Two-component system RstAB promotes the pathogenicity of adherent-invasive Escherichia coli in response to acidic conditions within macrophages.

Adherent-invasive Escherichia coli (AIEC) strain LF82, isolated from patients with Crohn's disease, invades gut epithelial cells, and replicates in macrophages contributing to chronic inflammation. In this study, we found that RstAB contributing to the colonization of LF82 in a mouse model of chronic colitis by promoting bacterial replication in macrophages. By comparing the transcriptomes of rstAB mutant- and wild-type when infected macrophages, 83 significant differentially expressed genes in LF82 were identified. And we identified two possible RstA target genes (csgD and asr) among the differentially expressed genes. The electrophoretic mobility shift assay and quantitative real-time PCR confirmed that RstA binds to the promoters of csgD and asr and activates their expression. csgD deletion attenuated LF82 intracellular biofilm formation, and asr deletion reduced acid tolerance compared with the wild-type. Acidic pH was shown by quantitative real-time PCR to be the signal sensed by RstAB to activate the expression of csgD and asr. We uncovered a signal transduction pathway whereby LF82, in response to the acidic environment within macrophages, activates transcription of the csgD to promote biofilm formation, and activates transcription of the asr to promote acid tolerance, promoting its replication within macrophages and colonization of the intestine. This finding deepens our understanding of the LF82 replication regulation mechanism in macrophages and offers new perspectives for further studies on AIEC virulence mechanisms.

Exploring the effects of Hippo signaling pathway on rumen epithelial proliferation.

BACKGROUND: The current understanding to the mechanism of rumen development is limited. We hypothesized that the Hippo signaling pathway controlled the proliferation of rumen epithelium (RE) during postnatal development. In the present study, we firstly tested the changes of the Hippo signaling pathway in the RE during an early growing period from d5 to d25, and then we expanded the time range to the whole preweaning period (d10-38) and one week post weaning (d45). An in vitro experiment was also carried out to verify the function of Hippo signaling pathway during RE cell proliferation. RESULTS: In the RE of lambs from d5 to d25, the expression of baculoviral IAP repeat containing (BIRC3/5) was increased, while the expressions of large tumor suppressor kinase 2 (LATS2), TEA domain transcription factor 3 (TEAD3), axin 1 (AXIN1), and MYC proto-oncogene (MYC) were decreased with rumen growth. From d10 to d38, the RE expressions of BIRC3/5 were increased, while the expressions of LATS2 and MYC were decreased, which were similar with the changes in RE from d5 to d25. From d38 to d45, different changes were observed, with the expressions of LATS1/2, MOB kinase activator 1B (MOB1B), and TEAD1 increased, while the expressions of MST1 and BIRC5 decreased. Correlation analysis showed that during the preweaning period, the RE expressions of BIRC3/5 were positively correlated with rumen development variables, while LAST2 was negatively correlated with rumen development variables. The in vitro experiment validated the changes of LATS2 and BIRC3/5 in the proliferating RE cells, which supported their roles in RE proliferation during preweaning period. CONCLUSIONS: Our results suggest that the LATS2-YAP1-BIRC3/5 axis participates in the RE cell proliferation and promotes rumen growth during the preweaning period.

Efficient Catalyst-Free One-Pot Synthesis of Polysaccharide-Polypeptide Hydrogels in Aqueous Solution.

The preparation of polysaccharide-peptide hydrogels usually involves multiple synthetic steps, thus reducing the effectiveness and practicality of these approaches. Inspired by recent discoveries in aqueous N-carboxyanhydride (NCA) ring-opening polymerization (ROP) and ring-opening polymerization-induced nanogelation, we present an aqueous one-pot strategy to prepare polysaccharide-polypeptide hydrogels. In this study, water-soluble polysaccharide carboxymethyl chitosan is used as the macromolecular initiator to prepare polysaccharide-polypeptide copolymers through the aqueous ROP of NCA. The catalyst-free approach afforded hydrogels with properties that could be controlled by adjusting the type and amount of NCA used, with the elastic modulus ranging from 50 Pa to 18000 Pa. The hydrogen bond-cross-linked hydrogel exhibited self-healing and injectable properties. Morphology characterization revealed that micelles were formed in the early stage of reaction, suggesting that the polymerization follows an aqueous ring-opening polymerization-induced self-assembly (ROPISA) mechanism and that aggregation of micelles during the reaction caused the gelation. Moreover, the hydrogels displayed high swelling ratios (>95% water content), and hemolysis and cytotoxicity experiments demonstrated that the hydrogels had excellent biocompatibility, indicating their potential in medical applications.

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

Background: This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses. Methods: Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies. Results: Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib. Conclusions: The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Metabolism-driven in vitro/in vivo disconnect of an oral ERɑ VHL-PROTAC.

