Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception.

Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 µm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor's capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

Investigation on taxonomy, secondary metabolites and antibacterial activity of Streptomyces sediminicola sp. nov., a novel marine sediment-derived Actinobacteria.

BACKGROUND: Marine actinomycetes, especially Streptomyces, are recognized as excellent producers of diverse and bioactive secondary metabolites on account of the multiplicity of marine habitations and unique ecological conditions, which are yet to be explored in terms of taxonomy, ecology, and functional activity. Isolation, culture and genome analysis of novel species of Streptomyces to explore their potential for discovering bioactive compounds is an important approach in natural product research. RESULTS: A marine actinobacteria, designated strain SCSIO 75703 T, was isolated, and the potential for bioactive natural product discovery was evaluated based on genome mining, compound detection, and antimicrobial activity assays. The phylogenetic, phenotypic and chemotaxonomic analyses indicate that strain SCSIO 75703 T represents a novel species in genus Streptomyces, for which the name Streptomyces sediminicola sp. nov. is proposed. Genome analysis revealed the presence of 25 secondary metabolite biosynthetic gene clusters. The screening for antibacterial activity reveals the potential to produce bioactive metabolites, highlighting its value for in-depth exploration of chemical constituents. Seven compounds (1-7) were separated from the fractions guided by antibacterial activities, including three indole alkaloids (1-3), three polyketide derivatives (4-6), and 4-(dimethylamino)benzoic acid (7). These primarily antibacterial components were identified as anthracimycin (4), 2-epi-anthracimycin (5) and ß-rubromycin (6), presenting strong antibacterial activities against Gram-positive bacteria with the MIC value ranged from 0.125 to 16 µg/mL. Additionally,, monaprenylindole A (1) and 3-cyanomethyl-6-prenylindole (2) displayed moderate inhibitory activities against α-glucosidase with the IC50 values of 83.27 and 86.21 µg/mL, respectively. CONCLUSION: Strain SCSIO 75703 T was isolated from marine sediment and identified as a novel species within the genus Streptomyces. Based on genomic analysis, compounds isolation and bioactivity studies, seven compounds were identified, with anthracimycin and ß-rubromycin showing significant biological activity and promising potential for further applications.

A single-pixel and non-redundant branching-based algorithm for nailfold capillary skeleton line extraction.

Background: Static nailfold capillary parameters are important parameters that reflect the health of the human body. Disease onset or progression is often accompanied by changes in the physiological parameters of the nailfold. Hence, the physiological parameters of the nailfold are closely related to the study of disease, with their automated and high-precision measurements playing a crucial role in these studies. Currently, manually measured values of the nailfold's parameters are the gold standard; however, they are time consuming and labor intensive, making the development of automated measurement methods essential. Most automated measurement methods use skeleton lines; however, current skeleton-thinning algorithms have non-single pixels and redundant branches that lead to reduced measurement accuracy. This study proposes a single-pixel and non-redundant branching-based skeleton line extraction algorithm for nailfold capillaries, which is then applied to nailfold static parameter calculations to improve accuracy. Methods: The algorithm includes deletion and restoration templates combined with the depth-first search method to obtain single-pixel skeleton lines without redundant branches. These lines are applied to the static nailfold capillary parameter measurement method based on digital image processing to calculate the blood vessel diameter. Results: The results show that the proposed method can obtain the single-pixel skeleton line without the redundant branches that are required for the parameter calculations and improve the accuracy of the nailfold capillary diameter measurement. Experiments showed that the root mean square errors (RMSEs) of the labeled apical diameter, arterial limb diameter, and venous limb diameter were 0.794, 0.756, and 0.830 µm, respectively, when the calculated results were compared with those of the manual calculations. According to the accuracy formula, the accuracy of the method in this study is 90%. We calculated the P values of the algorithmic and manual measurements to P<0.001 and found that the difference in the measurements of the proposed algorithm is statistically significant. Therefore, the method in this study has high sensitivity and specificity for the measurement of normal nailfold capillaries. Conclusions: The proposed algorithm could obtain single-pixel skeleton lines without redundant branches, thereby improving the nailfold static parameter measurement accuracy.

Regulatory network analysis reveals gene-metabolite relationships in pear fruit treated with methyl jasmonate.

