The relationship between glycemic risk and longitudinal changes in total physiological atherosclerotic burden in patients with coronary artery disease.

Background: The effects of glycemic status on coronary physiology have not been well evaluated. This study aimed to investigate changes in coronary physiology by using angiographic quantitative flow ratio (QFR), and their relationships with diabetes mellitus (DM) and glycemic control status. Methods: This retrospective cohort study included 530 patients who underwent serial coronary angiography (CAG) measurements between January 2016 and December 2021 at Tongji Hospital of Tongji University. Based on baseline and follow-up angiograms, 3-vessel QFR (3V-QFR) measurements were performed. Functional progression of coronary artery disease (CAD) was defined as a change in 3V-QFR (Δ3V-QFR = 3V-QFRfollow-up - 3V-QFRbaseline) ≤-0.05. Univariable and multivariable logistic regression analyses were applied to identify the independent predictors of coronary functional progression. Subgroup analysis according to diabetic status was performed. Results: During a median interval of 12.1 (10.6, 14.3) months between the two QFR measurements, functional progression was observed in 169 (31.9%) patients. Follow-up glycosylated hemoglobin (HbA1c) was predictive of coronary functional progression with an area under the curve (AUC) of 0.599 [95% confidence interval (CI): 0.546-0.651; P<0.001] in the entire population. Additionally, the Δ3V-QFR values were significantly lower in diabetic patients with HbA1c ≥7.0% compared to those with well-controlled HbA1c or non-diabetic patients [-0.03 (-0.09, 0) vs. -0.02 (-0.05, 0.01) vs. -0.02 (-0.05, 0.02); P=0.002]. In a fully adjusted multivariable logistics analysis, higher follow-up HbA1c levels were independently associated with progression in 3V-QFR [odds ratio (OR), 1.263; 95% CI: 1.078-1.479; P=0.004]. Furthermore, this association was particularly strong in diabetic patients (OR, 1.353; 95% CI: 1.082-1.693; P=0.008) compared to patients without DM. Conclusions: Among patients with established CAD, on-treatment HbA1c levels were independently associated with progression in physiological atherosclerotic burden, especially in patients with DM.

Injectable isoniazid-loaded bone cement based on hydrazone bonds achieving long-term release and decent mechanical properties.

A robust and easily manufactured high-strength and long-term release hydrazone-based isoniazid acrylic (HIA) bone cement is reported. The mechanical strength of HIA bone cement is similar to that of normal polymethyl methacrylate (PMMA) bone cement, far surpassing that of traditional isoniazid-containing antibiotic-loaded bone cement (INH bone cement). Isoniazid is connected to the bone cement through bioorthogonal hydrazone chemistry, and it possesses release properties superior to those of INH bone cement, allowing for the sustained release of isoniazid for up to 12 weeks. In vivo and in vitro studies also indicate that HIA cement exhibits better biocompatibility than INH bone cement. The results of this study not only signify progress in the realm of antimicrobial bone cement for addressing bone tuberculosis but also enhance our capacity to create and comprehend high-performing antimicrobial bone cement.

Correction: Co-expression of GR79 EPSPS and GAT generates high glyphosate-resistant alfalfa with low glyphosate residues.

[This corrects the article DOI: 10.1007/s42994-023-00119-3.].

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates.

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Application of PRI-E-a combined learning method in oral and maxillofacial oncology education.

The traditional lecture-based learning (LBL) method is facing great challenges due to its low efficiency and single proceeding form. We designed a PRI-E learning mode that combined and modified problem-based, case-based, and evidence-based learning with a step-by-step approach. We evaluated the practical learning outcomes of using the PRI-E mode by comparing it with traditional lecture-based learning in oral and maxillofacial oncology education. "PRI-E" consists of the first letters of the English words Passion, Research, Innovation, and Education, and it means "the best Education". This prospective randomized controlled trial included 40 participants. We evenly divided the participants into the PRI-E (n = 20) and LBL group (n = 20) based on the entrance test scores. The same staff group designed and then taught the learning content with different group measures. The evaluation included the final test scores and questionnaire assessments. Without affecting the examination results, the PRI-E teaching method was more satisfactory and popular with participants in terms of ability development and classroom participation. Enacting the PRI-E teaching method required more time, but this did not affect its popularity among the participants. Compared with the LBL learning mode, the PRI-E learning mode was more organized and efficient in oral and maxillofacial oncology education without affecting academic performance. This model has a high degree of satisfaction, which is conducive to training students' comprehensive ability.

