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Triple-negative breast cancer (TNBC), a specific subtype of breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER-2), has clinical features that include high invasiveness, high metastatic potential, proneness to relapse, and poor prognosis. Because TNBC tumors lack ER, PR, and HER2 expression, they are not sensitive to endocrine therapy or HER2 treatment, and standardized TNBC treatment regimens are still lacking. Therefore, development of new TNBC treatment strategies has become an urgent clinical need. By summarizing existing treatment regimens, therapeutic drugs, and their efficacy for different TNBC subtypes and reviewing some new preclinical studies and targeted treatment regimens for TNBC, this paper aims to provide new ideas for TNBC treatment.

Metabolic abnormalities are an important feature of tumours. The glutamine-arginine-proline axis is an important node of cancer metabolism and plays a major role in amino acid metabolism. This axis also acts as a scaffold for the synthesis of other nonessential amino acids and essential metabolites. In this paper, we briefly review (1) the glutamine addiction exhibited by tumour cells with accelerated glutamine transport and metabolism; (2) the methods regulating extracellular glutamine entry, intracellular glutamine synthesis and the fate of intracellular glutamine; (3) the glutamine, proline and arginine metabolic pathways and their interaction; and (4) the research progress in tumour therapy targeting the glutamine-arginine-proline metabolic system, with a focus on summarising the therapeutic research progress of strategies targeting of one of the key enzymes of this metabolic system, P5CS (ALDH18A1). This review provides a new basis for treatments targeting the metabolic characteristics of tumours.

CD133 and Notch1 double-positive GSCs were preferentially located along Jagged1-expressing white matter tracts, which exhibited a demyelinated phenotype. The NOTCH1–SOX9–SOX2 positive-feedback loop controls GSC invasion along white matter tracts.

High aldehyde dehydrogenase (ALDH) activity is a metabolic feature of adult stem cells and various cancer stem cells (CSCs). The ALDEFLUOR system is currently the most commonly used method for evaluating ALDH enzyme activity in viable cells. This system is applied extensively in the isolation of normal stem cells and CSCs from heterogeneous cell populations. For many years, ALDH1A1 has been considered the most important subtype among the 19 ALDH family members in determining ALDEFLUOR activity. However, in recent years, studies of many types of normal and tumour tissues have demonstrated that other ALDH subtypes can also significantly influence ALDEFLUOR activity. In this article, we briefly review the relationships between various members of the ALDH family and ALDEFLUOR activity. The clinical significance of these ALDH isoforms in different cancers and possible directions for future studies are also summarised.

Background The overall consumption of ultra-processed food (UPF) has previously been associated with type 2 diabetes. However, due to the substantial heterogeneity of this food category, in terms of their nutritional composition and product type, it remains unclear whether previous results apply to all underlying consumption patterns of UPF. Methods Of 70,421 participants (35–70 years, 58.6% women) from the Lifelines cohort study, dietary intake was assessed with a food frequency questionnaire. UPF was identified according to the NOVA classification. Principal component analysis (PCA) was performed to derive UPF consumption patterns. The associations of UPF and adherence to UPF consumption patterns with incidence of type 2 diabetes were studied with logistic regression analyses adjusted for age, sex, diet quality, energy intake, alcohol intake, physical activity, TV watching time, smoking status, and educational level. Results During a median follow-up of 41 months, a 10% increment in UPF consumption was associated with a 25% higher risk of developing type 2 diabetes (1128 cases; OR 1.25 [95% CI 1.16, 1.34]). PCA revealed four habitual UPF consumption patterns. A pattern high in cold savory snacks (OR 1.16 [95% CI 1.09, 1.22]) and a pattern high in warm savory snacks (OR 1.15 [95% CI 1.08, 1.21]) were associated with an increased risk of incident type 2 diabetes; a pattern high in traditional Dutch cuisine was not associated with type 2 diabetes incidence (OR 1.05 [95% CI 0.97, 1.14]), while a pattern high in sweet snacks and pastries was inversely associated with type 2 diabetes incidence (OR 0.82 [95% CI 0.76, 0.89]). Conclusions The heterogeneity of UPF as a general food category is reflected by the discrepancy in associations between four distinct UPF consumption patterns and incident type 2 diabetes. For better public health prevention, research is encouraged to further clarify how different UPF consumption patterns are related to type 2 diabetes.

Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes an adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT). A composite noise model is first established to characterize the interference. The signal undergoes a five-level DWT decomposition, where a local energy detection mechanism distinguishes signal-dominant from noise-dominant regions. An SNR-driven dynamic thresholding strategy is generated by combining inter-layer adaptive allocation with coefficient-level local weighting, followed by processing with an improved smoothing function to effectively suppress reconstruction artifacts. Simulations at a 1 dB input signal-to-noise ratio (SNR) yielded a 15.45 dB output SNR and a 0.05634 root mean square error (RMSE), outperforming traditional wavelet methods and modern benchmarks such as local variance and variational mode decomposition (VMD). Applied to a practical signal from an isolated vascular phantom with an initial SNR of −1.04 dB, the algorithm achieved a 13.86 dB output SNR and a 0.00258 RMSE. Results confirm the algorithm’s effectiveness for high-fidelity waveform capture in complex noise environments, offering a robust solution for vascular hemodynamic monitoring

Postoperative tumor recurrence and metastases often lead to cancer treatment failure. Here, we develop a local embedded photodynamic immunomodulatory DNA hydrogel for early warning and inhibition of postoperative tumor recurrence. The DNA hydrogel contains PDL1 aptamers that capture and enrich in situ relapsed tumor cells, increasing local ATP concentration to provide a timely warning signal. When a positive signal is detected, local laser irradiation is performed to trigger photodynamic therapy to kill captured tumor cells and release tumor-associated antigens (TAA). In addition, reactive oxygen species break DNA strands in the hydrogel to release encoded PDL1 aptamer and CpG, which together with TAA promote sufficient systemic antitumor immunotherapy. In a murine model where tumor cells are injected at the surgical site to mimic tumor recurrence, we find that the hydrogel system enables timely detection of tumor recurrence by enriching relapsed tumor cells to increase local ATP concentrations. As a result, a significant inhibitory effect of approximately 88.1% on recurrent tumors and effectively suppressing metastasis, offering a promising avenue for timely and effective treatment of postoperative tumor recurrence. Decreased survival after surgery is often associated to post-operative tumor recurrence and metastasis. Here the authors describe a DNA hydrogel enabling monitoring of tumor recurrence and spatiotemporally controlled photodynamic immunotherapy to prevent post-operative tumor recurrence and metastasis.

Cereal crop production is severely affected by seed-borne bacterial diseases across the world. Locally occurring disease resistance in various crops remains elusive. Here, we have observed that rice plants of the same cultivar can be differentiated into disease-resistant and susceptible phenotypes under the same pathogen pressure. Following the identification of a seed-endophytic bacterium as the resistance-conferring agent, integration of high-throughput data, gene mutagenesis and molecular interaction assays facilitated the discovery of the underlying mode of action. Sphingomonas melonis that is accumulated and transmitted across generations in disease-resistant rice seeds confers resistance to disease-susceptible phenotypes by producing anthranilic acid. Without affecting cell growth, anthranilic acid interferes with the sigma factor RpoS of the seed-borne pathogen Burkholderia plantarii , probably leading to impairment of upstream cascades that are required for virulence factor biosynthesis. The overall findings highlight the hidden role of seed endophytes in the phytopathology paradigm of ‘disease triangles’, which encompass the plant, pathogens and environmental conditions. These insights are potentially exploitable for modern crop cultivation threatened by globally widespread bacterial diseases. In rice, one endophyte ( Sphingomonas melonis ) colonizes seeds and produces anthranilic acid, which confers resistance to a bacterial pathogen ( Burkholderia plantarii ) in the plant.

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.

Studying key leaf functional traits is crucial for understanding plant resource utilization strategies and growth. To explore the patterns and driving factors of key leaf functional traits in forests along elevational gradients under global change, we collected survey data from 697 forests across China from 2008 to 2020. This study examined the elevational patterns of Specific Leaf Area (SLA, m²/kg), Leaf Dry Matter Content (LDMC, g/g), Leaf Nitrogen (LN, mg/g), and Leaf Phosphorus (LP, mg/g), and their responses to climate, soil nutrients, and stand factors. The results showed distinct differences in these key leaf traits at different elevational gradients. Generally, as elevation increased, SLA decreased, while LDMC significantly increased ( P < 0.001), and LN first increase and then decreased ( P < 0.001). The direct influence of elevation on the spatial variation of key leaf traits was greater than its indirect effects (through environmental and stand factors). The elevational patterns of leaf traits related to resource utilization strategies (SLA and LDMC) were mainly influenced by climate (temperature and precipitation) and soil nutrient factors, showing opposite trends in response to environmental changes. The patterns of leaf nutrient traits (LN and LP) along elevational gradients were primarily influenced by climatic factors, with LN exhibiting greater environmental plasticity. Compared to other stand factors, forest age predominantly influenced the spatial variation of key leaf traits, especially SLA. These findings have significant theoretical implications for revealing how plants adapt to global change.