The economic value of pear is determined by its intrinsic qualities, which are influenced by metabolites produced during the ripening process. Methyl jasmonate (MeJA), a hormone, plays an important role in plant metabolism. To date, few studies have investigated the molecular mechanism underlying the changes in metabolic pathways related to the internal quality of pear fruit after MeJA treatment. In this study, ultrahigh-performance liquid chromatography‒Q Exactive Orbitrap mass spectrometry (UHPLC‒QE‒MS) was used to determine the changes in metabolite contents in pear after MeJA treatment. MeJA treatment primarily activated carbohydrate metabolism and amino acid metabolism pathways. Through combined analysis of UHPLC‒QE‒MS data and whole-transcriptome data, the abovementioned pathways and each metabolite were analysed separately, and competitive endogenous RNA (ceRNA) and microRNA-transcription factor-target (miRNA-TF-target) regulatory networks were constructed. The core nodes of three genes (PEA, Pbr022732.1; GAA, Pbr035655.1; and miR8033-x) and two genes (SDS, Pbr031708.1; and novel-m6796-3p) were associated with the carbohydrate metabolism and amino acid metabolism pathways, respectively. The core mRNA nodes TCONS_00048038 and Pbr019584.1, the core miRNA node miR4993-x, the core lncRNA node TCONS_0004356, the core circRNA node novel_circ_001967 and the core transcription factor node TSO1 (Pbr025407.1) were identified via separate metabolite analyses. These findings elucidate the changes in metabolites related to fruit quality in 'Nanguo' pear and the relationships between the metabolites and genes, reveal the molecular mechanism underlying the response of MeJA treatment in pear fruit, and provide a theoretical basis for improving the internal quality of 'Nanguo' pear.

cNEK6 induces gemcitabine resistance by promoting glycolysis in pancreatic ductal adenocarcinoma via the SNRPA/PPA2c/mTORC1 axis.

Resistance to gemcitabine in pancreatic ductal adenocarcinoma (PDAC) leads to ineffective chemotherapy and, consequently, delayed treatment, thereby contributing to poor prognosis. Glycolysis is an important intrinsic reason for gemcitabine resistance as it competitively inhibits gemcitabine activity by promoting deoxycytidine triphosphate accumulation in PDAC. However, biomarkers are lacking to determine which patients can benefit significantly from glycolysis inhibition under the treatment of gemcitabine activity, and a comprehensive understanding of the molecular mechanisms that promote glycolysis in PDAC will contribute to the development of a strategy to sensitize gemcitabine chemotherapy. In this study, we aimed to identify a biomarker that can robustly indicate the intrinsic resistance of PDAC to gemcitabine and guide chemotherapy sensitization strategies. After establishing gemcitabine-resistant cell lines in our laboratory and collecting pancreatic cancer and adjacent normal tissues from gemcitabine-treated patients, we observed that circRNA hsa_circ_0008383 (namely cNEK6) was highly expressed in the peripheral blood and tumor tissues of patients and xenografts with gemcitabine-resistant PDAC. cNEK6 enhanced resistance to gemcitabine by promoting glycolysis in PDAC. Specifically, cNEK6 prevented K48 ubiquitination of small ribonucleoprotein peptide A from the BTRC, a ubiquitin E3 ligase; thus, the accumulated SNRPA stopped PP2Ac translation by binding to its G-quadruplexes in 5' UTR of mRNA. mTORC1 pathway was aberrantly phosphorylated and activated owing to the absence of PP2Ac. The expression level of cNEK6 in the peripheral blood and tumor tissues correlated significantly and positively with the activation of the mTORC1 pathway and degree of glycolysis. Hence, the therapeutic effect of gemcitabine is limited in patients with high cNEK6 levels, and in combination with the mTORC1 inhibitor, rapamycin, can enhance sensitivity to gemcitabine chemotherapy.

Inhalation of H2/O2 (66.7 %/33.3 %) mitigates depression-like behaviors in diabetes mellitus complicated with depression mice via suppressing inflammation and preventing hippocampal damage.

