Design of a Single-Atom In-N3-S site to Modulate Exciton Behavior in Carbon Nitride for Enhanced Photocatalytic Performance.

Rational tailoring of the local coordination environment of single atoms has demonstrated a significant impact on the electronic state and catalytic performance, but the development of catalysts beyond noble/transition metals is profoundly significant and highly desired. Herein, the main-group metal indium (In) single atom is immobilized on sulfur-doped porous carbon nitride nanosheets (In@CNS) in the form of three nitrogen atoms coordinated with one sulfur atom (In-N3-S). Both theoretical calculations and advanced characterization investigations clearly elucidated that the single-atomic In-N3-S structures on In@CNS are powerful in promoting the dissociation of excitons into more free carriers as well as the charge separation, synergistically elevating electron concentration by 2.19 times with respect to pristine CNS. Meanwhile, the loading of In single atoms on CNS is responsible for altering electronic structure and lowering the Gibbs free energy for hydrogen adsorption. Consequently, the optimized In@CNS-5.0 exhibited remarkable photocatalytic performance, remarkable water-splitting and tetracycline hydrochloride degradation. The H2 production achieved to 10.11 mmol h-1g-1 with a notable apparent quantum yield of 19.70% at 400 nm and remained at 10.40% at 420 nm. These findings open a new perspective for in-depth comprehending the effect of the main-group metal single-atom coordination environment on promoting photocatalytic performance.

Self-Healing and Wide Temperature-Tolerant Cellulose-Based Eutectogels for Reversible Humidity Detection.

A kind of ionic conductive gel (also named eutectogel) is developed from an inorganic salt (ZnCl2)-based deep eutectic solvent (DES). The ternary DES consists of ZnCl2, acrylic acid, and water, and cotton linter cellulose is introduced into the DES system to tailor its mechanical and conductive properties. Enabled by the extensive hydrogen bonds and ion-dipole interactions, the obtained eutectogel displays superior ionic conductivity (0.33 S/m), high stretchability (up to 2050%), large tensile strength (1.82 MPa), and wide temperature tolerance (-40 to 60 °C). In particular, the water-induced coordination interactions can tune the strength of hydrogen/ionic bonds in the eutectogels, imparting them with appealing humidity sensing ability in complex and extreme conditions.

The role and regulation of SIRT1 in pulmonary fibrosis.

Pulmonary fibrosis (PF) is a progressive and fatal lung disease with high incidence and a lack of effective treatment, which is a severe public health problem. PF has caused a huge socio-economic burden, and its pathogenesis has become a research hotspot. SIRT1 is a nicotinamide adenosine dinucleotide (NAD)-dependent sirtuin essential in tumours, Epithelial mesenchymal transition (EMT), and anti-aging. Numerous studies have demonstrated after extensive research that it is crucial in preventing the progression of pulmonary fibrosis. This article reviews the biological roles and mechanisms of SIRT1 in regulating the progression of pulmonary fibrosis in terms of EMT, oxidative stress, inflammation, aging, autophagy, and discusses the potential of SIRT1 as a therapeutic target for pulmonary fibrosis, and provides a new perspective on therapeutic drugs and prognosis prospects.

Effect of cesarean section on the risk of autism spectrum disorders/attention deficit hyperactivity disorder in offspring: a meta-analysis.

PURPOSE: This study was conducted to investigate the relationship between cesarean section (CS) offspring and autism spectrum disorders (ASD)/attention deficit hyperactivity disorder (ADHD). METHODS: Searching of the databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies on the relationship between mode of delivery and ASD/ADHD until August 2022. The primary outcome was the incidence of ASD/ADHD in the offspring. RESULTS: This meta-analysis included 35 studies (12 cohort studies and 23 case-control studies). Statistical results showed a higher risk of ASD (odds ratio (OR) = 1.25, P < 0.001) and ADHD (OR = 1.11, P < 0.001) in CS offspring compared to the VD group. Partial subgroup analysis showed no difference in ASD risk between CS and VD offspring in sibling-matched groups (OR = 0.98, P = 0.625). The risk of ASD was higher in females (OR = 1.66, P = 0.003) than in males (OR = 1.17, P = 0.004) in the CS offspring compared with the VD group. There was no difference in the risk of ASD between CS under regional anesthesia group and VD group (OR = 1.07, P = 0.173). However, the risk of ASD was higher in the CS offspring under general anesthesia than in the VD offspring (OR = 1.62, P < 0.001). CS offspring developed autism (OR = 1.38, P = 0.011) and pervasive developmental disorder-not otherwise specified (OR = 1.46, P = 0.004) had a higher risk than VD offspring, but there was no difference in Asperger syndrome (OR = 1.19, P = 0.115). Offspring born via CS had a higher incidence of ADHD in different subgroup analyses (sibling-matched, type of CS, and study design). CONCLUSIONS: In this meta-analysis, CS was a risk factor for ASD/ADHD in offspring compared with VD.

