A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

OBJECTIVES: Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS: Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance. RESULTS: On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively. CONCLUSIONS: Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.

Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study.

BACKGROUND: The interaction of dysbiosis of gut microbiota (GM) with diabetic nephropathy (DN) drew our attention and a better understanding of GM on DN might provide potential therapeutic approaches. However, the exact causal effect of GM on DN remains unknown. METHODS: We applied two-sample Mendelian Randomization (MR) analysis, including inverse variance weighted (IVW), MR-Egger methods, etc., to screen the significant bacterial taxa based on the GWAS data. Sensitivity analysis was conducted to assess the robustness of MR results. To identify the most critical factor on DN, Mendelian randomization-Bayesian model averaging (MR-BMA) method was utilized. Then, whether the reverse causality existed was verified by reverse MR analysis. Finally, transcriptome MR analysis was performed to investigate the possible mechanism of GM on DN. RESULTS: At locus-wide significance levels, the results of IVW suggested that order Bacteroidales (odds ratio (OR) = 1.412, 95% confidence interval (CI): 1.025-1.945, P = 0.035), genus Akkermansia (OR = 1.449, 95% CI: 1.120-1.875, P = 0.005), genus Coprococcus 1 (OR = 1.328, 95% CI: 1.066-1.793, P = 0.015), genus Marvinbryantia (OR = 1.353, 95% CI: 1.037-1.777, P = 0.030) and genus Parasutterella (OR = 1.276, 95% CI: 1.022-1.593, P = 0.032) were risk factors for DN. Reversely, genus Eubacterium ventriosum (OR = 0.756, 95% CI: 0.594-0.963, P = 0.023), genus Ruminococcus gauvreauii (OR = 0.663, 95% CI: 0.506-0.870, P = 0.003) and genus Erysipelotrichaceae (UCG003) (OR = 0.801, 95% CI: 0.644-0.997, P = 0.047) were negatively associated with the risk of DN. Among these taxa, genus Ruminococcus gauvreauii played a crucial role in DN. No significant heterogeneity or pleiotropy in the MR result was found. Mapped genes (FDR < 0.05) related to GM had causal effects on DN, while FCGR2B and VNN2 might be potential therapeutic targets. CONCLUSIONS: This work provided new evidence for the causal effect of GM on DN occurrence and potential biomarkers for DN. The significant bacterial taxa in our study provided new insights for the 'gut-kidney' axis, as well as unconventional prevention and treatment strategies for DN.

Two novel angiotensin-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibiting peptides from tilapia (Oreochromis mossambicus) skin and their molecular docking mechanism.

In the study, papain was used to hydrolyze tilapia (Oreochromis mossambicus) skin to obtain a tilapia skin hydrolysate (TSH) with dual angiotensin-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibitory activities. The resulting TSH was sequentially fractionated by ultrafiltration, size exclusion separation chromatography, and reverse-phase high-performance liquid chromatography. Its inhibitory effects on ACE and DPP-IV were determined by commercial reagent kits. Two peptides purified from TSH were identified as Gly-Pro-Leu-Gly-Ala-Leu (GPLGAL) and Lys-Pro-Ala-Gly-Asn (KPAGN) by the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Inhibitory concentration (IC50) of GPLGAL on ACE and DPP-IV were 117.20 ± 1.69 and 187.10 ± 2.75 µM, respectively. IC50 of KPAGN on ACE and DPP-IV were 137.40 ± 2.33 and 259.20 ± 2.85 µM, respectively. The molecular simulation demonstrated that the binding affinities of GPLGAL to ACE and DPP-IV proteins were -8.5 and -7.4 kcal/mol, respectively, whereas those of KPAGN to ACE and DPP-IV proteins were -7.9 and -6.7 kcal/mol, respectively. GPLGAL interacted with 21 amino acid residues of the ACE active site, whereas KPAGN engaged with 19 amino acid residues. Additionally, GPLGAL interacted with 10 amino acid residues of the DPP-IV active site, whereas KPAGN engaged with 13 amino acid residues. The two peptides predominantly occupied the active sites of ACE (His513, Tyr523, and Ala354) and DPP-IV (Tyr662 and Arg125) through hydrogen bonding. This leads to the deactivation of ACE and DPP-IV. PRACTICAL APPLICATION: Accelerate tilapia skin development and high-value utilization; provide foundation for preparing the peptides with dual ACE and DPP-IV inhibiting activity.

