Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning.

BACKGROUND: Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection. METHODS: The oncogenic function of DLG3 was explored in breast cancer cells via DLG3 knockdown. Immunohistochemistry (IHC) was used to evaluate the relationship between DLG3 expression and docetaxel/epirubin sensitivity. Machine learning (ML) and deep learning (DL) algorithms were used to develop multiomics signatures. Survival analysis was conducted by K-M curves and log-rank. Multivariate logistic regression analysis was used to develop nomograms. RESULTS: A total of 311 patients with malignant breast tumours who underwent NAC were retrospectively included in this multicentre study. Multiomics (DLG3, RADL and PATHO) signatures could accurately predict pCR (AUC: training: 0.900; testing: 0.814; external validation: 0.792). Its performance is also superior to that of clinical TNM staging and the single RADL signature in different cohorts. Patients in the low DLG3 group more easily achieved pCR, and those in the high RADL Signature_pCR and PATHO_Signature_pCR (OR = 7.93, 95 % CI: 3.49-18, P < 0.001) groups more easily achieved pCR. In the TEC regimen NAC group, patients who achieved pCR had a lower DLG3 score (4.00 ± 2.33 vs. 6.43 ± 3.01, P < 0.05). Patients in the low RADL_Signature_DLG3 and PATHO_Signature_DLG3 groups had lower DLG3 IHC scores (P < 0.05). Patients in the high RADL signature, PATHO signature and DLG3 signature groups had worse DFS and OS. CONCLUSIONS: Multiomics signatures (RADL, PATHO and DLG3) demonstrated great potential in predicting the pCR of breast cancer patients who underwent NAC. The RADL and PATHO signatures are associated with DLG3 status and could help doctors or patients choose proper neoadjuvant chemotherapy regimens (TEC regimens). This simple, structured, convenient and inexpensive multiomics model could help clinicians and patients make treatment decisions.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water.

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. ENVIRONMENTAL IMPLICATION: 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.

Clinicopathological Characteristics of Breast Cancer Patients with HER-2 Low Expression Receiving Neoadjuvant Therapy.

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.

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models.

Reactions of reactive halogen species (Cl•, Br•, and Cl2•-) 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 Cl2•-, respectively, with R2test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl2•- more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl2•-, 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.

Development and validation of a nomogram for predicting internal mammary sentinel node metastasis in breast cancer patients.

OBJECTIVE: Internal mammary nodes are important in breast cancer prognosis, but their diagnosis is often missed in clinical practice, leading to inaccurate staging and treatment. We developed a validated nomogram to predict the presence of internal mammary sentinel nodes (IMSN) metastasis. METHODS: A total of 864 sequential IMSN biopsy procedures from a prospective studies database of 1505 cases were used for model development and validation. Multivariable logistic regression was performed on 519 sequential IMSN biopsy procedures from multi-center data between August 2018 and July 2022 to predict the presence of IMSN metastasis. A nomogram was developed based on the logistic regression model and subsequently applied to 345 sequential IMSN biopsy procedures from single-center data between November 2011 and July 2018. The model's discrimination was assessed using the area under the receiver operating characteristic curve. RESULTS: The overall frequency of IMSN metastasis was 17.0% in our study. A predictive model for IMSN metastasis was constructed using tumor size, tumor location, lymphovascular invasion, the number of positive axillary nodes (P < 0.05 for all variables in multivariate analysis), and histological grade (P < 0.05 only in univariate analysis). The nomogram was accurate, with a concordance index of 0.84 in the bootstrapping analysis and an area under the receiver operating characteristic curve of 0.80 in the validation population. CONCLUSION: Our nomogram provides an accurate and validated multivariable predictive model for estimating the individual likelihood of having IMSN metastasis. This may be useful for personalized treatment decisions regarding internal mammary radiotherapy in breast cancer patients.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy.

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.

Lower subcutaneous fat index predicts bone metastasis in breast cancer.

BACKGROUND: Bone metastases affect 50% to 70% of breast cancer (BC) patients and have a high mortality rate. Adipose tissue loss plays a pivotal role in the progression of cancer. OBJECTIVE: This study aims to evaluate the prognostic value of adipose tissue for bone metastasis in BC patients. METHODS: 517 BC patients were studied retrospectively. Patients' characteristics before the surgery were collected. Quantitative measurements of the subcutaneous fat index (SFI) were performed at the level of the eleventh thoracic vertebra. In order to adjust for the heterogeneity between the low SFI and high SFI groups, propensity score matching (PSM) was used. The Kaplan-Meier method was used to estimate the 5-year bone metastatic incidence. The prognostic analysis was performed with the Cox regression models. RESULTS: Compared with the patients without bone metastasis, the patients with bone metastasis had reduced SFI levels. In addition, Kaplan-Meier analysis revealed that patients with low SFI were more likely to develop bone metastases. The independent predictive value of SFI for bone metastases was confirmed by Cox regression analysis. The survival analysis was repeated after PSM with a 1:1 ratio, yielding similar results (P< 0.05). CONCLUSIONS: SFI is an independent predictor of bone metastasis in BC patients.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning.