Rapid Determination of Major Compounds in the Ethanol Extract of Geopropolis from Malaysian Stingless Bees, Heterotrigona itama, by UHPLC-Q-TOF/MS and NMR.

A reliable, rapid analytical method was established for the characterization of constituents of the ethanol extract of geopropolis (EEGP) produced by Malaysian stingless bees-Heterotrigona itama-by combining ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Based on known standards, the online METLIN database, and published literature, 28 compounds were confirmed. Phenolic acids, flavones, triterpenes and phytosterol were identified or tentatively identified using characteristic diagnostic fragment ions. The results indicated that terpenoids were the main components of EEGP, accompanied by low levels of phenolic acids, flavonoids, and phytosterol. Two major components were further purified by preparative high-performance liquid chromatography (PHPLC) and identified by nuclear magnetic resonance (NMR) as 24(E)-cycloart-24-ene-26-ol-3-one and 20-hydroxy-24-dammaren-3-one. These two triterpenes, confirmed in this geopropolis for the first time, are potential chemical markers for the identification of geopropolis from Malaysian stingless bees, H. itama.

Thermo-responsive properties driven by hydrogen bonding in aqueous cationic gemini surfactant systems.

A series of unexpected thermo-responsive phenomena were discovered in an aqueous solution of the cationic gemini surfactant, 2-hydroxypropyl-1,3-bis(alkyldimethylammonium chloride) (n-3(OH)-n(2Cl), n = 14, 16), in the presence of an inorganic salt. The viscosity change trend for the 14-3(OH)-14(2Cl) system was investigated in the 20-40 °C temperature range. As the temperature increased, the viscosity of the solution first decreased to a minimum point corresponding to 27 °C, and then increased until a maximum was reached, after which the viscosity decreased again. In the 16-3(OH)-16(2Cl) system, the gelling temperature (T(gel)) and viscosity changes upon heating were similar to those in the 14-3(OH)-14(2Cl) system above 27 °C. The reversible conversion of elastic hydrogel to wormlike micelles in the aqueous solution of the 16-3(OH)-16(2Cl) system in the presence of an inorganic salt was observed at relatively low temperatures. Various techniques were used to study and verify the phase-transition processes in these systems, including rheological measurements, cryogenic transmission electron microscopy (cryo-TEM), electric conductivity, and differential scanning calorimetry. The abovementioned phenomena were explained by the formation and destruction of intermolecular hydrogen bonds, and the transition mechanisms of the aggregates were analyzed accordingly.

A designed ZrOCl2/ethylene glycol deep eutectic solvent for efficient lignocellulose valorization.

Deep eutectic solvents (DESs) hold great potential in biorefining because they can efficiently deconstruct the recalcitrant structure of lignocellulose. In particular, inorganic salts with Lewis acids have been proven to be effective at cleaving lignin-carbohydrate complexes. Herein, a Zr-based DES system composed of metal chloride hydrate (ZrOCl2·8H2O) and ethylene glycol (EG) was designed and used for poplar powder pretreatment. Zr4+-based salts provide sufficient acidity for lignocellulose depolymerization. The acidity of the DES was analysed by the Kamlet-Taft solvatochromic parameter, and the results demonstrated that the acidity can be regulated by the DES composition. Under the optimum conditions (ZrOCl2·8H2O:EG molar ratio of 1:2), the DES pretreatment removes nearly 100 % hemicellulose and 94.7 % lignin. The recovered lignin exhibited a low polydispersity of 1.7. The cellulose residues deliver an efficiency of 94.4 % upon enzymatic digestion. Moreover, the DES can be easily recovered with high yield and purity, and the recycled DES still maintains high delignification and enzymatic hydrolysis efficiencies. The proposed DES pretreatment technology is promising for biomass valorization.