Transcriptomic Analysis of Self-Incompatibility in Alfalfa.

Alfalfa (Medicago sativa L.) is an important forage crop worldwide, but molecular genetics and breeding research in this species are hindered by its self-incompatibility (SI). Although the mechanisms underlying SI have been extensively studied in other plant families, SI in legumes, including alfalfa, remains poorly understood. Here, we determined that self-pollinated pollen tubes could germinate on the stigma of alfalfa, grow through the style, and reach the ovarian cavity, but the ovules collapsed ~48 h after self-pollination. A transcriptomic analysis of dissected pistils 24 h after self-pollination identified 941 differently expressed genes (DEGs), including 784 upregulated and 157 downregulated genes. A gene ontology (GO) analysis showed that the DEGs were highly enriched in functions associated with the regulation of pollen tube growth and pollen germination. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that pentose and glucuronate interconversion, plant hormone signal transduction, the spliceosome, and ribosomes might play important roles in SI. Our co-expression analysis showed that F-box proteins, serine/threonine protein kinases, calcium-dependent protein kinases (CDPKs), bHLHs, bZIPs, and MYB-related family proteins were likely involved in the SI response. Our study provides a catalog of candidate genes for further study to understand SI in alfalfa and related legumes.

Soil warming-induced reduction in water content enhanced methane uptake at different soil depths in a subtropical forest.

Global warming can significantly impact soil CH4 uptake in subtropical forests due to changes in soil moisture, temperature sensitivity of methane-oxidizing bacteria (MOB), and shifts in microbial communities. However, the specific effects of climate warming and the underlying mechanisms on soil CH4 uptake at different soil depths remain poorly understood. To address this knowledge gap, we conducted a soil warming experiment (+4 °C) in a natural forest. From August 2020 to October 2021, we measured soil temperature, soil moisture, and CH4 uptake rates at four different soil depths: 0-10 cm, 10-20 cm, 20-40 cm, and 40-60 cm. Additionally, we assessed the soil MOB community structure and pmoA gene (with qPCR) at the 0-10 and 10-20 cm depths. Our findings revealed that warming significantly enhanced soil net CH4 uptake rate by 12.28 %, 29.51 %, and 61.05 % in the 0-10, 20-40, and 40-60 cm soil layers, respectively. The warming also led to reduced soil moisture levels, with more pronounced reductions observed at the 20-40 cm depth compared to the 0-20 cm depth. At the 0-10 cm depth, warming increased the relative abundance of upland soil cluster α (a type of MOB) and decreased the relative abundance of Methylocystis, but it did not significantly increase the pmoA gene copies. Our structural equation model analysis indicated that warming directly regulated soil CH4 uptake rate through the decrease in soil moisture, rather than through changes in the pmoA gene and MOB community structure at the 0-20 cm depth. In summary, our results demonstrate that warming enhances soil CH4 uptake at different depths, with soil moisture playing a crucial role in this process. Under warming conditions, the drier soil pores allow for better CH4 penetration, thereby promoting more efficient activity of MOB.

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.

BACKGROUND: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS: In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS: This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

Niacin supplementation attenuates the regression of three-dimensional capillary architecture in unloaded female rat skeletal muscle.

