Results from a real-world study: a novel glycosyltransferase risk score for prognosis, tumor microenvironment phenotypes and immunotherapy in bladder cancer.

BACKGROUND: Although immunotherapy shows tremendous potential in the treatment of bladder cancer (BLCA), the overall prognosis and response rates to immunotherapy in BLCA remain suboptimal. METHODS: We performed an extensive evaluation of glycosyltransferase expression patterns in BLCA patients by analyzing 210 glycosyltransferase-related genes. Subsequently, we established correlations between these glycosyltransferase patterns, prognosis, and tumor microenvironment (TME) phenotypes. To offer personalized patient assessments, we developed a glycosyltransferase risk score that accurately predicts prognosis, TME phenotypes, and molecular subtypes. Importantly, we developed a RNA-seq cohort, named Xiangya cohort, to validate our results. RESULTS: Two distinct patterns of glycosyltransferase expression were identified, corresponding to inflamed and noninflamed TME phenotypes, and demonstrated the potential to predict prognosis. We developed and validated a comprehensive risk score that accurately predicted individual patient prognosis in the TCGA-BLCA cohort. Additionally, we constructed a nomogram that integrated the risk score with several key clinical factors. Importantly, this risk score was successfully validated in external cohorts, including the Xiangya cohort and GSE48075. Furthermore, we discovered a positive correlation between this risk score and tumor-infiltrating lymphocytes in both the TCGA-BLCA and Xiangya cohorts, suggesting that patients with a higher risk score exhibited an inflamed TME phenotype and were more responsive to immunotherapy. Finally, we observed that the high and low risk score groups were consistent with the luminal and basal subtypes of BLCA, respectively, providing further validation of the risk score's role in the TME in terms of molecular subtypes. CONCLUSIONS: Glycosyltransferase patterns exhibit distinct TME phenotypes in BLCA. Our comprehensive risk score provides a promising approach for prognostic prediction and assessment of immunotherapy efficacy, offering valuable guidance for precision medicine.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

Dichloroacetate Prevents Sepsis Associated Encephalopathy by Inhibiting Microglia Pyroptosis through PDK4/NLRP3.

Dichloroacetate (DCA), a pyruvate dehydrogenase kinase inhibitor, is often used to treat lactic acidosis and malignant tumors. Increasing studies have shown that DCA has neuroprotective effects. Here, we explored the role and mechanism of DCA in Sepsis associated encephalopathy (SAE). Single-cell analysis was used to determine the important role of PDK4 in SAE and identify the cell type. GO and GSEA analysis were used to determine the correlation between DCA and pyroptosis. Through LPS + ATP stimulation, a microglia pyroptosis model was established to observe the expression level of intracellular pyroptosis-related proteins under DCA intervention, and further detect the changes in intracellular ROS and JC-1. Additionally, a co-culture environment of microglia and neuron was simply constructed to evaluate the effect of DCA on activated microglia-mediated neuronal apoptosis. Finally, Novel object recognition test and the Morris water maze were used to explore the effect of DCA on cognitive function in mice from different groups after intervention. Based on the above experiments, this study concludes that DCA can improve the ratio of peripheral and central M1 macrophages, inhibit NLRP3-mediated pyroptosis through ROS and mitochondrial membrane potential (MMP). DCA can reduce neuron death caused by SAE and improve cognitive function in LPS mice. In SAE, DCA may be a potential candidate drug for the treatment of microglia-mediated neuroinflammation.

5-Fluorouracil resistance-based immune-related gene signature for COAD prognosis.

Background: Drug resistance is the primary obstacle to advanced tumor therapy and the key risk factor for tumor recurrence and death. 5-Fluorouracil (5-FU) chemotherapy is the most common chemotherapy for individuals with colorectal cancer, despite numerous options. Methods: The Gene Expression Omnibus database was utilized to extract expression profile data of HCT-8 human colorectal cancer wild-type cells and their 5-FU-induced drug resistance cell line. These data were used to identify 5-FU resistance-related differentially expressed genes (5FRRDEGs), which intersected with the colorectal adenocarcinoma (COAD) transcriptome data provided by the Cancer Genome Atlas Program database. A prognostic signature containing five 5FRRDEGs (GOLGA8A, KLC3, TIGD1, NBPF1, and SERPINE1) was established after conducting a Cox regression analysis. We conducted nomogram development, drug sensitivity analysis, tumor immune microenvironment analysis, and mutation analysis to assess the therapeutic value of the prognostic qualities. Results: We identified 166 5FRRDEGs in patients with COAD. Subsequently, we created a prognostic model consisting of five 5FRRDEGs using Cox regression analysis. The patients with COAD were divided into different risk groups by risk score; the high-risk group demonstrated a worse prognosis than the low-risk group. Conclusion: In summary, the 5FRRDEG-based prognostic model is an effective tool for targeted therapy and chemotherapy in patients with COAD. It can accurately predict the survival prognosis of these patients as well as to provide the direction for exploring the resistance mechanism underlying COAD.