Cellulose-reinforced highly stretchable and adhesive eutectogels as efficient sensors.

A cellulose-reinforced eutectogel was constructed by deep eutectic solvent (DES) and cotton linter cellulose. Cellulose was dispersed in the ternary DES consisting of acrylic acid, choline chloride and AlCl3·6H2O. The photoinitiator was then introduced into the system to in situ polymerize acrylic acid monomer to form transparent and ionic conductive eutectogels while keeping all the DES. The crosslinks formed by Al3+ induced ionic bonds and reversible links formed by hydrogen bonds give the eutectogels high stretchability (3200 ± 200 % tensile strain), self-adhesive (52.1 kPa to glass), self-healing and good mechanical strength (670 kPa). The eutectogels were assembled into sensors and epidermal patch electrodes that demonstrated high quality human motion sensing and physiological signal detection (electrocardiogram and electromyography). This work provides a facile way to design flexible electronics for sensing.

Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma.

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.

Immune cell senescence and exhaustion promote the occurrence of liver metastasis in colorectal cancer by regulating epithelial-mesenchymal transition.

BACKGROUND: Liver metastasis (LM) stands as a primary cause of mortality in metastatic colorectal cancer (mCRC), posing a significant impediment to long-term survival benefits from targeted therapy and immunotherapy. However, there is currently a lack of comprehensive investigation into how senescent and exhausted immune cells contribute to LM. METHODS: We gathered single-cell sequencing data from primary colorectal cancer (pCRC) and their corresponding matched LM tissues from 16 mCRC patients. In this study, we identified senescent and exhausted immune cells, performed enrichment analysis, cell communication, cell trajectory, and cell-based in vitro experiments to validate the results of single-cell multi-omics. This process allowed us to construct a regulatory network explaining the occurrence of LM. Finally, we utilized weighted gene co-expression network analysis (WGCNA) and 12 machine learning algorithms to create prognostic risk model. RESULTS: We identified senescent-like myeloid cells (SMCs) and exhausted T cells (TEXs) as the primary senescent and exhausted immune cells. Our findings indicate that SMCs and TEXs can potentially activate transcription factors downstream via ANGPTL4-SDC1/SDC4, this activation plays a role in regulating the epithelial-mesenchymal transition (EMT) program and facilitates the development of LM, the results of cell-based in vitro experiments have provided confirmation of this conclusion. We also developed and validated a prognostic risk model composed of 12 machine learning algorithms. CONCLUSION: This study elucidates the potential molecular mechanisms underlying the occurrence of LM from various angles through single-cell multi-omics analysis in CRC. It also constructs a network illustrating the role of senescent or exhausted immune cells in regulating EMT.

Adverse drug events associated with fluorouracil use in patients with metastatic colorectal cancer: a real-world pharmacovigilance study based on the FDA adverse event reporting system.

BACKGROUND: Fluorouracil (5-FU) is widely used to treat metastatic colorectal cancer (mCRC), but real-world safety data is limited. Our study aimed to evaluate 5-FU's safety profile in a large mCRC population using the FAERS database. RESEARCH DESIGN AND METHODS: We conducted disproportionality analyses to identify adverse drug events associated with 5-FU use in mCRC patients from 2004 to 2023. Subgroup analyses, gender difference analyses, and logistic regression were also performed. RESULTS: We identified 1,458 reports with 5-FU as the primary suspected drug, with males accounting for 48.8% of reports. Gastrointestinal disorders were the most common adverse event (864 cases), while pregnancy-related conditions showed the strongest signal intensity (ROR = 2.97). We found 19 preferred terms with positive signals, including ischemic hepatitis (ROR = 59.32), blood iron increased (ROR = 59.32), and stress cardiomyopathy (ROR = 51.94). Males were more susceptible to weight loss and skin toxicity. Most adverse events occurred within the first month of 5-FU administration. CONCLUSION: Our study provides a comprehensive analysis of 5-FU's safety profile in mCRC patients, helping healthcare professionals mitigate risks in clinical practice.