A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery Using Deep Learning: A Comparison with Senior Sonographers.

BACKGROUND AND OBJECTIVE: The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability around the globe. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system. METHODS: We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers. RESULTS: Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively]. CONCLUSION: Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

Anlotinib enhanced CD8+ T cell infiltration via induction of CCL5 improves the efficacy of PD-1/PD-L1 blockade therapy in lung cancer.

Non-small cell lung cancer (NSCLC) is a leading cause of mortality worldwide and requires effective treatment strategies. Recently, the development of a novel multiple-target tyrosine kinase inhibitor, anlotinib, has drawn increasing attention, especially it shows advantages when combined with PD-1/PD-L1 blockade. However, the mechanism by which anlotinib improves immunotherapy and remodeling of the tumor microenvironment remains unclear. In this study, we found that anlotinib combined with PD-1 blockade significantly inhibited tumor growth and reduced tumor weight in a lung cancer xenograft model compared to any single treatment. Both immunofluorescence and flow cytometry analyses revealed that anlotinib induced a CD8+ T cell dominated tumor microenvironment, which might account for its improved role in immunotherapy. Further investigations showed that CCL5-mediated CD8+ T cell recruitment plays a critical role in anlotinib and PD-1 blockade strategies. The depletion of CD8+ T cells abrogated this process. In conclusion, our findings showed that the combination of anlotinib and PD-1 blockade produced promising effects in the treatment of lung cancer, and that the induction of CCL5-mediced CD8+ T cell recruitment by anlotinib provided a novel mechanism of action.

The impact of volunteer service on moral education performance and mental health of college students.

BACKGROUND: Moral education in colleges and universities is an important part of the talent training system, including moral education curriculum, moral education practice, mental health education. Volunteer service is a public welfare act in which volunteers volunteer their time, knowledge, property, technology, with the ultimate goal of helping others and serving the society without personal compensation. As an innovative form of moral education practice in colleges and universities, college students' voluntary service is of great significance in promoting the reform and innovation of moral education, enhancing the affinity, appeal and influence of moral education, and building a positive psychology for college students. SUBJECTS AND METHODS: As an effective carrier of moral education practice in colleges and universities, voluntary service is helpful to enhance the effectiveness of moral education practice and construct the positive psychology of college students. This project is based on the actual situation of college students participating in volunteer services, and collected the volunteer services of 4545 college students in Zhejiang Province. Through model construction and data modeling, the correlation between college students' participation in volunteer service and their moral education performance and mental health was analyzed, and the basic path and guarantee measures to promote the role of volunteer service in moral education and positive psychological construction were deeply explored. RESULTS: From the correlation analysis of students' voluntary service participation, moral education performance and voluntary service motivation, students' attributes are determined according to their voluntary service participation, so as to predict their moral education performance and mental health level. CONCLUSION: College students' voluntary service is partially positively related to their moral education performance and mental health. In order to improve students' moral education performance and mental health, we can optimize the participation frequency, participation duration, participation ways and type structure of voluntary service, constantly increase the participation frequency of voluntary service, increase the duration of voluntary service, broaden the participation ways of voluntary activities, and enrich the types of voluntary service activities.

Elucidating the changes in the heterogeneity and function of radiation-induced cardiac macrophages using single-cell RNA sequencing.