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.

Predictive value of controlling nutritional status score in postoperative recurrence and metastasis of breast cancer patients with HER2-low expression.

Background: To investigate the predictive value of controlling nutritional status (CONUT) score in Postoperative Recurrence and Metastasis of Breast Cancer Patients with HER2-Low Expression. Methods: The clinicopathological data of 697 female breast cancer patients who pathology confirmed invasive ductal carcinoma and surgery in Harbin Medical University Tumor Hospital from January 2014 to January 2017 were retrospectively analyzed. The relationship between CONUT score and various clinicopathological factors as well as prognosis was evaluated. Results: Based on the cut-off point of ROC curve, compared with the low CONUT score group, the high CONUT score group had worse 5-year RFS. In subgroup analysis, compared with the low CONUT group, the high CONUT group had worse prognosis at different TNM stages. Univariate and multivariate results showed that the low CONUT score group had better overall survival and recurrence-free survival than the high CONUT group. Conclusion: CONUT score is an independent predictor of postoperative recurrence and metastasis in HER2-low breast cancer patients. It is may be used as an effective tool to predict the recurrence and metastasis of HER2-low breast cancer.

Ultrasound Triggered Tumor Metabolism Suppressor Induces Tumor Starvation for Enhanced Sonodynamic Immunotherapy of Breast Cancer.

Introduction: Sonodynamic therapy (SDT) as an emerging tumor treatment gained wide attention. However, tumor vascular destruction and oxygen depletion in SDT process may lead to further hypoxia. This may lead to enhanced glycolysis, lactate accumulation, and immunosuppression. Methods: A glycolysis inhibitor (3PO) loaded and PEG modified black phosphorus nanosheets (BO) is constructed for potent starvation therapy and efficient immune activation. Results: Under ultrasound irradiation, the BO can produce ROS to destroy tumors and tumor blood vessels and lead to further hypoxia and nutrients block. Then, the released 3PO inhibits tumor glycolysis and prevents the hypoxia-induced glycolysis and lactate accumulation. Both SDT and 3PO can cut off the source of lactic acid, as well as achieve antitumor starvation therapy through the blockade of the adenosine triphosphate (ATP) supply. In addition, the combination of starvation treatment and SDT further facilitates dendritic cells (DC) maturation, promotes antigen presentation by DCs, and eventually propagates the antitumor immunity and inhibition of abscopal tumor growth. Conclusion: This is the first time that combines SDT with inhibition of glycolysis, achieving admirable tumor treatment and decreasing adverse events caused by SDT process and that has caused good immune activation. Our system provides a new idea for the future design of anti-tumor nanomedicines.

The tremendous clinical potential of the microbiota in the treatment of breast cancer: the next frontier.

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.

Gold nanoparticle doped Cuhemin nanosheets with a remodeling tumor microenvironment for multiple radiotherapy sensitization.

Effective radiosensitizers are urgently needed due to the serious negative effects that high radiation doses might have. We created an integrated nano-system (Cuhemin-Au) made of Cuhemin nanosheets and Au nanoparticles (Au NPs) for sensitizing radiotherapy to solve this issue. This system can manifest enzyme-like activities to universally suppress the resistance pathways in breast cancer cells for amplifying radiation damage. Cuhemin-Au NPs increase the energy deposition of radiation owing to the high X-ray attenuation coefficient of Au. In addition, Cuhemin-Au has peroxidase (POD)-like and glucose oxidase (Gox)-like activity, and can also consume intracellular GSH, which can reduce intracellular GSH levels to reduce tumor cells' capacity to repair DNA and deplete intracellular glucose via their characteristic Gox-like catalytic activities, which can cause an increase in the oxidative stress and further produce H2O2. Cuhemin-Au then produced ˙OH, which upsets redox equilibrium and destroys mitochondria, leading to radiation sensitivity, after reacting with enough hydrogen peroxide in tumor cells. Cuhemin-Au combined with low dose RT (4 Gy) could significantly limit tumor development with fewer adverse effects, according to in vivo and in vitro experiments. This platform generated a fresh concept for the construction of a radiotherapy sensitization system and accomplished synergistic radiotherapy sensitization.

Correlation analysis between androgen receptor and the clinicopathological features and prognosis of mammary Paget's disease.

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.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy.

BACKGROUND AND PURPOSE: The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR). METHODS: A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis. We determined OS and DFS using K-M survival curves and the log-rank test. The relationship between the mSIS and OS and DFS was evaluated by a Cox regression model. A nomogram was constructed based on Cox regression analysis. RESULTS: Patients in the mSIS low-risk group had better 5- and 8-year OS rates than those in the mSIS high-risk group (59.8% vs. 77.0%; 50.1% vs. 67.7%; X2 = 8.5, P = 0.0035, respectively). Patients in the mSIS (1 + 2 score) + pCR subgroup had the highest 5- and 8-year OS and disease-free survival (DFS) rates (OS: 55.0% vs. 75.7% vs. 84.8, 42.8% vs. 65.7% vs. 79.8%, X2 = 16.6, P = 0.00025; DFS: 38.8% vs. 54.7% vs. 76.3%, 33.3% vs. 42.3 vs. 72.1%, X2 = 12.4, P = 0.002, respectively). Based on the mSIS, clinical T stage and pCR results, the nomogram had better predictive ability than the clinical TNM stage, NLR and Alb. CONCLUSIONS: mSIS is a promising prognostic tool for malignant breast tumor patients who underwent NAC, and the combination of mSIS and pCR is helpful in enhancing the ability to predict a pCR.

