Amino acids and their roles in tumor immunotherapy of breast cancer.

Breast cancer is the most commonly diagnosed cancer among women. The primary treatment options include surgery, radiotherapy, chemotherapy, targeted therapy and hormone therapy. The effectiveness of breast cancer therapy varies depending on the stage and aggressiveness of the cancer, as well as individual factors. Advances in early detection and improved treatments have significantly increased survival rates for breast cancer patients. Nevertheless, specific subtypes of breast cancer, particularly triple-negative breast cancer, still lack effective treatment strategies. Thus, novel and effective therapeutic targets for breast cancer need to be explored. As substrates of protein synthesis, amino acids are important sources of energy and nutrition, only secondly to glucose. The rich supply of amino acids enables the tumor to maintain its proliferative competence through participation in energy generation, nucleoside synthesis and maintenance of cellular redox balance. Amino acids also play an important role in immune-suppressive microenvironment formation. Thus, the biological effects of amino acids may change unexpectedly in tumor-specific or oncogene-dependent manners. In recent years, there has been significant progress in the study of amino acid metabolism, particularly in their potential application as therapeutic targets in breast cancer. In this review, we provide an update on amino acid metabolism and discuss the therapeutic implications of amino acids in breast cancer.

Neutrophils in triple-negative breast cancer: an underestimated player with increasingly recognized importance.

Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer, with limited therapeutic options readily available. Immunotherapy such as immune checkpoint inhibition has been investigated in TNBC but still encounters low overall response. Neutrophils, the most abundant leukocytes in the body, are increasingly recognized as an active cancer-modulating entity. In the bloodstream, neutrophils escort circulating tumor cells to promote their survival and stimulate their proliferation and metastasis. In the tumor microenvironment, neutrophils modulate the immune milieu through polarization between the anti-tumor and the pro-tumor phenotypes. Through a comprehensive review of recently published literature, it is evident that neutrophils are an important player in TNBC immunobiology and can be used as an important prognostic marker of TNBC. Particularly, in their pro-tumor form, neutrophils facilitate TNBC metastasis through formation of neutrophil extracellular traps and the pre-metastatic niche. These findings will help advance the potential utilization of neutrophils as a therapeutic target in TNBC.

Lovastatin enhances chemosensitivity of paclitaxel-resistant prostate cancer cells through inhibition of CYP2C8.

Chemotherapy is the mainstay of treatment for prostate cancer, with paclitaxel being commonly used for hormone-resistant prostate cancer. However, drug resistance often develops and leads to treatment failure in a variety of prostate cancer patients. Therefore, it is necessary to enhance the sensitivity of prostate cancer to chemotherapy. Lovastatin (LV) is a natural compound extracted from Monascus-fermented foods and is an inhibitor of HMG-CoA reductase (HMGCR), which has been approved by the FDA for hyperlipidemia treatment. We have previously found that LV could inhibit the proliferation of refractory cancer cells. Up to now, the effect of LV on chemosensitization and the mechanisms involved have not been evaluated in drug-resistant prostate cancer. In this study, we used prostate cancer cell line PC3 and its paclitaxel-resistant counterpart PC3-TxR as the cell model. Alamar Blue cell viability assay showed that LV and paclitaxel each conferred concentration-dependent inhibition of PC3-TxR cells. When paclitaxel was combined with LV, the proliferation of PC3-TxR cells was synergistically inhibited, as demonstrated by combination index <1. Moreover, colony formation decreased while apoptosis increased in paclitaxel plus LV group compared with paclitaxel alone group. Quantitative RT-PCR showed that the combination of paclitaxel and LV could significantly reduce the expression of CYP2C8, an important drug-metabolizing enzyme. Bioinformatics analysis from the TCGA database showed that CYP2C8 expression was negatively correlated with progression-free survival (PFS) in prostate cancer patients. Our results suggest that LV might increase the sensitivity of resistant prostate cancer cells to paclitaxel through inhibition of CYP2C8 and could be utilized as a chemosensitizer for paclitaxel-resistant prostate cancer cells.

Natural products inducing nucleolar stress: implications in cancer therapy.