Purpose: A mouse model of irradiation (IR)-induced heart injury was established to investigate the early changes in cardiac function after radiation and the role of cardiac macrophages in this process. Methods: Cardiac function was evaluated by heart-to-tibia ratio, lung-to-heart ratio and echocardiography. Immunofluorescence staining and flow cytometry analysis were used to evaluate the changes of macrophages in the heart. Immune cells from heart tissues were sorted by magnetic beads for single-cell RNA sequencing, and the subsets of macrophages were identified and analyzed. Trajectory analysis was used to explore the differentiation relationship of each macrophage subset. The differentially expressed genes (DEGs) were compared, and the related enriched pathways were identified. Single-cell regulatory network inference and clustering (SCENIC) analysis was performed to identify the potential transcription factors (TFs) which participated in this process. Results: Cardiac function temporarily decreased on Day 7 and returned to normal level on Day 35, accompanied by macrophages decreased and increased respectively. Then, we identified 7 clusters of macrophages by single-cell RNA sequencing and found two kinds of stage specific macrophages: senescence-associated macrophage (Cdkn1ahighC5ar1high) on Day 7 and interferon-associated macrophage (Ccr2highIsg15high) on Day 35. Moreover, we observed cardiac macrophages polarized over these two-time points based on M1/M2 and CCR2/major histocompatibility complex II (MHCII) expression. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses suggested that macrophages on Day 7 were characterized by an inflammatory senescent phenotype with enhanced chemotaxis and inflammatory factors, while macrophages on Day 35 showed enhanced phagocytosis with reduced inflammation, which was associated with interferon-related pathways. SCENIC analysis showed AP-1 family members were associated with IR-induced macrophages changes. Conclusion: We are the first study to characterize the diversity, features, and evolution of macrophages during the early stages in an IR-induced cardiac injury animal model.

Aromatic Amino Acids Promote Lipid Metabolism Disorders by Increasing Hepatic Bile Acid Synthesis.

BACKGROUND: Obesity is a progressive metabolic disease that begins with lipid metabolism disorders. Aromatic amino acids (AAAs), including tryptophan, phenylalanine, and tyrosine, have diverse biological activities as nutrients. However, the underlying mechanisms by which AAAs affect lipid metabolism are unclear. OBJECTIVES: This study was designed to investigate the possible roles and underlying molecular mechanisms of AAA in the pathogenesis of lipid metabolism disorders. METHODS: We added an AAA mixture to the high-fat diet (HFD) of mice. Glucose tolerance test was recorded. Protein expression of hepatic bile acid (BA) synthase and mRNA expression of BA metabolism-related genes were determined. Hepatic BA profiles and gut microbial were also determined in mice. RESULTS: The results showed that AAA significantly increased body weight and white adipose tissue, aggravated liver injury, impaired glucose tolerance and intestinal integrity, and significantly increased hepatic BA synthesis by inhibiting intestinal farnesoid X receptor (FXR). Moreover, AAA increased the content of total BA in the liver and altered the hepatic BA profile, with elevated levels of lithocholic acid, glycochenodeoxycholic acid, and glycoursodeoxycholic acid. AAA markedly increased the levels of proteins involved in BA synthesis (cholesterol 7α-hydroxylase and oxysterol 7α-hydroxylase) and inhibited the intestinal FXR. Gut microbial composition also changed, reducing the abundance of some beneficial bacteria, such as Parvibacter and Lactobacillus. CONCLUSIONS: Under HFD conditions, AAAs stimulate BA synthesis in both the classical and alternative pathways, leading to aggravation of liver injury and fat deposition. Excessive intake of AAA disrupts BA metabolism and contributes to the development of lipid metabolism disorders, suggesting that AAA may be a causative agent of lipid metabolism disorders.

Excessive Tryptophan and Phenylalanine Induced Pancreatic Injury and Glycometabolism Disorder in Grower-finisher Pigs.

