practical applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • A kinetic model was established to predict contaminants removal. • Carbonate radical played crucial roles with high selectivity. • Contribution of carbonate radical was significantly inhibited in real water. • Energy consumption was calculated based on the model. • The degradation pathway of acetaminophen was determined. UV/sodium percarbonate (UV/SPC) advanced oxidation process is an efficient and green technology to remove contaminants of emerging concern (CECs), with the simultaneous formation of hydroxyl radical (•OH) and carbonate radical (CO 3 •−). This study investigated the degradation of eight CECs in UV/SPC process, including phenol, atrazine, bisphenol A, ibuprofen, sulfadiazine, acetaminophen, carbamazepine, and diuron. A model was established to simulate the experimental results, which shows that both •OH and CO 3 •− played crucial roles in UV/SPC process, and the steady-state concentrations of CO 3 •− were much higher than that of •OH. The higher concentration compensates for the low reactivity of CO 3 •− with CECs and the loss of •OH depleting by CO 3 •−. Also, the Hammett constants decreased linearly with increasing natural logarithms of rate constants, indicating the selectivity of CO 3 •− with electron-donating substituents. In real water, CEC degradation was significantly inhibited due to the competition for light absorption from background compounds and scavenging of free radicals, especially CO 3 •−, by water matrix. Further, demethylation, decarbonylation, hydroxylation, amino oxidation, and quinonization were proposed as the main reactions for CEC degradation in UV/SPC process. The minimum electrical energy per order (EE/O) was calculated as 0.152 kWh m−3, with SPC dose of 0.138 mM and UV intensity of 2.02 × 10-7 Einstein L−1 s−1. [ABSTRACT FROM AUTHOR]

• Vacuum UV (VUV) degraded Cl-HALS rapidly with the lowest half-life ever reported. • Cl-HALs photolysis rates decreased with rising pH, DO, and initial concentration. • Mineralization appeared at high DO while photodehalogenation occurred at low DO. • Increasing DO increased •OH generation while increasing pH increased •H/e⁻ aq yield. • Tuning VUV treatability for pollutant control was achieved by adjusting pH and DO. Haloacetaldehydes (HALs) are the third largest disinfection by-product (DBP) ubiquitously detected in finished drinking water and have relatively higher toxicity than currently regulated DBPs. To efficiently alleviate them, this study investigated a green, chemical-free technology by using ultraviolet/vacuum ultraviolet (UV/VUV) on degrading three refractory chlorinated HALs (Cl-HALs). The results indicate that the rates of Cl-HALs decomposition in tap water irradiated by UV/VUV were 23–70 times higher than those irradiated by UV, proving that VUV instead of UV played the key role in degrading Cl-HALs. Increasing Cl-HALs dosage, pH, and dissolved oxygen (DO) all decreased the Cl-HALs degradations significantly, and the rates in tap water were apparently lower than those in ultrapure water. Unlike previous studies, this study proved that both oxidation and reduction were present during the VUV process. Photooxidation via oxidative radicals like •OH mineralized Cl-HALs, leading to substantial drops of total organic carbon; photoreduction via reductive radicals like •H dehalogenated Cl-HALs, resulting in formation of considerable intermediate organics (e.g., formic acid and acetic acid). No matter what pathway, the mass balances of chlorine were always maintained, meaning that dehalogenation occurred instantaneously rather than sequentially. Although the overall photodegradation rates dropped with rising pH and DO, photoreduction was increased with rising pH while photooxidation was elevated with rising DO. The results hence provide insights to better understand the VUV technology in controlling micropollutants in water. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the prognostic value of androgen receptor (AR) status in mammary Paget's disease (MPD). The purpose of this study was to explore AR status and the distribution of molecular subtypes in MPD as well as the relationship between AR expression and clinicopathological factors and to evaluate its prognostic value. Methods: We analyzed 170 MPD patients of varying subtypes. AR expression was verified by immunohistochemical staining, and the correlations between AR expression and clinicopathological characteristics and survival status were analyzed. We further investigated 91 MPD patients with invasive ductal carcinoma (MPD-IDC). Results: AR was expressed in 55.3% of overall MPD patients, and 78.2% had the human epidermal growth factor receptor 2 (HER2) overexpression subtype. AR positivity was significantly correlated with BMI (P = 0.037) and pathological N stage (P = 0.023). Multivariate analysis indicated that pathological T stage and pathological N stage were independent prognostic factors for overall survival (OS). The positive AR group was significantly associated with better OS (P = 0.014). Among 91 MPD-IDC patients, AR was expressed in 56.0%, and 80.0% had the HER2 overexpression subtype. AR positivity was significantly correlated with pathological N stage (P = 0.033). Multivariate analysis indicated that AR and pathological T stage were independent prognostic factors for OS. Furthermore, AR positivity was significantly related to better OS (P = 0.005) in MPD-IDC patients as well as in patients with the HER2 overexpression subtype (P = 0.029). Conclusion: Our results confirmed that AR is a potential biomarker for evaluating the prognosis of patients. [ABSTRACT FROM AUTHOR]