Diabetes mellitus complicated with depression (DD) is a prevalent psychosomatic disorder. It is characterized by severe cognitive impairment, and associated with high rates of disability and mortality. Although conventional treatment options are available, the efficacy of these regimens in managing DD remains limited. Molecular hydrogen (H2), a selective hydroxyl radical scavenger, has shown therapeutic potential in the treatment of various systemic diseases. This study aims to investigate the therapeutic effects of H2 on DD. A DD mouse model was established through intraperitoneal injection of streptozotocin (STZ, 150 mg/kg) and lipopolysaccharide (LPS, 0.5 mg/kg). Following the induction of DD, the mice were treated with H2/O2 (66.7 %/33.3 %)inhalation for 7 days. Behavioral assessments were conducted by standard behavioral tests, and the levels of inflammatory cytokines in peripheral blood serum and hippocampal tissue were measured using enzyme-linked immunosorbent assay (ELISA). Furthermore, magnetic resonance imaging (MRI) scans and immunofluorescence staining of the hippocampus were performed to evaluate hippocampal structural integrity. The results demonstrated that inhalation of H2/O2 (66.7 %/33.3 %) significantly ameliorated depressive behaviors and symptoms in DD mice, reversed hippocampal volume reduction, decreased inflammatory cytokine levels in peripheral blood serum and hippocampal tissue, and inhibited the activation of A1 astrocytes in the hippocampus. Our study suggests that H2/O2 (66.7 %/33.3 %) inhalation therapy may offer a promising treatment strategy for DD and its associated symptoms.

Epitope prime editing shields hematopoietic cells from CD123 immunotherapy for acute myeloid leukemia.

Acute myeloid leukemia (AML) is a malignant cancer characterized by abnormal differentiation of hematopoietic stem and progenitor cells (HSPCs). While chimeric antigen receptor T (CAR-T) cell immunotherapies target AML cells, they often induce severe on-target/off-tumor toxicity by attacking normal cells expressing the same antigen. Here, we used base editors (BEs) and a prime editor (PE) to modify the epitope of CD123 on HSPCs, protecting healthy cells from CAR-T-induced cytotoxicity while maintaining their normal function. Although BE effectively edits epitopes, complex bystander products are a concern. To enhance precision, we optimized prime editing, increasing the editing efficiency from 5.9% to 78.9% in HSPCs. Epitope-modified cells were resistant to CAR-T lysis while retaining normal differentiation and function. Furthermore, BE- or PE-edited HSPCs infused into humanized mice endowed myeloid lineages with selective resistance to CAR-T immunotherapy, demonstrating a proof-of-concept strategy for treating relapsed AML.

Paper Spray Ionization Mass Spectrometry Coupled with Paper-Based Three-Dimensional Tumor Model for Rapid Metabolic Gradient Profiling.

The tumor microenvironment (TME), especially with its complicated metabolic characteristics, will dynamically affect the proliferation, migration, and drug response of tumor cells. Rapid metabolic analysis brings out a deeper understanding of the TME, while the susceptibility and environmental dependence of metabolites extremely hinder real-time metabolic profiling since the TME is easily disrupted. Here, we directly integrated paper spray ionization mass spectrometry with a paper-based three-dimensional (3D) tumor model, realizing the rapid capture of metabolic gradients. The entire procedure, from sample preparation to mass spectrometry detection, took less than 4 min, which was able to provide metabolic results close to real time and contributed to understanding the real metabolic processes. At present, our method successfully detected 160 metabolites; notably, over 40 significantly gradient metabolites were revealed across the six layers of the paper-based 3D tumor model. At least 22 gradient metabolites were reported to be associated with cell viability. This strategy was powerful enough to rapidly profile metabolic gradients of a paper-based 3D tumor model for revealing cell viability changes from a metabolomics perspective.

Optimizing nitrogen fertilizer for improved root growth, nitrogen utilization, and yield of cotton under mulched drip irrigation in southern Xinjiang, China.

The root system plays a crucial role in water and nutrient absorption, making it a significant factor affected by nitrogen (N) availability in the soil. However, the intricate dynamics and distribution patterns of cotton (Gossypium hirsutum L.) root density and N nutrient under varying N supplies in Southern Xinjiang, China, have not been thoroughly understood. A two-year experiment (2021 and 2022) was conducted to determine the effects of five N rates (0, 150, 225, 300, and 450 kg N ha-1) on the root system, shoot growth, N uptake and distribution, and cotton yield. Compared to the N0 treatment (0 kg N ha-1), the application of N fertilizer at a rate of 300 kg N ha-1 resulted in consistent and higher seed cotton yields of 5875 kg ha-1 and 6815 kg ha-1 in 2021 and 2022, respectively. This N fertilization also led to a significant improvement in dry matter weight and N uptake by 32.4% and 53.7%, respectively. Furthermore, applying N fertilizer at a rate of 225 kg N ha-1 significantly increased root length density (RLD), root surface density (RSD), and root volume density (RVD) by 49.6-113.3%, 29.1-95.1%, and 42.2-64.4%, respectively, compared to the treatment without N fertilization (0 kg N ha-1). Notably, the roots in the 0-20 cm soil layers exhibited a stronger response to N fertilization compared to the roots distributed in the 20-40 cm soil layers. The root morphology parameters (RLD, RSD, and RVD) at specific soil depths (0-10 cm in the seedling stage, 10-25 cm in the bud stage, and 20-40 cm in the peak boll stage) were significantly associated with N uptake and seed cotton yield. Optimizing nitrogen fertilizer supply within the range of 225-300 kg N ha-1 can enhance root foraging, thereby promoting the interaction between roots and shoots and ultimately improving cotton production in arid areas.

Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study.

PURPOSE: To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. METHODS: From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance. RESULTS: Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses. CONCLUSIONS: The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm. CRITICAL RELEVANCE STATEMENT: Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making. KEY POINTS: MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

Integrated metabolomic and transcriptomic analysis revealed the role of PbrCYP94B in wax accumulation in pear fruit after bagging treatment.

Preharvest fruit bagging is a safe and environmentally friendly production measure. Cuticular wax, as the first protective layer on the fruit surface, has important functions. However, the effects of preharvest bagging on cuticular wax synthesis in pears and the related molecular mechanisms are still unclear. Here, the impact of fruit bagging with different materials on cuticular wax synthesis in pear fruit, and the underlying molecular mechanism, were revealed from metabolomic, transcriptomic, morphological, and molecular biological perspectives. Our results revealed that, compared with that in the not bagged (NB) treatment group (0.59 mg/cm2), the total wax concentration was 1.32- and 1.37-fold greater in the single-layered white paper bag (WPB, 1.37 mg/cm2) and double-layered yellow-white paper bag, (YWPB, 1.40 mg/cm2) treatment groups, while it was slightly lower in the double-layered yellow-black paper bag (YBPB, 0.45 mg/cm2) group, which was consistent with the scanning electron microscopy (SEM) results. Integrated metabolomic and transcriptomic analysis revealed 29 genes associated with cuticular wax synthesis. Overexpression of PbrCYP94B, which is a key gene in the wax synthesis pathway in pear fruit, increased the total wax and alkane contents. This study provides valuable insights for the creation of new pear germplasms with high wax contents.

Sulindac (K-80003) with nab-paclitaxel and gemcitabine overcomes drug-resistant pancreatic cancer.

The Nab-paclitaxel combined with gemcitabine (AG) regimen is the main chemotherapy regimen for pancreatic cancer, but drug resistance often occurs. Currently, the ability to promote sensitization in drug-resistant cases is an important clinical issue, and the strategy of repurposing conventional drugs is a promising strategy. This study aimed to identify a classic drug that targets chemotherapy resistance's core signaling pathways and combine it with the AG regimen to enhance chemosensitivity. We also aimed to find reliable predictive biomarkers of drug combination sensitivity. Using RNA sequencing, we found that abnormal PI3K/Akt pathway activation plays a central role in mediating resistance to the AG regimen. Subsequently, through internal and external verification of randomly selected AG-resistant patient-derived organoid (PDO) and PDO xenograft models, we discovered for the first time that the classic anti-inflammatory drug sulindac K-80003, an inhibitor of the PI3K/Akt pathway that we focused on, promoted sensitization in half (14/28) of AG-resistant pancreatic ductal adenocarcinoma cases. Through RNA-sequencing, multiplex immunofluorescent staining, and immunohistochemistry experiments, we identified cFAM124A as a novel biomarker through which sulindac K-80003 promotes AG sensitization. Its role as a sensitization marker is explained via the following mechanism: cFAM124A enhances both the mRNA expression of cathepsin L and the activity of the cathepsin L enzyme. This dual effect stimulates the cleavage of RXRα, leading to large amounts of truncated RXRα, which serves as a direct target of K-80003. Consequently, this process results in the pathological activation of the PI3K/Akt pathway. In summary, our study provides a new treatment strategy and novel biological target for patients with drug-resistant pancreatic cancer.

RNA m6A modification in ferroptosis: implications for advancing tumor immunotherapy.