Inactivity can lead to muscle atrophy and capillary regression in skeletal muscle. Niacin (NA), known for inducing hypermetabolism, may help prevent this capillary regression. In this study involving adult female Sprague-Dawley rats, the animals were randomly assigned to one of four groups: control (CON), hindlimb unloading (HU), NA, and HU with NA supplementation (HU + NA). For a period of 2 weeks, the rats in the HU and HU + NA groups underwent HU, while those in the NA and HU + NA groups received NA (750 mg/kg) twice daily through oral administration. The results demonstrated that HU lowered capillary number, luminal diameter, and capillary volume, as well as decreased succinate dehydrogenase activity, slow fiber composition, and PGC-1α expression within the soleus muscle. However, NA supplementation prevented these alterations in capillary structure due to unloading by stimulating PGC-1α factors and inhibiting mitochondrial dysfunction. Therefore, NA supplementation could serve as a potential therapeutic approach for preserving the capillary network and mitochondrial metabolism of muscle fibers during periods of inactivity.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Impact of coexisting type 2 diabetes mellitus on the urinary microbiota of kidney stone patients.

Objectives: Type 2 diabetes mellitus (T2DM) commonly complicates kidney stone disease (KSD). Our objective is to investigate the variations in the urinary microbiota between individuals with KSD alone and those with KSD plus T2DM. This exploration could have implications for disease diagnosis and treatment strategies. Methods: During lithotripsy, a ureterscope was employed, and 1 mL of urine was collected from the renal pelvis after bladder disinfection. Sequencing targeting the V3-V4 hypervariable region was performed using the 16S rRNA and Illumina Novaseq platform. Results: The Shannon index showed a significant decrease in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.041). Principal Coordinate Analysis (PCoA) demonstrated a distinct bacterial community in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.027). The abundance of Sphingomonas, Corynebacterium, and Lactobacillus was significantly higher in the KSD plus T2DM group than in the KSD-only group (false discovery rate < 0.05). Furthermore, Enhydrobacter, Chryseobacterium, and Allobaculum were positively correlated with fasting blood glucose and HbA1c values (P < 0.05). Conclusions: The urinary microbiota in the renal pelvis exhibits differences between patients with KSD plus T2DM and those with KSD alone. Further studies employing animal models are necessary to validate these distinctions, potentially paving the way for therapeutic developments based on the urinary microbiota.

Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter.

BACKGROUND: Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized the significant role of cancer-associated fibroblasts (CAFs) in OC development and progression. METHODS: Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated the relationship between FGF7 expression and various clinical parameters. A series of in vitro experiments were undertaken to evaluate the effect of CAFs-derived FGF7 on OC cell activities, such as proliferation, migration, and invasion. Single-cell transcriptomic analysis was also conducted to elucidate the interaction between FGF7 and its receptor. Detailed mechanistic investigations sought to clarify the pathways through which FGF7 fosters OC progression. RESULTS: Our findings indicate that higher FGF7 levels correlate with advanced tumor stages, increased vascular invasion, and poorer prognosis. CAFs-derived FGF7 significantly enhanced OC cell proliferation, migration, and invasion. Single-cell analysis and in vitro studies revealed that CAFs-derived FGF7 inhibits the ubiquitination and degradation of hypoxia-inducible factor 1 alpha (HIF-1α) via FGFR2 interaction. Activation of the FGF7/HIF-1α pathway resulted in the upregulation of mesenchymal markers and downregulation of epithelial markers. Importantly, in vivo treatment with neutralizing antibodies targeting CAFs-derived FGF7 substantially reduced tumor growth. CONCLUSION: Neutralizing FGF7 in the medium or inhibiting HIF-1α signaling reversed the effects of FGF7-mediated EMT, emphasizing the dependence of FGF7-mediated EMT on HIF-1α activation. These findings suggest that targeting the FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities to intervene in OC progression.

A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens.