Targeting the estrogen receptor alpha (ERα) pathway is validated in the clinic as an effective means to treat ER+ breast cancers. Here we present the development of a VHL-targeting and orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of ERα. In vitro studies with this PROTAC demonstrate excellent ERα degradation and ER antagonism in ER+ breast cancer cell lines. However, upon dosing the compound in vivo we observe an in vitro-in vivo disconnect. ERα degradation is lower in vivo than expected based on the in vitro data. Investigation into potential causes for the reduced maximal degradation reveals that metabolic instability of the PROTAC linker generates metabolites that compete for binding to ERα with the full PROTAC, limiting degradation. This observation highlights the requirement for metabolically stable PROTACs to ensure maximal efficacy and thus optimisation of the linker should be a key consideration when designing PROTACs.

Comprehensive prognostic and immune analysis of sterol O-acyltransferase 1 in patients with hepatocellular carcinoma.

BACKGROUND: Sterol O-acyltransferase 1 (SOAT1) is an important target in the diagnosis and treatment of liver cancer. However, the prognostic value of SOAT1 in patients with hepatocellular carcinoma (HCC) is still not clear. AIM: To investigate the correlation of SOAT1 expression with HCC, using RNA-seq and gene expression data of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) and pan-cancer. METHODS: The correlation between SOAT1 expression and HCC was analyzed. Cox hazard regression models were conducted to investigate the prognostic value of SOAT1 in HCC. Overall survival and disease-specific survival were explored based on TCGA-LIHC data. Biological processes and functional pathways mediated by SOAT1 were characterized by gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes. In addition, the protein-protein interaction network and co-expression analyses of SOAT1 in HCC were performed to better understand the regulatory mechanisms of SOAT1 in this malignancy. RESULTS: SOAT1 and SOAT2 were highly expressed in unpaired samples, while only SOAT1 was highly expressed in paired samples. The area under the receiver operating characteristic curve of SOAT1 expression in tumor samples from LIHC patients compared with para-carcinoma tissues was 0.748, while the area under the curve of SOAT1 expression in tumor samples from LIHC patients compared with GTEx was 0.676. Patients with higher SOAT1 expression had lower survival rates. Results from GO/KEGG and gene set enrichment analyses suggested that the PI3K/AKT signaling pathway, the IL-18 signaling pathway, the calcium signaling pathway, secreted factors, the Wnt signaling pathway, the Jak/STAT signaling pathway, the MAPK family signaling pathway, and cell-cell communication were involved in such association. SOAT1 expression was positively associated with the abundance of macrophages, Th2 cells, T helper cells, CD56bright natural killer cells, and Th1 cells, and negatively linked to the abundance of Th17 cells, dendritic cells, and cytotoxic cells. CONCLUSION: Our findings demonstrate that SOAT1 may serve as a novel target for HCC treatment, which is helpful for the development of new strategies for immunotherapy and metabolic therapy.

Dauricine attenuates ovariectomized-induced bone loss and RANKL-induced osteoclastogenesis via inhibiting ROS-mediated NF-κB and NFATc1 activity.

BACKGROUND: Osteoclast plays an important role in maintaining the balance between bone anabolism and bone catabolism. The abnormality of osteoclast is closely related to osteolytic bone diseases such as osteoporosis, rheumatoid arthritis and tumor bone metastasis. PURPOSE: We aim to search for natural compound that may suppress osteoclast formation and function. STUDY DESIGN: In this study, we assessed the impact of Dauricine (Dau) on the formation and function of osteoclasts in vitro, as well as its potential in preventing bone loss in an ovariectomy mouse model in vivo. METHODS: Multiple in vitro experiments were carried out, including osteoclastogenesis, podosomal belt formation, bone resorption assay, RNA-sequencing, real-time quantitative PCR, ROS level detection, surface plasmon resonance assay, luciferase assay and western blot. To verify the effect in vivo, an ovariectomized mouse model (OVX model) was constructed, and bone parameters were measured using micro-CT and histology. Furthermore, metabolomics analysis was performed on blood serum samples from the OVX model. RESULTS: In vitro experiments demonstrated that Dau inhibits RANKL-induced osteoclastogenesis, podosomal belt formation, and bone resorption function. RNA-sequencing results revealed that Dau significantly suppresses genes related to osteoclast. Functional enrichment analysis indicated that Dau's inhibition of osteoclasts may be associated with NF-κB signaling pathway and reactive oxygen metabolism pathway. Molecular docking, surface plasmon resonance assay and western blot analysis further confirmed that Dau inhibits RANKL-induced osteoclastogenesis by modulating the ROS/NF-κB/NFATc1 pathway. Moreover, administration of Dau to OVX-induced mice validated its efficacy in treating bone loss disease. CONCLUSION: Dau prevents OVX-induced bone loss by inhibiting osteoclast activity and bone resorption, potentially offering a new approach for preventing and treating metabolic bone diseases such as osteoporosis. This study provides innovative insights into the inhibitory effects of Dau in an in vivo OVX model and elucidates the underlying mechanism.