The pursuit of innovative therapeutic strategies in oncology remains imperative, given the persistent global impact of cancer as a leading cause of mortality. Immunotherapy is regarded as one of the most promising techniques for systemic cancer therapies among the several therapeutic options available. Nevertheless, limited immune response rates and immune resistance urge us on an augmentation for therapeutic efficacy rather than sticking to conventional approaches. Ferroptosis, a novel reprogrammed cell death, is tightly correlated with the tumor immune environment and interferes with cancer progression. Highly mutant or metastasis-prone tumor cells are more susceptible to iron-dependent nonapoptotic cell death. Consequently, ferroptosis-induction therapies hold the promise of overcoming resistance to conventional treatments. The most prevalent post-transcriptional modification, RNA m6A modification, regulates the metabolic processes of targeted RNAs and is involved in numerous physiological and pathological processes. Aberrant m6A modification influences cell susceptibility to ferroptosis, as well as the expression of immune checkpoints. Clarifying the regulation of m6A modification on ferroptosis and its significance in tumor cell response will provide a distinct method for finding potential targets to enhance the effectiveness of immunotherapy. In this review, we comprehensively summarized regulatory characteristics of RNA m6A modification on ferroptosis and discussed the role of RNA m6A-mediated ferroptosis on immunotherapy, aiming to enhance the effectiveness of ferroptosis-sensitive immunotherapy as a treatment for immune-resistant malignancies.

Discovery of natural product derivative triptolidiol as a direct NLRP3 inhibitor by reducing K63-specific ubiquitination.

BACKGROUND AND PURPOSE: NLRP3 is up-regulated in inflammatory and autoimmune diseases. The development of NLRP3 inhibitors is challenged by the identification of compounds with distinct mechanisms of action avoiding side effects and toxicity. Triptolide is a natural product with multiple anti-inflammatory activities, but a narrow therapeutic window. EXPERIMENTAL APPROACH: Natural product triptolide derivatives were screened for NLRP3 inhibitors in human THP-1 and mouse bone marrow-derived macrophages. The efficacy of potent NLRP3 inhibitors was evaluated in LPS-induced acute lung injury and septic shock models. KEY RESULTS: Triptolidiol was identified as a selective inhibitor of NLRP3 with high potency. Triptolidiol inactivated the NLRP3 inflammasome in human THP-1 and mouse primary macrophages primed with LPS. Triptolidiol specifically inhibited pro-caspase 1 cleavage downstream of NLRP3, but not AIM2 or NLRC4 inflammasomes. Based on the structure-activity relationship study, the C8-ß-OH group was critical for its binding to NLRP3. Triptolidiol exhibited a submicromolar KD for NLRP3, binding to residue C280. This binding prevented the interaction of NLRP3 with NEK7, the key regulator of NLRP3 inflammasome oligomerization and assembly, but not with the inflammasome adaptor protein ASC. Triptolidiol decreased the K63-specific ubiquitination of NLRP3, leading NLRP3 to a "closed" inactive conformation. Intraperitoneal administration of triptolidiol significantly attenuated LPS-induced acute lung injury and lethal septic shock. CONCLUSION AND IMPLICATIONS: Triptolidiol is a novel NLRP3 inhibitor that regulates inflammasome assembly and activation by decreasing K63-linked ubiquitination. Triptolidiol has novel structural features that make it distinct from reported NLRP3 inhibitors and represents a viable therapeutic lead for inflammatory diseases.

Rapid generation of long, chemically modified pegRNAs for prime editing.

The editing efficiencies of prime editing (PE) using ribonucleoprotein (RNP) and RNA delivery are not optimal due to the challenges in solid-phase synthesis of long PE guide RNA (pegRNA) (>125 nt). Here, we develop an efficient, rapid and cost-effective method for generating chemically modified pegRNA (125-145 nt) and engineered pegRNA (epegRNA) (170-190 nt). We use an optimized splint ligation approach and achieve approximately 90% production efficiency for these RNAs, referred to as L-pegRNA and L-epegRNA. L-epegRNA demonstrates enhanced editing efficiencies across various cell lines and human primary cells with improvements of up to more than tenfold when using RNP delivery and several hundredfold with RNA delivery of PE, compared to epegRNA produced by in vitro transcription. L-epegRNA-mediated RNP delivery also outperforms plasmid-encoded PE in most comparisons. Our study provides a solution to obtaining high-quality pegRNA and epegRNA with desired chemical modifications, paving the way for the use of PE in therapeutics and various other fields.

Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.

Background: Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ultrasonography (US) is the first-line imaging method for evaluating the thyroid gland and CLNs. However, this assessment relies mainly on the subjective judgment of the sonographer and is very much dependent on the sonographer's experience. This prospective study was designed to construct a machine learning model based on contrast-enhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis in patients with TC. Methods: Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2-minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lymph node imaging video, three machine learning models, namely, support vector machine, linear discriminant analysis (LDA), and decision tree (DT), were constructed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). Results: A total of 75 lymph nodes were included in the study, with 42 benign cases and 33 malignant cases. Among the machine learning models constructed, the support vector machine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%. Conclusions: The machine learning model based on US video is helpful for the diagnosis of whether metastasis occurs in the CLNs of TC patients.