A novel LDMOS featuring bulk full gate (BFG) with bulk channel and electron accumulation effect, named BFG-LDMOS, is proposed and investigated. The BFG includes bulk gate oxide (BGO) that is inserted in the ${N}$ -drift and the full gate (FG) that is formed by the wide open base transistor (P-body/ ${N}$ -drift/P+). The gate potential is extended in the gate- ${N}$ -drift-drain (GND) region, and thus, the bulk channel of the P-body and electron accumulation effect of the ${N}$ -drift in the source- ${N}$ -drift-drain (SND) region is achieved, which significantly reduces the specific ON-resistance (${R}_{\text {ON,sp}}$). In addition, the P-body, ${N}$ -drift, and N+ drain are divided by the BGO, and the P-body/ ${N}$ -drift junction (PN1) sustains the breakdown electric field for both sides, which guarantees the breakdown voltage (BV) like conventional LDMOS. The 3-D simulation results indicate that the BV and ${R}_{\text {ON,sp}}$ are 249 V and 2.93 $\text{m}\Omega \cdot $ cm2 for the proposed BFG-LDMOS, respectively, and the Baliga’s figure of merit (FoM) is high up to 21 MW/cm2, which breaks through the silicon limit of the reduced surface field (RESURF). [ABSTRACT FROM AUTHOR]

Introduction: Human epidermal growth factor receptor-2 (HER-2) low expression breast malignant tumors have become a research hotspot in recent years, but it is still unclear whether HER-2 low expression represents a special subtype of breast cancer. However, this molecular type requires more effective treatment regimens in the neoadjuvant therapy stage. Methods: This study enrolled breast cancer patients who were treated at Harbin Medical University Cancer Hospital with neoadjuvant treatment between October 2011 and May 2019 and was a single-center retrospective study. Results: A total of 1,053 breast cancer patients who received preoperative therapy, including 279 (26%) HER-2 low expression patients, were included in this retrospective study. The HER-2 low expression group had a higher proportion of patients under 50 years old than the other two molecular subtype groups (p = 0.047, 62.0% vs. 57.2% and 52.5%), and the percentage of patients with Ki67 index above 15% was lower than that in HER-2-negative and HER-2-positive patients (p < 0.001, 50.2% vs. 63.6% and 71.5%). Most of the patients with HER-2 low expression were hormone receptor (HR) positive (p < 0.001, 85.7% vs. 60.4% and 36.0%), and their pathologic complete response (pCR) rate after neoadjuvant therapy was significantly lower than that of HER-2-negative and HER-2-positive patients (p < 0.001, 5.7% vs. 11.8% and 20.5%). The results of the subgroup analysis showed HR-positive patients with HER-2 low expression had a lower pCR rate (p < 0.001, 4.6% vs. 14.6%) and objective response rate (p = 0.001, 77.8% vs. 91.0%) than HER-2-positive patients and had no significant difference in these rates compared to HER-2-negative patients. There were no significant differences in overall survival (OS) and disease-free survival (DFS) up to 67 months (the median follow-up time) among HER-2 low, HER-2-negative, and HER-2-positive patients. The results of Cox hazard proportional showed that the Ki67 index and T stage (T3) were independent influencing factors for DFS. In terms of OS, Ki67 index, P53, T stage, and objective response were independent influencing factors for OS in HER-2 low expression patients. Conclusions: In general, further studies are needed to confirm that HER-2 low expression is a special breast cancer molecular subtype. The efficacy of neoadjuvant therapy in patients with HER-2 low expression is relatively poor, and the efficacy of neoadjuvant therapy can predict the prognosis of patients with HER-2 low expression. [ABSTRACT FROM AUTHOR]