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.

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.

A multicentre single arm phase 2 trial of neoadjuvant pyrotinib and letrozole plus dalpiciclib for triple-positive breast cancer.

Current therapies for HER2-positive breast cancer have limited efficacy in patients with triple-positive breast cancer (TPBC). We conduct a multi-center single-arm phase 2 trial to test the efficacy and safety of an oral neoadjuvant therapy with pyrotinib, letrozole and dalpiciclib (a CDK4/6 inhibitor) in patients with treatment-naïve, stage II-III TPBC with a Karnofsky score of ≥70 (NCT04486911). The primary endpoint is the proportion of patients with pathological complete response (pCR) in the breast and axilla. The secondary endpoints include residual cancer burden (RCB)-0 or RCB-I, objective response rate (ORR), breast pCR (bpCR), safety and changes in molecular targets (Ki67) from baseline to surgery. Following 5 cycles of 4-week treatment, the results meet the primary endpoint with a pCR rate of 30.4% (24 of 79; 95% confidence interval (CI), 21.3-41.3). RCB-0/I is 55.7% (95% CI, 44.7-66.1). ORR is 87.4%, (95% CI, 78.1-93.2) and bpCR is 35.4% (95% CI, 25.8-46.5). The mean Ki67 expression reduces from 40.4% at baseline to 17.9% (P < 0.001) at time of surgery. The most frequent grade 3 or 4 adverse events are neutropenia, leukopenia, and diarrhoea. There is no serious adverse event- or treatment-related death. This fully oral, chemotherapy-free, triplet combined therapy has the potential to be an alternative neoadjuvant regimen for patients with TPBC.

Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework.

Abstract: Background and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM. Methods: From the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis. Results: Age and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size <=20mm. Compared to other models, Xgboost was the best ML model with the biggest AUC of 0.813 (95% CI: 0.7994 - 0.8262) and the smallest Brier score of 0.186 (95% CI: 0.799-0.826). SHAP plots demonstrated that tumor size was the most vital risk factor for LNM. In both training and test sets, Xgboost had better AUC (0.761 vs 0.745; 0.813 vs 0.775; respectively), and it also achieved a smaller Brier score (0.202 vs 0.204; 0.186 vs 0.191; 0.220 vs 0.221; respectively) than the nomogram model based on LR in those three different sets. After adjusting for five most influential variables (tumor size, age, ER, HER-2, and PR), prediction score based on the Xgboost model was still correlated with LNM (adjusted OR:2.73, 95% CI: 1.30-5.71, P=0.008). Conclusions: The Xgboost model outperforms the traditional LR-based nomogram model in predicting the LNM of IMPC patients. Combined with SHAP, it can more intuitively reflect the influence of different variables on the LNM. The tumor size was the most important risk factor of LNM for breast IMPC patients. The prediction score obtained by the Xgboost model could be a good indicator for LNM.

The Pretreatment Systemic Inflammation Response Index as a Useful Prognostic Factor is Better Than Lymphocyte to Monocyte Ratio in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy.

It has been reported that the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR), as well as systemic inflammation response index (SIRI), are closely related with overall survival (OS) in breast cancer patients. However, which one is the optimal indicator is vague. This study incorporates 280 breast cancer patients who received NACT. A cut-off value of LMR, PLR, SIRI and NLR is determined by Youden index. The Pearson's X2 test or Fisher's exact test is applied to compare the correlation of different clinicopathologic characteristics divided by SIRI. The K-M survival curves and log-rank test were applied to determine OS. Univariate and multivariable analysis are explored by the Cox regression model. We apply the Z test to contrast the prognostic capacity of SIRI, LMR, PLR, and NLR. At the meanwhile, we construct the nomogram based on the results of multivariable analysis. All enrolled cases are divided into two parts by pretreatment SIRI (cut-off value = 0.52). Compared to high pre-treatment SIRI, high pre-treatment NLR and clinical T3 + T4 stage, the low pre-treatment SIRI, low pretreatment NLR and clinical T1 + T2 stage had longer OS time. The Z test showed that the SIRI group had bigger AUC than LMR and PLR, and the difference is statistically significant. The ability of nomogram, based on pretreatment SIRI, pre-treatment NLR and clinical T stage, to predict the 3-year, 5-year, and 8-year overall survival rates of breast malignant tumor patients is better than clinical TNM stage. Pre-treatment SIRI was a more crucial and integral prognostic index for breast malignant tumor patients receiving NACT. It could be helpful for doctors to predict the prognosis of breast malignant tumor patients and to evaluate the treatment status of patients.

Coexisting oxidation and reduction of chloroacetaldehydes in water by UV/VUV irradiation.

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.