Identifying Factors Contributing to Delayed Diagnosis of Ovarian Cancer: A Comprehensive Analysis.

Background: Ovarian cancer (OC) remains the deadliest gynecologic malignancy worldwide due to delayed diagnosis, recurrence, and drug resistance. This study aimed to identify key factors affecting delayed diagnosis in OC patients. Methods: A retrospective analysis was conducted on OC patients treated at Taihe Hospital, Hubei University of Medicine from June 2023 to September 2023. Patients were categorized based on a three-months cut-off point for delayed diagnosis. Collected data included demographics, tumor incidence, and disease cognition. The analysis of variance and the chi-squared test was used for comparison between groups. Results: The significant differences were found in age, residence, education level, family income, family history of tumor, histology, FIGO stage, and tumor location between groups (P<0.05). Multifactorial logistic regression analysis identified education level [odds ratio (OR) = 0.606; 95% confidence interval (CI): 0.440, 0.833; P = 0.002], family history of tumor (OR = 0.462; 95% CI: 0.214, 0.997; P = 0.049), emotional barriers (OR = 1.332; 95% CI: 1.081, 1.642; P = 0.007), and practical barriers (OR = 2.964; 95% CI: 2.195, 4.004; P < 0.001) as risk factors for delayed diagnosis of OC. Conclusion: Patient cognition is crucial in OC diagnosis delay. Enhancing public awareness and understanding of OC is essential to eliminate fear and improve early diagnosis.

Platelet-Derived Growth Factor C Facilitates Malignant Behavior of Pancreatic Ductal Adenocarcinoma by Regulating SREBP1 Mediated Lipid Metabolism.

Lipid metabolism reprogramming stands as a fundamental hallmark of cancer cells. Unraveling the core regulators of lipid biosynthesis holds the potential to find promising therapeutic targets in pancreatic ductal adenocarcinoma (PDAC). Here, it is demonstrated that platelet-derived growth factor C (PDGFC) orchestrated lipid metabolism, thereby facilitated the malignant progression of PDAC. Expression of PDGFC is upregulated in PDAC cohorts and is corelated with a poor prognosis. Aberrantly high expression of PDGFC promoted proliferation and metastasis of PDAC both in vitro and in vivo. Mechanistically, PDGFC accelerated the malignant progression of PDAC by upregulating fatty acid accumulation through sterol regulatory element-binding protein 1 (SREBP1), a key transcription factor in lipid metabolism. Remarkably, Betulin, an inhibitor of SREBP1, demonstrated the capability to inhibit proliferation and metastasis of PDAC cell lines, along with attenuating the process of liver metastasis in vivo. Overall, the study underscores the pivotal role of PDGFC-mediated lipid metabolism in PDAC progression, suggesting PDGFC as a potential biomarker for PDAC metastasis. Targeting PDGFC-induced lipid metabolism emerges as a promising therapeutic strategy for metastatic PDAC, with the potential to improve clinical outcomes.

Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance.

Increasing evidence indicates that the remodeling of immune microenvironment heterogeneity influences pancreatic cancer development, as well as sensitivity to chemotherapy and immunotherapy. However, a gap remains in the exploration of the immunosenescence microenvironment in pancreatic cancer. In this study, we identified two immunosenescence-associated isoforms (IMSP1 and IMSP2), with consequential differences in prognosis and immune cell infiltration. We constructed the MLIRS score, a hazard score system with robust prognostic performance (area under the curve, AUC = 0.91), based on multiple machine learning algorithms (101 cross-validation methods). Patients in the high MLIRS score group had worse prognosis (P < 0.0001) and lower abundance of immune cell infiltration. Conversely, the low MLIRS score group showed better sensitivity to chemotherapy and immunotherapy. Additionally, our MLIRS system outperformed 68 other published signatures. We identified the immunosenescence microenvironmental windsock GLUT1 with certain co-expression properties with immunosenescence markers. We further demonstrated its positive modulation ability of proliferation, migration, and gemcitabine resistance in pancreatic cancer cells. To conclude, our study focused on training of composite machine learning algorithms in multiple datasets to develop a robust machine learning modeling system based on immunosenescence and to identify an immunosenescence-related microenvironment windsock, providing direction and guidance for clinical prediction and application.