Background and purpose: The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients. Methods: A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research. K–M survival curves and log-rank tests are used to determine OS and DFS. Cox regression model was used to evaluate the relationship between NPS and OS and DFS. Nomogram was developed based on the results of multivariate Cox regression analysis. Prognostic models were internally validated using bootstrapping and the consistency index (C-index). Results: Age group was correlated with NPS (p < 0.05). Low and moderate Naples risk patients had higher 5-year OS and DFS rates than high risk Naples patients (93.8% vs. 75.4% vs. 60.0%; X2 = 9.2, P = 0.01; 82.4% vs 64.5% vs 43.7%; X2 = 7.4, P = 0.024; respectively). The nomogram based on demonstrated good performance in predicting OS and DFS (AUC = 0.728, 0.630; respectively). Conclusions: In breast cancer patients who have undergone NAC, NPS is a novel prognostic indicator. NPS combined with clinicopathological features showed good predictive ability, and its performance was better than that of traditional pathological TNM staging. [ABSTRACT FROM AUTHOR]

Although significant advances have been made in the diagnosis and treatment of breast cancer (BC) in recent years, BC remains the most common cancer in women and one of the main causes of death among women worldwide. Currently, more than half of BC patients have no known risk factors, emphasizing the significance of identifying more tumor-related factors. Therefore, we urgently need to find new therapeutic strategies to improve prognosis. Increasing evidence demonstrates that the microbiota is present in a wider range of cancers beyond colorectal cancer. BC and breast tissues also have different types of microbiotas that play a key role in carcinogenesis and in modulating the efficacy of anticancer treatment, for instance, chemotherapy, radiotherapy, and immunotherapy. In recent years, studies have confirmed that the microbiota can be an important factor directly and/or indirectly affecting the occurrence, metastasis and treatment of BC by regulating different biological processes, such as estrogen metabolism, DNA damage, and bacterial metabolite production. Here, we review the different microbiota-focused studies associated with BC and explore the mechanisms of action of the microbiota in BC initiation and metastasis and its application in various therapeutic strategies. We found that the microbiota has vital clinical value in the diagnosis and treatment of BC and could be used as a biomarker for prognosis prediction. Therefore, modulation of the gut microbiota and its metabolites might be a potential target for prevention or therapy in BC. [ABSTRACT FROM AUTHOR]

Background: Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients. Methods: From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared. Results: NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706). Conclusions: The ML model XGBoost can well predict NSLNM in breast cancer patients. [ABSTRACT FROM AUTHOR]

Background. It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning. Methods. This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test. Results. After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance (AUC = 0.865 and 0.771 , respectively). For overall survival (OS, cutoff value = 0.3917) and disease-free survival (cutoff value = 0.4896), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001 ; and 59.6% vs. 32.1%, P < 0.0001 , respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables (AUC = 0.615 , P = 0.0003 ; AUC = 0.596 , P = 0.0003 , respectively). Conclusion. The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression. [ABSTRACT FROM AUTHOR]