The nucleolus is the site of ribosome biogenesis and is found to play an important role in stress sensing. For over 100 years, the increase in the size and number of nucleoli has been considered as a marker of aggressive tumors. Despite this, the contribution of the nucleolus and the biologic processes mediated by it to cancer pathogenesis has been largely overlooked. This state has been changed over the recent decades with the demonstration that the nucleolus controls numerous cellular functions associated with cancer development. Induction of nucleolar stress has recently been regarded as being superior to conventional cytotoxic/cytostatic strategy in that it is more selective to neoplastic cells while sparing normal cells. Natural products represent an excellent source of bioactive molecules and some of them have been found to be able to induce nucleolar stress. The demonstration of these nucleolar stress-inducing natural products has paved the way for a new therapeutic approach to more delicate tumor cell-killing. This review provides a contemporary summary of the role of the nucleolus as a novel promising target for cancer therapy, with particular emphasis on natural products as an exciting new class of anti-cancer drugs with nucleolar stress-inducing properties.

Correction of uneven illumination in color microscopic image based on fully convolutional network.

The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images. An effective fully convolutional network (FCN) is proposed to directly process color microscopic image in this paper. The proposed method estimates the distribution of illumination information in input image, and then carry out the correction of the corresponding uneven illumination through a feature encoder module, a feature decoder module, and a detail supplement module. In this process, overlapping residual blocks are designed to better transfer the illumination information, and in particular a well-designed weighted loss function ensures that the network can not only correct the illumination but also preserve image details. The proposed method is compared with some related methods on real pathological cell images qualitatively and quantitatively. Experimental results show that our method achieves the excellent performance. The proposed method is also applied to the preprocessing of whole slide imaging (WSI) tiles, which greatly improves the effect of image mosaicking.

[Clinical phenotype and novel mutation in one of twins with glutaric acidemia type I].

OBJECTIVE: To review the clinical features of a male twin affected with glutaric academia type I (GA-I) and analyze the variations of glutaryl-CoA dehydrogenase (GCDH) gene. METHODS: Clinical data of the pair of twins and their parents were collected. Genomic DNA was extracted from peripheral blood samples, and variants of GCDH genes were detected by capture sequencing using a customized panel. Variants of the twins and their parents were verified by Sanger sequencing. RESULTS: The level of glutaric acyl carnitine (C5DC + C6OH) was 3.26 µmol/L in the male twin. The relative level of glutaric acid in urine was 547.51 by gas chromatography mass spectrometry analysis. Cerebral ultrasonography showed that the patient had subependymal hemorrhage, but no serious clinical manifestation was noted. After treating with special formula milk powder and L-carnitine, the boy showed good growth and development. Two heterozygous variants of the GCDH gene were detected in the patient, among which c.416C>G was suspected to be pathogenic, while c.109_110delCA was unreported. The variants were respectively inherited from his parents. The twin girl only carried the c.416C>G variant. CONCLUSION: GA-I can be diagnosed by mass spectrometry, urine gas chromatographic mass spectrometry, imaging as well as genetic diagnosis. Early diagnosis and intervention is important.

Speckle noise reduction of multi-frame optical coherence tomography data using multi-linear principal component analysis.

Optical coherence tomography (OCT) is an important interferometric diagnostic technique extensively applied in medical sciences. However, OCT images inevitably suffer from speckle noise, which reduces the accuracy of the diagnosis of ocular diseases. To deal with this problem, a speckle noise reduction method based on multi-linear principal component analysis (MPCA) is presented to denoise multi-frame OCT data. To well preserve local image features, nonlocal similar 3D blocks extracted from the data are first grouped using k-means++ clustering method. MPCA transform is then performed on each group and the transform coefficients are shrunk to remove speckle noise. Finally, the filtered OCT volume is obtained by inverse MPCA transform and aggregation. Experimental results show that the proposed method outperforms other compared approaches in terms of both speckle noise reduction and fine detail preservation.

Comparative analysis of gradient-field-based orientation estimation methods and regularized singular-value decomposition for fringe pattern processing.