Understanding the subcellular localization of lncRNAs is crucial for comprehending their regulation activities. The conventional detection of lncRNA subcellular location usually uses in situ detection techniques, which are resource intensive. Some machine learning-based algorithms have been proposed for lncRNA subcellular location prediction in mammals. However, due to the low level of conservation of lncRNA sequence, the performance of cross-species models remains unsatisfactory. In this study, we curated a novel dataset containing subcellular location information of lncRNAs in Homo sapiens. Subsequently, based on the BERT pre-trained language algorithm, we developed a model for lncRNA subcellular location prediction. Our model achieved a micro-average area under the receiver operating characteristic (AUROC) of 0.791 on the training set and an AUROC of 0.700 on the testing nucleus set. Additionally, we conducted cross-species validation and motif discovery to further investigate underlying patterns. In summary, our study provides valuable guidance and computational analysis tools for exploring the mechanisms of lncRNA subcellular localization and the dynamic spatial changes of RNA in abnormal physiological states.

Sequence-based design and construction of synthetic nanobody library.

Nanobody (Nb), the smallest antibody fragments known to bind antigens, is now widely applied to various studies, including protein structure analysis, bioassay, diagnosis, and biomedicine. The traditional approach to generating specific nanobodies involves animal immunization which is time-consuming and expensive. As the understanding of the antibody repertoire accumulation, the synthetic library, which is devoid of animals, has attracted attention widely in recent years. Here, we describe a synthetic phage display library (S-Library), designed based on the systematic analysis of the next-generation sequencing (NGS) of nanobody repertoire. The library consists of a single highly conserved scaffold (IGHV3S65*01-IGHJ4*01) and complementary determining regions of constrained diversity. The S-Library containing 2.19 × 108 independent clones was constructed by the one-step assembly and rapid electro-transformation. The S-Library was screened against various targets (Nb G8, fusion protein of Nb G8 and green fluorescent protein, bovine serum albumin, ovalbumin, and acetylcholinesterase). In comparison, a naïve library (N-Library) from the source of 13 healthy animals was constructed and screened against the same targets as the S-Library. Binders were isolated from both S-Library and N-Library. The dynamic affinity was evaluated by the biolayer interferometry. The data confirms that the feature of the Nb repertoire is conducive to reducing the complexity of library design, thus allowing the S-Library to be built on conventional reagents and primers.

Advancing Lithium Battery Performance through Porous Conductive Polyaniline-Modified Graphene Composites Additive.

This study aimed to enhance lithium battery performance through the utilization of porous conductive polyaniline-modified graphene composites (PMGCs). Given the growing importance of green energy, coupled with the development of lithium-ion battery systems and electric vehicles, achieving high-speed charge and discharge performance is imperative. Traditional approaches involve incorporating additives like carbon nanotubes and graphene into electrodes to improve conductivity, but they encounter challenges related to cost and aggregation issues. In this study, polyaniline (PANI), a cost-effective, stable, and conductive polymer, was explored. PMGCs was formed by employing ammonium persulfate (APS) as an oxidant during PANI polymerization, simultaneously serving as a surface modifier for graphene. This study systematically investigated the impacts of varying amounts of PMGCs on lithium-ion battery electrodes by assessing the reductions in internal resistance, aging effects, different charge and discharge rates, and cycle performance. The PMGC exhibited a porous structure formed by nanoscale PANI intertwining on graphene. Various measurements, including FT-IR, TGA, Raman spectroscopy, and battery performance assessments, confirmed the successful synthesis and positive effects of PMGCs. The results indicated that a 0.5% addition of PMGC led to a reduced internal resistance and enhanced fast-charge and discharge capacity. However, an excessive amount of PMGCs adversely affected aging and self-discharge. This study provides valuable insights into optimizing the PMGC content for improved lithium battery performance, presenting potential advancements in energy storage systems and electric vehicles.

Simultaneous management of aortic and mitral regurgitation through one-stage transcatheter aortic valve replacement and transcatheter edge-to-edge repair: case report.