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

Background and objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD. Methods: We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Results: Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively. Conclusion: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Concerting magnesium implant degradation facilitates local chemotherapy in tumor-associated bone defect.

Effective management of malignant tumor-induced bone defects remains challenging due to severe systemic side effects, substantial tumor recurrence, and long-lasting bone reconstruction post tumor resection. Magnesium and its alloys have recently emerged in clinics as orthopedics implantable metals but mostly restricted to mechanical devices. Here, by deposition of calcium-based bilayer coating on the surface, a Mg-based composite implant platform is developed with tailored degradation characteristics, simultaneously integrated with chemotherapeutic (Taxol) loading capacity. The delicate modulation of Mg degradation occurring in aqueous environment is observed to play dual roles, not only in eliciting desirable osteoinductivity, but allows for modification of tumor microenvironment (TME) owing to the continuous release of degradation products. Specifically, the sustainable H2 evolution and Ca2+ from the implant is distinguished to cooperate with local Taxol delivery to achieve superior antineoplastic activity through activating Cyt-c pathway to induce mitochondrial dysfunction, which in turn leads to significant tumor-growth inhibition in vivo. In addition, the local chemotherapeutic delivery of the implant minimizes toxicity and side effects, but markedly fosters osteogenesis and bone repair with appropriate structure degradation in rat femoral defect model. Taken together, a promising intraosseous administration strategy with biodegradable Mg-based implants to facilitate tumor-associated bone defect is proposed.

Mg-based implants with a sandwiched composite coating simultaneously facilitate antibacterial and osteogenic properties.

Insufficient antibacterial effects and over-fast degradation are the main limitations of magnesium (Mg)-based orthopedic implants. In this study, a sandwiched composite coating containing a triclosan (TCS)-loaded poly(lactic acid) (PLA) layer inside and brushite (DCPD) layer outside was prepared on the surface of the Mg-Nd-Zn-Zr (denoted as JDBM) implant. In vitro degradation tests revealed a remarkable improvement in the corrosion resistance and moderate degradation rate. The drug release profile demonstrated a controllable and sustained TCS release for at least two weeks in vitro. The antibacterial rates of the implant were all over 99.8% for S. aureus, S. epidermidis, and E. coli, demonstrating superior antibacterial effects. Additionally, this coated JDBM implant exhibited no cytotoxicity but improved cell adhesion and proliferation, indicating excellent cytocompatibility. In vivo assays were conducted by implant-related femur osteomyelitis and osseointegration models in rats. Few bacteria were attached to the implant surface and the surrounding bone tissue. Furthermore, the coated JDBM implant exhibited more new bone formation than other groups due to the synergistic biological effects of released TCS and Mg2+, revealing excellent osteogenic ability. In summary, the JDBM implant with the sandwiched composite coating could significantly enhance the antibacterial activities and osteogenic properties simultaneously by the controllable release of TCS and Mg2+, presenting great potential for clinical transformation.

Detection, production, modification, and application of arylsulfatases.

Arylsulfatase is a subset of sulfatase which catalyzes the hydrolysis of aryl sulfate ester. Arylsulfatase is widely distributed among microorganisms, mammals and green algae, but the arylsulfatase-encoding gene has not yet been found in the genomes of higher plants so far. Arylsulfatase plays an important role in the sulfur flows between nature and organisms. In this review, we present the maturation and catalytic mechanism of arylsulfatase, and the recent literature on the expression and production of arylsulfatase in wild-type and engineered microorganisms, as well as the modification of arylsulfatase by genetic engineering are summarized. We focus on arylsulfatases from microbial origin and give an overview of different assays and substrates used to determine the arylsulfatase activity. Furthermore, the researches about arylsulfatase application on the field of agar desulfation, soil sulfur cycle and soil evaluation are also discussed. Finally, the perspectives concerning the future research on arylsulfatase are prospected.

A new hybrid PM[Formula: see text] volatility forecasting model based on EMD and machine learning algorithms.

In recent years, the frequent occurrence of air pollution incidents has seriously affected people's health and life. Therefore, PM[Formula: see text], as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM[Formula: see text] volatility makes the PM[Formula: see text] prediction content perfect, which is an important aspect of PM[Formula: see text] concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM[Formula: see text] volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM[Formula: see text] volatility analysis.