A nomogram based on contrast-enhanced ultrasound for evaluating the glomerulosclerosis rate in transplanted kidneys.

Background: A high rate of glomerulosclerosis serves as an important signal of poor response to treatment and a high risk of disease progression or adverse prognosis in transplanted kidneys. We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a novel imaging biomarker in the early prediction of glomerulosclerosis rate by evaluating renal allograft microcirculation. Methods: A retrospective analysis was performed on 143 transplanted kidney recipients with confirmed pathology, including 100 in the training group and 43 in the validation group. All patients underwent conventional ultrasound (CUS) and CEUS examinations. The patients were divided into two groups: those with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis. The nomograms derived from independent predictors identified by multivariate logistic analysis were assessed using receiver operating characteristic (ROC) curve analysis, 1,000 bootstrap resamples, calibration curves, and decision curve analysis (DCA). Results: The patients with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis showed statistically significant differences in CEUS parameters, including in peak intensity (PI) (25 vs. 30; P<0.001), absolute time to peak (ATTP) (10 vs. 9; P=0.004), and time to peak (TTP) (22 vs. 19.5; P=0.026). Multivariate analysis revealed that PI [odds ratio (OR) =0.852; 95% confidence interval (CI): 0.737-0.986], peak systolic velocity (PSV) of the interlobar artery (OR =0.850; 95% CI: 0.758-0.954), cortical echogenicity (OR =38.429; 95% CI: 3.695-399.641), and time since transplantation (OR =1.017; 95% CI: 1.006-1.028) were independent predictors of whether the glomerulosclerosis rate was >50% and were incorporated into the construction of a nomogram. The area under the curve (AUC) of the nomogram in the training and validation groups was 0.914 (95% CI: 0.840-0.960) and 0.909 (95% CI: 0.781-0.975), respectively, with a bootstrap resampling AUC of 0.877. The calibration curve and DCA confirmed the diagnostic performance of the nomogram model. Conclusions: The nomogram, which combined CUS, CEUS, and clinical indicators, exhibited notable predictive efficacy for the glomerulosclerosis rate in transplanted kidneys, thereby demonstrating the potential to improve clinical decision-making.

MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential.

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Impact of immediate postrecanalization cooling on outcome in acute ischemic stroke patients with a large ischemic core: prospective cohort study.

BACKGROUND: Patients with large acute ischemic strokes (AIS) often have a poor prognosis despite successful recanalization due to multiple factors including reperfusion injury. The authors aim to describe our preliminary experience of endovascular cooling in patients with a large AIS after recanalization. METHODS: From January 2021 to July 2022, AIS patients presenting with large infarcts (defined as ASPECTS ≤5 on noncontrast CT or ischemic core ≥50 ml on CT perfusion) who achieved successful recanalization after endovascular treatment were analyzed in a prospective registry. Patients were divided into targeted temperature management (TTM) and non-TTM group. Patients in the TTM group received systemic cooling with a targeted core temperature of 33° for at least 48 h. The primary outcome is 90-day favorable outcome [modified Rankin Scale (mRS) 0-2]. The secondary outcomes are 90-day good outcome (mRS 0-3), mortality, intracranial hemorrhage and malignant cerebral edema within 7 days or at discharge. RESULTS: Forty-four AIS patients were recruited (15 cases in the TTM group and 29 cases in the non-TTM group). The median Alberta Stroke Program Early CT Score (ASPECTS) was 3 (2-5). The median time for hypothermia duration was 84 (71.5-147.6) h. The TTM group had a numerically higher proportion of 90-day favorable outcomes than the non-TTM group (46.7 vs. 27.6%, P=0.210), and no significant difference were found regarding secondary outcomes (all P>0.05). The TTM group had a numerically higher rates of pneumonia (66.7 vs. 58.6%, P=0.604) and deep vein thrombosis (33.3 vs. 13.8%, P=0.138). Shivering occurred in 4/15 (26.7%) of the TTM patients and in none of the non-TTM patients (P=0.009). CONCLUSIONS: Postrecanalization cooling is feasible in patients with a large ischemic core. Future randomized clinical trials are warranted to validate its efficacy.

One Stone Several Birds: Self-Localizing Submicrocages With Dual Loading Points for Multifunctional Drug Delivery.

