A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer

PurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People’s Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People’s Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.ResultsIn the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.ConclusionThe developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP

Correction-Efficiency-Coefficient-Based Trajectory Optimization for Two-Dimensional Trajectory Correction Projectile

Based on the lift-to-drag ratio of a two-dimensional trajectory correction projectile, in this paper, a novel correction efficiency coefficient model has been proposed for the trajectory optimization of a two-dimensional trajectory correction projectile, and research on the influence a correction efficiency coefficient has on the flight parameters of correction trajectory is carried out. A series of results are obtained through theoretical analysis and simulation calculations, indicating that, the smaller the value of the correction efficiency coefficient is, the stronger the correction ability of the projectile reserves. The trajectory and canard geometry of the correction section of the two-dimensional trajectory correction projectile are optimized by the Gauss pseudo-spectral method and correction efficiency coefficient, and, after taking the correction efficiency coefficient into account, the projectile can accurately hit the target and effectively eliminate the swing phenomenon of the projectile’s lateral trajectory. Meanwhile, a stable roll control command is obtained. When the diameter aspect ratio of the canard is 0.4, both the flight state quantity of the optimized projectile and the roll control command are more stable, and, when the canard shape is trapezoidal, the correction efficiency coefficient is smaller, the result of trajectory optimization is more stable, and the stability of the output roll control command is better. The research results of this paper can provide certain references for both the designs of the two-dimensional trajectory correction projectile’s trajectory and the canard geometry

A Leakage Model of Contact Mechanical Seals Based on the Fractal Theory of Porous Medium

A theoretical model for calculating the leakage rate of contact mechanical seals based on the fractal theory of the porous media, which can consider the real seal contact interface and objectively reflect the flow of the interfacial fluid from a microscopic perspective, is established. In order to obtain the microstructural parameters of the porous media included in the leakage model, such as the fractal dimension and the maximum pore diameter, the real seal contact interface obtained from experiments is reconstructed, a contact model between the dynamic and static rings is proposed, and then the calculation methods for the interface characteristic parameters are provided. Numerical simulation results show that as the contact pressure increases from 0.05 to 0.5 MPa, the interface porosity and the maximum pore diameter decreases gradually. Furthermore, the fractal dimension of the pore area increases and the leakage rate of the interface decreases from 0.48 to 0.33 mL/h. The proposed method provides a novel way of calculating the leakage rate of contact mechanical seals

Synthesis of 2,4-Dicyanoalkylated Benzoxazines through the Radical-Mediated Cascade Cyclization of Isocyanides with AIBN under Metal- and Additive-Free Conditions

A general and novel method for the radical cascade cyclization of aryl isocyanides with AIBN has been described. This strategy provides straightforward access to various 2,4-dicyanoalkylated benzoxazines in moderate to good yields under metal- and additive-free conditions. The reaction can apply to a gram scale and tolerate diverse functional groups. 2,4-Dicyanoalkylated benzoxazine derivatives feature a large Stokes shift and intramolecular charge transfer properties

Metalloporphyrin-based porous polymers prepared via click chemistry for size-selective adsorption of protein

<p>Zinc porphyrin-based porous polymers (PPs-Zn) with different pore sizes were prepared by controlling the reaction condition of click chemistry, and the protein adsorption in PPs-Zn and the catalytic activity of immobilized enzyme were investigated. PPs-Zn-1 with 18 nm and PPS-Zn-2 with 90 nm of pore size were characterized by FTIR, NMR and nitrogen absorption experiments. The amount of adsorbed protein in PPs-Zn-1 was more than that in PPs-Zn-2 for small size proteins, such as lysozyme, lipase and bovine serum albumin (BSA). And for large size proteins including myosin and human fibrinogen (HFg), the amount of adsorbed protein in PPs-Zn-1 was less than that in PPs-Zn-2. The result indicates that the protein adsorption is size-selective in PPs-Zn. Both the protein size and the pore size have a significant effect on the amount of adsorbed protein in the PPs-Zn. Lipase and lysozyme immobilized in PPs-Zn exhibited excellent reuse stability.</p

Effect of the probiotic strain, Lactiplantibacillus plantarum P9, on chronic constipation: A randomized, double-blind, placebo-controlled study

Chronic constipation (CC) is a common gastrointestinal condition associated with intestinal inflammation, and the condition considerably impairs patients’ quality of life. We conducted a large-scale 42-day randomized, double-blind, placebo-controlled trial to investigate the effect of probiotics in alleviating CC. 163 patients diagnosed with CC (following Rome IV criteria) were randomly divided into probiotic (n = 78; received Lactiplantibacillus plantarum P9 [P9]; 1 ×1011 CFU/day) and placebo (n = 85; received placebo material) groups. Ingesting P9 significantly improved the weekly mean frequency of complete spontaneous bowel movements (CSBMs) and spontaneous bowel movements (SBMs), while significantly reducing the level of worries and concerns (WO; P < 0.05). Comparing with the placebo group, P9 group was significantly enriched in potentially beneficial bacteria (Lactiplantibacillus plantarum and Ruminococcus_B gnavus), while depriving of several bacterial and phage taxa (Oscillospiraceae sp., Lachnospiraceae sp., and Herelleviridae; P < 0.05). Interesting significant correlations were also observed between some clinical parameters and subjects’ gut microbiome, including: negative correlation between Oscillospiraceae sp. and SBMs; positive correlation between WO and Oscillospiraceae sp., Lachnospiraceae sp. Additionally, P9 group had significantly (P < 0.05) more predicted gut microbial bioactive potential involved in the metabolism of amino acids (L-asparagine, L-pipecolinic acid), short-/medium-chain fatty acids (valeric acid and caprylic acid). Furthermore, several metabolites (p-cresol, methylamine, trimethylamine) related to the intestinal barrier and transit decreased significantly after P9 administration (P < 0.05). In short, the constipation relief effect of P9 intervention was accompanied by desirable changes in the fecal metagenome and metabolome. Our findings support the notion of applying probiotics in managing CC

Image_1_A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer.pdf

PurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People’s Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People’s Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.ResultsIn the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.ConclusionThe developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.</p