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ctDNA and tumor-based biomarkers of giredestrant response in acelERA breast cancer
Abstract Endocrine therapy (ET) resistance in estrogen receptor positive (ER+) advanced breast cancer is often linked to ESR1 mutations, yet responses to oral selective ER degraders vary within mutant subgroups. Through a biomarker analysis of acelERA Breast Cancer (NCT04576455), we show that tumor ER transcriptional activity as well as circulating tumor DNA (ctDNA) genomics and dynamics effectively stratify response to ET, including giredestrant. We find that following first-line therapy, the ctDNA genomic landscape is diverse and influenced by CDK4/6 inhibitor exposure. Despite this complexity, ER activity in ESR1 -mutant tumors remains comparable to early breast cancer but is reduced in most non-mutant cases. This maintained ER activity is associated with giredestrant benefit. Furthermore, early ctDNA clearance identifies responding patients, and the combination of low ER activity and high ctDNA burden predicts rapid clinical progression. These findings provide a framework for personalizing future breast cancer therapies by integrating liquid biopsies with tissue-based signatures.
Massive Intravascular Hemolysis from <i>Clostridium perfringens</i> Bacteremia
Nonlinear dynamics and stability of a delayed leukemia model with real-world applications
Proteostasis failure and mitochondrial dysfunction contribute to chromosomal instability-induced microcephaly
European Study of Prostate Cancer Screening — 23-Year Follow-up
Development and validation of a cardiometabolic multimorbidity prediction model in middle-aged and older adults
Abstract Cardiometabolic multimorbidity (CMM) is a prevalent syndrome among middle-aged and older adults, significantly impairing quality of life and imposing substantial health and economic burdens on China’s aging healthcare system. The development of timely predictive models is crucial for enabling early intervention. This study aimed to integrate multidimensional data from the China Health and Retirement Longitudinal Study (CHARLS) to develop an effective predictive model for assessing the five-year risk of CMM onset, thereby facilitating early intervention and management for individuals at risk in China. We analyzed data from the 2015 to 2020 CHARLS surveys, involving 5,388 middle-aged and older adults initially free of CMM. The dataset was randomly split into a training set (70%) and a validation set (30%). Key predictors were identified from 31 potential variables using LASSO regression with 10-fold cross-validation. Selected predictors underwent correlation analysis and were used to construct both Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) models. Variable contributions in the XGBoost model were interpreted using SHapley Additive exPlanations (SHAP) values, while the LR model was visualized via a nomogram. The performance of the superior model was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. LASSO regression screened significant variables from the initial 31 candidates. Correlation analysis ultimately identified nine key predictors: systolic blood pressure, BMI, fasting blood glucose, total cholesterol, triglycerides, uric acid, age, comorbidities, and pain. The logistic regression model demonstrated superior and more stable predictive performance on the validation set, with an area under the curve (AUC) of 0.732 (95% CI: 0.703–0.761). Calibration curves indicated reliable predictive accuracy, and decision curve analysis established the clinical net benefit across a range of risk thresholds. This study developed and validated a reliable, clinically applicable logistic regression model, supplemented by a nomogram, for assessing the five-year risk of CMM in Chinese middle-aged and older adults. The model effectively identifies high-risk individuals, supporting targeted early intervention and management strategies to alleviate the health and economic burdens associated with CMM in this population.
Microgravity-activated high-performance van der Waals InSe ferroelectric semiconductor
A Feverish Pace
Achieving precise chip control for high-end manufacturing
Abstract In high-end manufacturing, where precision and automation are strictly required, chip control presents an incredibly daunting challenge during the machining process. In this paper, a novel chip control method called grooves induced chip-breaking (GICB) is comprehensively investigated. The core concept of GICB is to achieve spontaneous chip-breaking through the pre-processed micro grooves (PPMG) on the workpiece surface. To understand the underlying chip-breaking mechanisms, PPMG with identical geometric shapes are fabricated on the surface of a 316 L stainless steel workpiece using laser ablation. A cutting experiment is then conducted, where the cutting depth ( d ) and feed rate ( f ) are varied. Experimental observations reveal that, apart from enabling controllable chip-breaking, the GICB method can also enhance the quality of the machined surface. Specifically, the surface roughness (Ra) is reduced by up to 26.6%. Further in-depth analyses indicate that the primary cause of this improvement is the reduced fluctuation of the thrust force. With the assistance of PPMG, the chips produced under controlled periodic fracture exhibit similar lengths and curvatures. Benefiting from that, chip agglomeration and entanglement are effectively avoided. In addition, the fluctuation of cutting force is greatly reduced, which in turn improves the machined surface quality. The practical significance of this study is twofold. Firstly, a high-performance chip control method with significant potential applications is systematically explored. Secondly, for the first time, the crucial relationship between chip control and machined surface quality is experimentally verified.
XA-Novo: high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures
The COBRRA Trial — Ending the Venous Thromboembolism Safety Toss-up
Research on characteristic recognition and quantification of internal powder residue in LPBF porous structure based on image processing
Exon inclusion signatures enable accurate estimation of splicing factor activity
Postpartum Persistence of Ebola Virus in Breast Milk
Giant energy storage and dielectric performance in all-polymer nanocomposites
Development of Quantum dot-based enzyme biosensor for the detection of dopamine in urine
Abstract Dopamine (DA), a neurotransmitter released by the hypothalamus, plays a significant role in maintaining mental well-being. Abnormal DA level leads to neurological disorders such as depression and schizophrenia. Consequently, DA is commonly monitored in urine using various analytical methods as a non‑invasive approach for assessing its physiological status. The available methods for DA detection are laborious and time-consuming. To circumvent this issue, we developed a Quantum dot-based enzyme biosensor for the rapid, sensitive detection of DA. Fluorescence quenching of QDs was in proportion with the DA concentration and was found to be linear with an R 2 = 0.99, with p < 0.05. The biosensor used to detect DA in urine samples in the range of 1.2 µM-8 µM, with R² = 0.97 and p < 0.05, and a limit of detection (LOD) of 1.2 µM in a urine sample (1:100). Spiking and recovery analysis in urine showed 94–98% recovery with p < 0.05. The developed method showed specificity towards detecting DA in the presence of common interfering factors such as uric acid and ascorbic acid. The results show that dopamine-specific quenching is consistent and concentration-dependent, effectively distinguishing the target from background components in complex samples. This approach provides a promising platform for reliable DA monitoring in clinical diagnostics.
Synthesis of monodisperse InSb colloidal quantum dots by monomer concentration control for short-wave infrared photodetectors
Abstract InSb colloidal quantum dots combine a low bulk bandgap (0.17 eV) with a large exciton Bohr radius, enabling access to short-wave infrared wavelength within the quantum confinement regime, alongside strong covalent bonding, complementary metal-oxide semiconductor compatibility, and restriction of hazardous substances compliance. However, prior one-pot and hot-injection approaches yield broad size distributions and weak excitonic absorption, while continuous-injection methods improve spectral features but are restricted to small dot sizes (<1.2 μm excitonic peaks). Here, we introduce a monomer-concentration-controlled approach that produces narrow-size-dispersed InSb quantum dots tunable from 950 to 1900 nm with the sharpest excitonic absorption peaks reported to date. Their monodisperse nature allowed the emergence of heavy hole-light hole splitting evident in their optical absorption spectra. Leveraging these high-quality nanocrystals, we demonstrate short-wave infrared photodetectors achieving external quantum efficiencies of 22% at 1500 nm and 19% at 1580 nm, extending the spectral reach of III-V colloidal quantum dot photodetectors at this wavelength range.