As the core index, how to improve bioavailability of loaded cargoes is a hot topic of drug carriers. In this study, aminated ß-cyclodextrin (ß-CD) as a cross-linking points is first integrated into 3D poly(acrylamide-co-acrylonitrile) (P(AAm-co-AN)) network to build up a unique submicrocage (466.2 ± 47.6 nm), featuring upper critical solution temperature (UCST; ≈40 °C), high volume expansion coefficient, and excellent biocompatibility. Hereinto, hydrophobic ß-elemene (ELE) is locally loaded in ß-CD with high loading efficiency (8.72%) and encapsulation efficiency (78.60%) through hydrophobic desolvation and host-guest interaction. Above UCST, the release of the loaded ELE is accelerated to 72.87% in 24 h, together with the enhanced sensitization effect of synergized radiotherapy. Given spontaneous long-lasting delivery, targeted embolization, and post-treatment removal of such UCST-type submicrocage, it is anticipated to provide a novel, facile, efficient, and versatile strategy of comprehensive anticancer treatments for high drug bioavailability.

Enhancing X-Ray Sensitization with Multifunctional Nanoparticles.

In the progression of X-ray-based radiotherapy for the treatment of cancer, the incorporation of nanoparticles (NPs) has a transformative impact. This study investigates the potential of NPs, particularly those comprised of high atomic number elements, as radiosensitizers. This aims to optimize localized radiation doses within tumors, thereby maximizing therapeutic efficacy while preserving surrounding tissues. The multifaceted applications of NPs in radiotherapy encompass collaborative interactions with chemotherapeutic, immunotherapeutic, and targeted pharmaceuticals, along with contributions to photodynamic/photothermal therapy, imaging enhancement, and the integration of artificial intelligence technology. Despite promising preclinical outcomes, the paper acknowledges challenges in the clinical translation of these findings. The conclusion maintains an optimistic stance, emphasizing ongoing trials and technological advancements that bolster personalized treatment approaches. The paper advocates for continuous research and clinical validation, envisioning the integration of NPs as a revolutionary paradigm in cancer therapy, ultimately enhancing patient outcomes.

Long-short diffeomorphism memory network for weakly-supervised ultrasound landmark tracking.

Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition. Accurate tracking of an anatomical landmark has been of high interest for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy. However, tracking an anatomical landmark accurately in ultrasound video is very challenging, due to landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphism memory network (LSDM), which is a multi-task framework with an auxiliary learnable deformation prior to supporting accurate landmark tracking. Specifically, we design a novel diffeomorphic representation, which contains both long and short temporal information stored in separate memory banks for delineating motion margins and reducing cumulative errors. We further propose an expectation maximization memory alignment (EMMA) algorithm to iteratively optimize both the long and short deformation memory, updating the memory queue for mitigating local anatomical ambiguity. The proposed multi-task system can be trained in a weakly-supervised manner, which only requires few landmark annotations for tracking and zero annotation for deformation learning. We conduct extensive experiments on both public and private ultrasound landmark tracking datasets. Experimental results show that LSDM can achieve better or competitive landmark tracking performance with a strong generalization capability across different scanner types and different ultrasound modalities, compared with other state-of-the-art methods.

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.

Background: Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies. Methods: Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy. Results: The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC. Conclusion: Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

Dual-Channel/Localization Single-Molecule Fluorescence Probe for Monitoring ATP and HOCl in Early Diagnosis and Therapy of Rheumatoid Arthritis.

Rheumatoid arthritis (RA), a common chronic inflammatory illness, is still incurable, reducing the sufferers' quality of life significantly. Adenosine 5'-triphosphate (ATP) and hypochlorous acid (HOCl) are key indicators in RA, but their precise mechanisms in RA pathophysiology are unknown. As a result, in order to detect ATP and HOCl simultaneously, we created two new dual-channel/localization single-molecule fluorescence probes, RhTNMB and RhFNMB. Furthermore, RhFNMB outperformed RhTNMB in terms of detection performance. ATP and HOCl produce independent fluorescence responses in the light red channel (λex = 520 nm, λem = 586 nm) and deep red channel (λex = 620 nm, λem = 688 nm), respectively, without spectral crosstalk. It should be noted that the probe RhFNMB successfully imaged ATP in mitochondria and HOCl in cells. Surprisingly, the probe RhFNMB demonstrated remarkable detection ability in the diagnosis and treatment of Pseudomonas aeruginosa-induced abdominal inflammation in mice. We continued to apply the probe RhFNMB to track ATP and HOCl in RA and discovered that ATP and HOCl concentrations were considerably greater in RA joints than in normal joints. We also confirmed the therapeutic effect of methotrexate on RA. This study is the first to achieve dual-channel imaging of ATP and HOCl, which is of great value for the early diagnosis and therapy of RA.