Fringe orientation is an important feature of fringe patterns and has a wide range of applications such as guiding fringe pattern filtering, phase unwrapping, and abstraction. Estimating fringe orientation is a basic task for subsequent processing of fringe patterns. However, various noise, singular and obscure points, and orientation data degeneration lead to inaccurate calculations of fringe orientation. Thus, to deepen the understanding of orientation estimation and to better guide orientation estimation in fringe pattern processing, some advanced gradient-field-based orientation estimation methods are compared and analyzed. At the same time, following the ideas of smoothing regularization and computing of bigger gradient fields, a regularized singular-value decomposition (RSVD) technique is proposed for fringe orientation estimation. To compare the performance of these gradient-field-based methods, quantitative results and visual effect maps of orientation estimation are given on simulated and real fringe patterns that demonstrate that the RSVD produces the best estimation results at a cost of relatively less time.

Viral Noncoding RNAs in Cancer Biology.

Over 12 % of all human cancers are caused by oncoviruses, primarily including Epstein-Barr virus (EBV), high-risk human papillomaviruses (HPVs), hepatitis B and C viruses (HBV and HCV, respectively), and Kaposi's sarcoma herpesvirus (KSHV). In addition to viral oncoproteins, a variety of noncoding RNAs (ncRNAs) produced by oncoviruses have been recognized as important cofactors that contribute to the oncogenic events. In this chapter, we will focus on the recent understanding of the long and short noncoding RNAs, as well as microRNAs of the viruses, and discuss their roles in the biology of multistep oncogenesis mediated by established human oncoviruses.

Application of a sensitive and specific LC-MS/MS method for determination of eriodictyol-8-C-ß-d-glucopyranoside in rat plasma for a bioavailability study.

This study is the first to detail the development and validation of a rapid, sensitive and specific LC-ESI-MS/MS method for the determination of eriodictyol-8-C-ß-d-glucopyranoside (EG) in rat plasma. A simple protein precipitation method was used for plasma sample preparation. Chromatographic separation was successfully achieved on an Agilent Zorbax XDB C18 column (2.1 × 50 mm, 3.5 µm) using a step gradient program with the mobile phase of 0.1% formic acid aqueous solution and acetonitrile with 0.1% formic acid. EG and the internal standard (IS) were detected using an electrospray negative ionization mass spectrometry in the multiple reaction monitoring mode. This method demonstrated good linearity and did not show any endogenous interference with the active compound and IS peaks. The lower limit of quantification of EG was 0.20 ng/mL in 50 µL rat plasma. The average recoveries of EG and IS from rat plasma were both above 80%. The inter-day precisions (relative standard deviation) of EG determined over 5 days were all within 15%. The present method was successfully applied to a quantification and bioavailability study of EG in rats after intravenous and oral administration. The oral absolute bioavailability of EG in rats was estimated to be 7.71 ± 1.52%.

LC-ESI-MS/MS analysis and pharmacokinetics of plantainoside D isolated from Chirita longgangensis var. hongyao, a potential anti-hypertensive active component in rats.

Plantainoside D (PD) is a potential anti-hypertensive active ingredient newly isolated from the dried plants of Chirita longgangensis var. hongyao. A sensitive and specific LC-ESI-MS/MS method was first developed and validated for the analysis of PD in rat plasma using genistein as the internal standard (IS). The plasma samples were pretreated with methanol-acetonitrile (50:50, v/v) to precipitate protein, and then chromatographed on a reverse-phase Agilent Zorbax XDB C18 column (50 mm × 2.1 mm, 3.5 µm). Gradient elution was utilized, with a mobile phase consisting of water and acetonitrile both containing 0.1% formic acid, and the flow rate was set at 0.50 mL/min. The analytes were monitored by tandem-mass spectrometry with negative electrospray ionization. The precursor/product transitions (m/z) in the negative ion mode were 639.2 → 160.9 Thomson (Th) and 268.9 → 158.9 Thomson (Th) for PD and IS, respectively. Linearity was achieved in the 0.10-200 ng/mL range, with a lower limit of quantification of 0.10 ng/mL. The precision and accuracy for both intra- and inter-day determination of the analyte were all within ±15%. The present method has been applied for pharmacokinetic study of PD after oral and intravenous administration in rats. The oral absolute bioavailability (F) of PD in rats was estimated to be 1.12% ± 0.46% with an elimination half-life (t1/2) value of 1.63 ± 0.19 h, suggesting its poor absorption and/or strong metabolism in vivo.