BACKGROUND: The increase in circulating insulin levels is associated with the onset of type 2 diabetes (T2D), and the levels of branched-chain amino acids and aromatic amino acids (AAAs) are altered in T2D, but whether AAAs play a role in insulin secretion and signaling remains unclear. OBJECTIVES: This study aimed to investigate the effects of different AAAs on pancreatic function and on the use of insulin in finishing pigs. METHODS: A total of 18 healthy finishing pigs (Large White) with average body weight of 100 ± 1.15 kg were randomly allocated to 3 dietary treatments: Con, a normal diet supplemented with 0.68% alanine; Phe, a normal diet supplemented with 1.26% phenylalanine; and Trp, a normal diet supplemented with 0.78% tryptophan. The 3 diets were isonitrogenous. There were 6 replicates in each group. RESULTS: Herein, we investigated the effects of tryptophan and phenylalanine on pancreatic function and the use of insulin in finishing pigs and found that the addition of tryptophan and phenylalanine aggravated pancreatic fat deposition, increased the relative content of saturated fatty acids, especially palmitate (C16:0) and stearate (C18:0), and the resulting lipid toxicity disrupted pancreatic secretory function. We also found that tryptophan and phenylalanine inhibited the growth and secretion of ß-cells, downregulated the gene expression of the PI3K/Akt pathway in the pancreas and liver, and reduced glucose utilization in the liver. CONCLUSIONS: Using fattening pigs as a model, multiorgan combined analysis of the insulin-secreting organ pancreas and the main insulin-acting organ liver, excessive intake of tryptophan and phenylalanine will aggravate pancreatic damage leading to glucose metabolism disorders, providing new evidence for the occurrence and development of T2D.

Extracellular Vesicles: A New Star for Gene Drug Delivery.

Recently, gene therapy has become a subject of considerable research and has been widely evaluated in various disease models. Though it is considered as a stand-alone agent for COVID-19 vaccination, gene therapy is still suffering from the following drawbacks during its translation from the bench to the bedside: the high sensitivity of exogenous nucleic acids to enzymatic degradation; the severe side effects induced either by exogenous nucleic acids or components in the formulation; and the difficulty to cross the barriers before reaching the therapeutic target. Therefore, for the successful application of gene therapy, a safe and reliable transport vector is urgently needed. Extracellular vesicles (EVs) are the ideal candidate for the delivery of gene drugs owing to their low immunogenicity, good biocompatibility and low toxicity. To better understand the properties of EVs and their advantages as gene drug delivery vehicles, this review covers from the origin of EVs to the methods of EVs generation, as well as the common methods of isolation and purification in research, with their pros and cons discussed. Meanwhile, the engineering of EVs for gene drugs is also highlighted. In addition, this paper also presents the progress in the EVs-mediated delivery of microRNAs, small interfering RNAs, messenger RNAs, plasmids, and antisense oligonucleotides. We believe this review will provide a theoretical basis for the development of gene drugs.

Genetic Analyses of Flower Main Traits from Two Pitayas and Their Progenies: A Cactus Plant.

Elucidation of the genetic foundation governing crucial traits in pitaya flowers is imperative for enhancing both the ornamental and economic values. In this study, the dynamic variation in flower genetics, segregation variation patterns, and a mixed inheritance model of the major and multigene flower traits of 'Dahong' and 'Honghuaqinglong' pitayas and their progenies were explored. The results showed that the main traits of flowers exhibited varying degrees of variation among the reciprocal F1 hybrids, with the data exhibiting the characteristics of quantitative traits. The betalain content, petal number, and stigma number exhibited values below the median values of the parents, suggesting a genetic inclination towards lower values. Perianth width, calyx tube width, petal number, and stigma number had the same genetic effects and significant correlation. Stigma-related traits had a clear maternal inheritance tendency. The heritability of flower length, stigma relative to anther distance, and petal betalain content was governed by two pairs of additive-dominant major genes. Perianth width, calyx tube width, petal number, and stigma number all conformed to the model of two pairs of equal-additive-dominant major genes. This study provides valuable information for parental selection, cross-breeding, and the enhancement of pitaya varieties to meet market preferences and environmental conditions.

Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data.

Accurate fine-mode and coarse-mode aerosol knowledge is crucial for understanding their impacts on the climate and Earth's ecosystems. However, current satellite-based Fine-Mode Aerosol Optical Depth (FAOD) and Coarse-Mode Aerosol Optical Depth (CAOD) methods have drawbacks including inaccuracies, low spatial coverage, and limited temporal duration. To overcome these issues, we developed new global-scale FAOD and CAOD from 2005 to 2020 using a novel deep learning model capable of the synergistic retrieval of two aerosol sizes. After validation with the aerosol robotic network (AERONET) and sky radiometer network (SKYNET), the new monthly FAOD and CAOD showed significant improvements in accuracy and spatial coverage. From 2005 to 2020, the new data showed that China had the greatest decrease in FAOD and CAOD. In contrast, India and South Latin America had a significant increase in FAOD versus North Africa in CAOD. FAOD in the regions of Argentina, Paraguay, and Uruguay in South America have shown an upward trend. The results reveal that FAOD and CAOD display distinct patterns of change in the same regions, particularly on the west coast of the United States where FAOD is increasing, while CAOD is decreasing. Aside from the year 2020 due to the global COVID-19 pandemic, the analysis showed that although China has seen at least an +85% increase in energy consumption and urban expansion in 2019 compared to 2005 due to the needs of development and construction, the implementation of China's air pollution control policies has led to a significant decrease in FAOD (-46%) and CAOD (-65%) after 2013. This research enriches our comprehension of global fine and coarse aerosol patterns, additional investigations are needed to determine the potential global implications of these changes.

Co-delivery of siBcl-2 and PTX with mitochondria-targeted functions to overcoming multidrug resistance.

Multidrug resistance (MDR) poses a significant impediment to the efficacy of chemotherapy in clinical settings. Despite Paclitaxel (PTX) being designated as the primary pharmaceutical agent for treating recurrent and metastatic breast cancer, the emergence of PTX resistance frequently results in therapeutic shortcomings, representing a substantial obstacle in clinical breast cancer management. In response, we developed a delivery system exhibiting dual specificity for both tumors and mitochondria. This system facilitated the sequential administration of small interfering B-cell lymphoma-2 (siBcl-2) and PTX to the tumor cytoplasm and mitochondria, respectively, with the aim of surmounting PTX resistance in tumor cells through the activation of the mitochondrial apoptosis pathway. Notably, we employed genetic engineering techniques to fabricate a recombinant ferritin containing the H-subunit (HFn), known for its tumor-targeting capabilities, for loading siBcl-2. This HFn-siBcl-2 complex was then combined with positively charged Triphenylphosphine-Liposome@PTX (TL@PTX) nanoparticles (NPs) to formulate HFn/siBcl-2@TL/PTX. Guided by HFn, these nanoparticles efficiently entered cells and released siBcl-2 through the action of triphenylphosphine (TPP)-mediated "proton sponge," thereby precisely modulating the expression of Bcl-2 protein. Simultaneously, PTX was directed to the mitochondria through the accurate targeting of TL@PTX, synergistically initiating the mitochondrial apoptosis pathway and effectively suppressing PTX resistance both in vitro and in vivo. In conclusion, the development of this dual-targeting delivery system presents a promising therapeutic strategy for overcoming PTX resistance in the clinical treatment of breast cancer.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol-1, significantly outperforming competitive models.

Inhibition of inflammation by berberine: Molecular mechanism and network pharmacology analysis.