A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems.

The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOPSO. The enhanced grid mechanism involved in EGC-CMOPSO is designed to locate superior Pareto optimal solutions. Subsequently, a hierarchical-based clustering is established on the grid for improving the accuracy rate of the grid selection. Due to the adaptive division of clustering centers, EGC-CMOPSO is applicable for solving MOPs with various Pareto front (PF) shapes. Particularly, the inferior solutions are discarded and the leading particles are identified by the comprehensive ranking of particles in each cluster. Finally, the selected leading particles compete against each other, and the winner guides the update of the current particle. The proposed EGC-CMOPSO and the eight latest multi-objective optimization algorithms are performed on 21 test problems. The experimental results validate that the proposed EGC-CMOPSO is capable of handling multi-objective optimization problems (MOPs) and obtaining superior performance on both convergence and diversity.

A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization.

In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) for high-dimensional EMOPs which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs.

Does proton pump inhibitors use increase risk of digestive tumors?: A 2-sample Mendelian randomization study.

The objective of this study was to explore the causal relationship between the use of proton pump inhibitors (PPIs) and 16 types of digestive system tumors. We utilized a 2-sample Mendelian randomization (MR) approach to investigate this relationship. We obtained exposure and outcome data from the UK Biobank and the Finland Biobank, respectively. The genetic data used in the analysis were derived from genome-wide association studies (GWAS) studies conducted on European populations. We screened single nucleotide polymorphisms significantly associated with the use of omeprazole, a commonly used PPIs, as instrumental variables. We then performed MR analyses using the inverse variance weighting (IVW) method, MR-Egger regression, and the weighted median method to evaluate the causal effect of omeprazole use on the 16 types of digestive system tumors. Our MR analysis revealed a significant causal relationship between the use of omeprazole and pancreatic malignancies, but not with any other types of digestive system tumors. The IVW analysis showed an odds ratio of 4.33E-05 (95%CI: [4.87E-09, 0.38], P = .03) and the MR-Egger analysis showed an odds ratio of 5.81E-11 (95%CI: [2.82E-20, 0.12], P = .04). We found no significant heterogeneity or pleiotropy, and sensitivity analysis confirmed the robustness of our results. Furthermore, statistical power calculations suggested that our findings were reliable. Conclusion The use of PPIs is a protective factor for pancreatic malignancies, but no causal relationship has been found with other digestive system tumors.

Oxeiptosis core genes and their multi-omics analysis in hepatocellular carcinoma.

Oxeiptosis is a recently discovered caspase-independent, non-inflammatory programmed cell death modality. Current studies suggest that oxeiptosis has crucial effects on biological processes in a variety of diseases. However, the mechanism of oxeiptosis in hepatocellular carcinoma (HCC) remains unclear and no relevant studies have been published. Therefore, this study is intended to investigate the mechanism and prognostic role of oxeiptosis-related genes in HCC. We explored the mechanisms and molecular phenotypes underlying the role of oxeiptosis in HCC through multi-omics analysis. Firstly, we obtained RNA-sequencing and clinical data from public database and divided the samples into trial and validation cohorts in subsequent analyses. We then screened oxeiptosis core genes (OCGs) and screened prognosis-related genes. Based on different molecular markers, we identified the molecular phenotypes of HCC, and the potential OCGs molecular mechanisms were explored. Subsequently, we construct a prognostic prediction system for HCC. Finally, we analyzed the tumor microenvironment and the immune escape phenomenon. We screened a total of 69 OCGs, most of which were prognostic risk factors for HCC. A majority of OCGs were enriched in cell cycle regulation and mitotic processes, which were related to both tumor cell proliferation and death. We identified 2 different molecular typing options with significant differences in prognosis, function, and signaling pathway enrichment between different molecular subtypes. The prognostic prediction model combined with molecular phenotypes and had a good predictive effect. Finally, we found CD4 + T-cell exhaustion in samples with specific molecular phenotypes. Through multi-omics analysis of OCGs, we not only revealed the possible molecular mechanisms of OCGs in HCC but also provided a prognostic prediction system for clinical application through molecular typing and risk scoring model. Meanwhile, we found immune escape mechanisms in HCC.