Identification of pork in meat products using real-time loop-mediated isothermal amplification.

In this study, a one-step, real-time, loop-mediated isothermal amplification (RealAmp) assay was developed, for the highly specific detection of pork DNA. For the assay, the mtDNA of cytochrome b (cytb) gene was amplified at 63 °C using SYBR Green I for 45 min with a Real-Time Polymerase Chain Reaction (PCR) System that measured the fluorescent signal at one-minute intervals. As little as 1 pg of template DNA could be detected, without any cross-reactivity with non-target species. Meat mixtures, heat-treated at 100 °C for 15 min, prepared by mixing pork meat with beef at different ratios (0.01%-10%) were tested, and the RealAmp assays allowed the detection of as little as 0.01% pork in the meat mixtures. Thus, this work showed that RealAmp could be used for specific identification and sensitive quantification of meat species, even for heat-treated meat products.

A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation.

Automatic liver tumor segmentation is one of the most important tasks in computer-aided diagnosis and treatment. Deep learning techniques have gained increasing popularity for medical image segmentation in recent years. However, due to the various shapes, sizes, and obscure boundaries of tumors, it is still difficult to automatically extract tumor regions from CT images. Based on the complementarity of edge detection and region segmentation, a three-path structure with multi-scale selective feature fusion (MSFF) module, multi-channel feature fusion (MFF) module, edge-inspiring (EI) module, and edge-guiding (EG) module is proposed in this paper. The MSFF module includes the process of generation, fusion, and selection of multi-scale features, which can adaptively correct the response weights in multiple branches to filter redundant information. The MFF module integrates richer hierarchical features to capture targets at different scales. The EI module aggregates high-level semantic information at different levels to obtain fine edge semantics, which is injected into the EG module for representation learning of segmentation features. Experiments on the LiTs2017 dataset show that our proposed method achieves a Dice index of 85.55% and a Jaccard index of 81.11%, which are higher than what can be obtained by the current state-of-the-art methods. Cross-dataset validation experiments conducted on 3Dircadb and Clinical datasets show the generalization and robustness of the proposed method by achieving dice indices of 80.14% and 81.68%, respectively.

Cardinality-constrained plan-quality and delivery-time optimization method for proton therapy.

BACKGROUND: While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality. PURPOSE: This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff. METHODS: A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l1,0-norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation. RESULTS: The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively. CONCLUSIONS: We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff.

Disulfidptosis: Six Riddles Necessitating Solutions.

Disulfidptosis occurs as a result of the accumulation of intracellular cystine followed by disulfide stress in actin cytoskeleton proteins due to a reduction of NADPH produced through the pentose phosphate pathway in cells with high expression of SLC7A11. It is a cell death caused by the redox imbalance resulting from the disruption of amino acid metabolism and glucose metabolism. The discovery of disulfidptosis has sparked immense enthusiasm, but there are numerous unresolved issues that need to be addressed. Solutions to these riddles will provide insights into the detailed mechanisms and the pathophysiological relevance of disulfidptosis and utilizing disulfidptosis as an actionable therapeutic target.

Neutrophil Extracellular Traps in Breast Cancer: Roles in Metastasis and Beyond.

Despite advances in the treatment of breast cancer, the disease continues to exhibit high global morbidity and mortality. The importance of neutrophils in cancer development has been increasingly recognized. Neutrophil extracellular traps (NETs) are web-like structures released into the extracellular space by activated neutrophils, serving as a potential antimicrobial mechanism for capturing and eliminating microorganisms. The roles played by NETs in cancer development have been a subject of intense research in the last decade. In breast cancer, current evidence suggests that NETs are involved in various stages of cancer development, particularly during metastasis. In this review, we try to provide an updated overview of the roles played by NETs in breast cancer metastasis. These include: 1) facilitating systemic dissemination of cancer cells; 2) promoting cancer-associated inflammation; 3) facilitating cancer-associated thrombosis; 4) facilitating pre-metastatic niche formation; and 5) awakening dormant cancer cells. The translational implications of NETs in breast cancer treatment are also discussed. Understanding the relationship between NETs and breast cancer metastasis is expected to provide important insights for developing new therapeutic strategies for breast cancer patients.