BACKGROUND: Traditional Chinese Medicine (TCM), renowned for its holistic approach with a 2000-year history of utilizing natural remedies, offers unique advantages in disease prevention and treatment. Berberine, found in various Chinese herbs, has been employed for many years, primarily for addressing conditions such as diarrhea and dysentery. Berberine has recently become a research focus owing to its pharmacological activities and benefits to human bodies. However, little is known about the anti-inflammatory mechanism of berberine. PURPOSE: To summarize recent findings regarding the pharmacological effects and mechanisms of berberine anti-inflammation and highlight and predict the potential therapeutic effects and systematic mechanism of berberine. METHODS: Recent studies (2013-2023) on the pharmacological effects and mechanisms of berberine anti-inflammation were retrieved from Web of Science, PubMed, Google Scholar, and Scopus up to July 2023 using relevant keywords. Network pharmacology and bioinformatics analysis were employed to predict the therapeutic effects and mechanisms of berberine against potential diseases. RESULTS: The related pharmacological mechanisms of berberine anti-inflammation include the inhibition of inflammatory cytokine production (e.g., IL-1ß, IL-6, TNF-α), thereby attenuating the inflammatory response; Inhibiting the activation of NF-κB signaling pathway and IκBα degradation; Inhibiting the activation of MAPK signaling pathway; Enhancing the activation of the STAT1 signaling pathway; Berberine interacts directly with cell membranes through a variety of pathways, thereby influencing cellular physiological activities. Berberine enhances human immunity and modulates immune system function, which is integral to addressing certain autoimmune and tumour-related health concerns. CONCLUSION: This study expounds on the correlation between berberine and inflammatory diseases, encapsulating the mechanisms through which berberine treats select typical inflammatory ailments. Furthermore, it delves into a deeper understanding of berberine's effectiveness by integrating network pharmacology and molecular docking techniques in the context of treating inflammatory diseases. It provides guidance and reference for berberine's subsequent revelation of the modern scientific connotation of Chinese medicine.

HMGA2 alleviates ferroptosis by promoting GPX4 expression in pancreatic cancer cells.

Pancreatic cancer is one of the most malignant tumor types and is characterized by high metastasis ability and a low survival rate. As a chromatin-binding protein, HMGA2 is widely overexpressed and considered an oncogene with various undefined regulatory mechanisms. Herein, we demonstrated that HMGA2 is highly expressed in pancreatic cancer tissues, mainly distributed in epithelial cells, and represents a subtype of high epithelial-mesenchymal transition. Deletion of HMGA2 inhibits tumor malignancy through cell proliferation, metastasis, and xenograft tumor growth in vivo. Moreover, HMGA2 enhanced the cellular redox status by inhibiting reactive oxygen species and promoting glutathione production. Importantly, ferroptotic cell death was significantly ameliorated in cells overexpressing HMGA2. Conversely, HMGA2 deletion exacerbated ferroptosis. Mechanistically, HMGA2 activated GPX4 expression through transcriptional and translational regulation. HMGA2 binds and promotes cis-element modification in the promoter region of the GPX4 gene by enhancing enhancer activity through increased H3K4 methylation and H3K27 acetylation. Furthermore, HMGA2 stimulated GPX4 protein synthesis via the mTORC1-4EBP1 and -S6K signaling axes. The overexpression of HMGA2 alleviated the decrease in GPX4 protein levels resulting from the pharmacologic inhibition of mTORC1. Conversely, compared with the control, HMGA2 deletion more strongly reduced the phosphorylation of 4EBP1 and S6K. A strong positive correlation between HMGA2 and GPX4 expression was confirmed using immunohistochemical staining. We also demonstrated that HMGA2 mitigated the sensitivity of cancer cells to combination treatment with a ferroptosis inducer and mTORC1 inhibition or gemcitabine. In summary, our results revealed a regulatory mechanism by which HMGA2 coordinates GPX4 expression and underscores the potential value of targeting HMGA2 in cancer treatment.

Proton Therapy in Breast Cancer: A Review of Potential Approaches for Patient Selection.

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

Design, synthesis, antibacterial evaluation of isopropylamine linked with different substituted phenol and piperazine novel derivatives.