This case report presents a 72-year-old male patient who presented with exertional dyspnea for over 10 years, which had progressively worsened over the past 4 months. Transthoracic echocardiography revealed severe aortic and mitral regurgitation, with a left ventricular ejection fraction of 37% and a left ventricular end-diastolic diameter of 64 mm. Despite receiving long-term optimal medical management, there was no improvement in symptoms or severity of valvular regurgitation. Given the relatively high surgical risk associated with double valve replacement in this elderly patient and his preference for minimally invasive procedures, a one-stage transapical aortic valve replacement and transcatheter mitral valve repair using the edge-to-edge technique were planned. The patient was discharged 8 days post-procedure without any complications. At 1-month follow-up, the patient's New York Heart Association (NYHA) functional class had improved to grade II.

Bladder-sparing treatment using tislelizumab combined with gemcitabine/cisplatin in selected patients with muscle-invasive bladder cancer: a real-world study.

PURPOSE: To retrospectively evaluate the tislelizumab-based chemoimmunotherapy combined with gemcitabine/cisplatin for bladder-sparing in patients with muscle-invasive bladder cancer (MIBC). METHODS: Forty-five patients who received bladder-sparing treatment or radical cystectomy (RC) for MIBC (cT2-T4a, NxM0) were retrospectively enrolled. All patients received maximal transurethral resection of bladder tumor (mTURBT), followed by four cycles of chemo-immunotherapy with tislelizumab (PD-L1 inhibitor), gemcitabine, and cisplatin. Clinical efficacy was evaluated to compare the benefit of bladder-sparing treatment on clinical CR (cCR) and RC for non-cCR patients. The primary outcomes were bladder intact disease-free survival (BIDFS) and overall survival (OS), and the secondary outcomes were adverse effects. The PD-L1 status and molecular subtypes of tumors were analyzed. RESULTS: The overall survival rate was 88.8% (95%CI: 79.6%, 98.0%) at 12 months, 85.7% (95%CI: 74.9%, 96.5%) at 18 months, and 66.6% (95%CI: 45.2%, 88.0%) at 24 months. Twenty-nine patients (64.4%) achieved cCR and their OS rate was 96.6% (95%CI: 89.9%, 100%). Sixteen patients were in the non-cCR group, and their OS rate was 75.0% (95%CI: 53.8%, 96.2%) at 12 months, 65.6% (95%CI: 40.3%, 90.9%) at 18 months, and 52.5% (95%CI: 21.9%, 83.1%) at 24 months. The BIDFS rate for patients who received bladder-sparing treatment was 96.0% (95%CI: 88.4%, 100%) from 12 to 24 months. Four patients (8.8%) were PD-L1 positive and 41 patients (91.2%) were PD-L1 negative. CONCLUSIONS: Our retrospective study of patients with MIBC suggests that tislelizumab-based neoadjuvant therapy was a safe and effective bladder-sparing treatment.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.

BACKGROUND: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear. METHODS: We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions. RESULTS: A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions. CONCLUSIONS: Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Harnessing innate immune pathways for therapeutic advancement in cancer.

The innate immune pathway is receiving increasing attention in cancer therapy. This pathway is ubiquitous across various cell types, not only in innate immune cells but also in adaptive immune cells, tumor cells, and stromal cells. Agonists targeting the innate immune pathway have shown profound changes in the tumor microenvironment (TME) and improved tumor prognosis in preclinical studies. However, to date, the clinical success of drugs targeting the innate immune pathway remains limited. Interestingly, recent studies have shown that activation of the innate immune pathway can paradoxically promote tumor progression. The uncertainty surrounding the therapeutic effectiveness of targeted drugs for the innate immune pathway is a critical issue that needs immediate investigation. In this review, we observe that the role of the innate immune pathway demonstrates heterogeneity, linked to the tumor development stage, pathway status, and specific cell types. We propose that within the TME, the innate immune pathway exhibits multidimensional diversity. This diversity is fundamentally rooted in cellular heterogeneity and is manifested as a variety of signaling networks. The pro-tumor effect of innate immune pathway activation essentially reflects the suppression of classical pathways and the activation of potential pro-tumor alternative pathways. Refining our understanding of the tumor's innate immune pathway network and employing appropriate targeting strategies can enhance our ability to harness the anti-tumor potential of the innate immune pathway and ultimately bridge the gap from preclinical to clinical application.