Dysregulated Ribosome Biogenesis Is a Targetable Vulnerability in Triple-Negative Breast Cancer: MRPS27 as a Key Mediator of the Stemness-inhibitory Effect of Lovastatin.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with limited effective therapeutic options readily available. We have previously demonstrated that lovastatin, an FDA-approved lipid-lowering drug, selectively inhibits the stemness properties of TNBC. However, the intracellular targets of lovastatin in TNBC remain largely unknown. Here, we unexpectedly uncovered ribosome biogenesis as the predominant pathway targeted by lovastatin in TNBC. Lovastatin induced the translocation of ribosome biogenesis-related proteins including nucleophosmin (NPM), nucleolar and coiled-body phosphoprotein 1 (NOLC1), and the ribosomal protein RPL3. Lovastatin also suppressed the transcript levels of rRNAs and increased the nuclear protein level and transcriptional activity of p53, a master mediator of nucleolar stress. A prognostic model generated from 10 ribosome biogenesis-related genes showed outstanding performance in predicting the survival of TNBC patients. Mitochondrial ribosomal protein S27 (MRPS27), the top-ranked risky model gene, was highly expressed and correlated with tumor stage and lymph node involvement in TNBC. Mechanistically, MRPS27 knockdown inhibited the stemness properties and the malignant phenotypes of TNBC. Overexpression of MRPS27 attenuated the stemness-inhibitory effect of lovastatin in TNBC cells. Our findings reveal that dysregulated ribosome biogenesis is a targetable vulnerability and targeting MRPS27 could be a novel therapeutic strategy for TNBC patients.

Quantification and pharmacokinetics of Taiwanin E methyl ether in rats by liquid chromatography-tandem mass spectrometry.

This study firstly describes the development of an accurate and sensitive high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for the quantification of Taiwanin E methyl ether (TEME) in rat plasma. The assay involved a simple liquid-liquid extraction step with ethyl acetate and a gradient elution using a mobile phase consisting of water containing 0.1% formic acid and acetonitrile containing 0.1% formic acid. Chromatographic separation was successfully achieved on an Agilent Zorbax-C(18) column (2.1 × 50 mm, 3.5 µm) with a flow rate of 0.40 mL/min. The multiple reaction monitoring was based on the transitions of m/z = 379.1 → 320.1 for TEME and 386.1 → 122.0 for buspirone (internal standard). The assay was validated to demonstrate the specificity, linearity, recovery, accuracy, precision and stability. The lower limit of quantification was 0.50 ng/mL in 50 µL of rat plasma. The developed and validated method was successfully applied to the quantification and pharmacokinetic study of TEME in rats after intravenous and oral administration of 1.45 mg/kg TEME. The oral absolute bioavailability of TEME was estimated to be 5.85 ± 1.41% with an elimination half-life value of 2.61 ± 0.55 h, suggesting its poor absorption and/or strong metabolism in vivo.

Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.

Manually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is urgently needed in clinical diagnosis. However, automatic segmentation methods face certain challenges due to heterogeneity, fuzzy boundaries, and irregularity of tumor tissue. In this paper, a novel deep learning-based approach with multi-scale-aware (MSA) module and twin-split attention (TSA) module is proposed for tumor segmentation. The MSA module can bridge the semantic gap and reduce the loss of detailed information. The TSA module can recalibrate the channel response of the feature map. Eventually, we can count tumors based on the segmentation results from a 3D perspective for cancer grading. Extensive experiments conducted on the LiTS2017 dataset show the effectiveness of the proposed method by achieving a Dice index of 85.97% and a Jaccard index of 81.56% over the state of the art. In addition, the proposed method also achieved a Dice index of 83.67% and a Jaccard index of 80.11% in 3Dircadb dataset verification, which further reflects its robustness and generalization ability.

RFPNet: Reorganizing feature pyramid networks for medical image segmentation.

Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.