BACKGROUND: Xanthomonas oryzae pv. oryzae (Xoo) is often considered one of the most destructive bacterial pathogens causing bacterial leaf blight (BLB), resulting in significant yield and cost losses in rice. In this study, a series of novel derivatives containing the isopropanolamine moiety linked to various substituted phenols and piperazines were designed, synthesized and screened. RESULTS: Antibacterial activity results showed that most compounds had good inhibitory effects on Xoo, among which compound W2 (EC50 = 2.74 µg mL-1 ) exhibited the most excellent inhibitory activity, and W2 also had a certain curative effect (35.89%) on rice compared to thiodiazole copper (TC) (21.57%). Scanning electron microscopy (SEM) results indicated that compound W2 could cause rupture of the Xoo cell membrane. Subsequently, proteomics and quantitative real-time polymerase chain reaction revealed that compound W2 affected the physiological processes of Xoo and may exert antibacterial activity by targeting the two-component system pathway. Interestingly, W2 upregulated Xoo's methyltransferase to impact on its pathogenicity. CONCLUSION: The present study offers a promising phenolic-piperazine-sopropanolamine compound as an innovative antibacterial strategy by specifically targeting the two-component system pathway and inducing upregulation of methyltransferase to effectively impact Xoo's pathogenicity. © 2024 Society of Chemical Industry.

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors.

Background: We aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors. Materials and methods: Clinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from 18F-FDG PET and enhanced plan CT images. Four machine learning algorithms, including SVM (Support Vector Machine), Random Forest (RF), and eXtreme Gradient Boosting (XGB) and logistic regression (LR), were applied using k-fold cross-validation to predict pCR after nCRT. The predictive ability of the models was assessed using receiver operating characteristics (ROC) curve analysis. Results: A total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of 18 F-FDG PET, CT and clinical features with SVM machine learning had the highest AUC of 0.852 in the test set when compared to single-modality models in various algorithms. The other combined models had AUC ranged 0.716 to 0.775. Conclusion: Machine learning models utilizing radiomics features from 18F-FDG PET and enhanced plan CT exhibit promising performance in predicting pCR in ESCC after nCRT and anti-PD-1 inhibitors. The fusion of features from multiple modalities radiomics and clinical features enhances the better predictive performance compared to using a single modality alone.

A nomogram based on peripheral lymphocyte for predicting 8-year survival in patients with prostate cancer: a single-center study using LASSO-cox regression.

PURPOSE: The purpose of this study was to develop a functional clinical nomogram for predicting 8-year overall survival (OS) of patients with prostate cancer (PCa) primary based on peripheral lymphocyte. PATIENTS AND METHODS: Using data from a single-institutional registry of 94 patients with PCa in China, this study identified and integrated significant prognostic factors for survival to build a nomogram. The discriminative ability was measured by concordance index (C-index) and ROC curves (Receiver Operating Characteristic Curves). And the predictive accuracy was measured by the calibration curves. Decision curve analyses (DCA) was used to measure the clinical usefulness. RESULTS: A total of 94 patients were included for analysis. Five independent prognostic factors were identified by LASSO-Cox regression and incorporated into the nomogram: age, the T stage, the absolute counts of peripheral CD3(+)CD4(+) T lymphocytes, CD3(-)CD16(+)CD56(+) NK cells and CD4(+)/CD8(+) ratio. The area under the curve (AUC) values of the predictive model for 5-, 8-, and 10-year overall survival were 0.81, 0.76, and 0.73, respectively. The calibration curves for probability of 5-,8- and 10-year OS showed optimal agreement between nomogram prediction and actual observation. The stratification into different risk groups allowed significant distinction. DCA indicated the good clinical application value of the model. CONCLUSION: We developed a novel nomogram that enables personalized prediction of OS for patients diagnosed with PCa. This finding revealed a relative in age and survival rate in PCa, and a more favorable prognosis in patients exhibiting higher levels of CD4 + T, CD4+/CD8 + ratio and CD3(-)CD16(+)CD56(+) NK cells specifically. This clinically applicable prognostic model exhibits promising predictive capabilities, offering valuable support to clinicians in informed decision-making process.