Impact of screw tip design on screw anchorage: mechanical testing and numerical simulation.

BACKGROUND: Screw loosening is a commonly reported issue following spinal screw fixation and can lead to various complications. The initial cause of screw loosening is biomechanical deterioration. Previous studies have demonstrated that modifications in screw design can impact the local biomechanical environment, specifically the stress distribution on bone-screw interfaces. There are several different designs of screw tips available for clinically used pedicle screws; however, it remains unclear whether these variations affect the local stress distribution and subsequent screw anchorage ability. METHODS: This study conducted comprehensive biomechanical research using polyurethane foam mechanical tests and corresponding numerical simulations to investigate this topic. Models of pedicle screw-fixed osteoporotic polyurethane foam were created with two different clinically used screw tip designs (flat and steep) featuring varying tip lengths, taper angles, and diameters, as well as identical flank overlap areas and thread designs. The anchorage ability of the different models was assessed through toggle and pull-out test. Additionally, numerical mechanical models were utilized to compute the stress distributions at the screw and bone-screw interfaces in the different models. RESULTS: Mechanical tests revealed superior anchorage ability in models utilizing flat-tipped screws. Furthermore, numerical modeling indicated improved anchorage ability and reduced stress concentration tendency in these models. CONCLUSION: Changes in screw tip design can significantly impact the biomechanical anchoring capability of screws. Specifically, flatter tip pedicle screws may mitigate the risk of screw loosening by alleviating stress concentration on bone-screw interfaces.

Jianpi Jiedu decoction suppresses colorectal cancer growth by inhibiting M2 polarization of TAMs through the tryptophan metabolism-AhR pathway.

BACKGROUND: Traditional Chinese medicine, JianpiJiedu decoction (JPJDF), has been utilized in colorectal cancer (CRC) treatment for over forty years. The potential of JPJDF to inhibit CRC through modulation of intestinal microbiota and their metabolites remains uncertain. AIMS: This study aims to further investigate the therapeutic mechanisms of JPJDF in CRC. METHODS: CAC mouse models were developed using azoxymethane (AOM) and dextran sulfate sodium (DSS). Intestinal tissues and contents underwent 16S rRNA gene sequencing and untargeted metabolomics analysis. Serum levels of IL-1ß and TNF-α were measured using ELISA. Immunohistochemistry was utilized to assess the expression of Ki67, ZO-1, Occludin, CD68, and CD206. Furthermore, western blotting was performed to evaluate the protein expression of AhR and NF-κB. RESULTS: JPJDF inhibited colorectal tumourigenesis in AOM/DSS treated mice, while also suppressing tumor cell proliferation and upregulating the expression of tight junction proteins. The results of 16S rRNA gene sequencing analysis revealed that JPJDF altered intestinal microbiota composition by increasing the abundance of beneficial bacteria. Additionally, JPJDF reduced tryptophan metabolites, effectively alleviating inflammation and significantly restoring intestinal barrier function in CAC mice. Molecular biology experiments confirmed that JPJDF suppressed the expression levels of AhR and M2-type tumor-associated macrophages, thereby promoting anti-tumor immunity and exerting inhibitory effects on CAC growth. CONCLUSION: JPJDF can regulate the tryptophan metabolism-AhR pathway by modulating the gut microbiota, reducing intestinal inflammation, improving intestinal barrier function, enhancing anti-tumor immunity, and effectively inhibiting CAC growth.

Application of color doppler ultrasound and US shear wave elastography with connective tissue growth factor in the risk assessment of papillary thyroid carcinoma.

BACKGROUND: This study aims to investigate the role of shear wave elastography (SWE) and connective tissue growth factor (CTGF) in the assessment of papillary thyroid carcinoma (PTC) prognosis. METHODS: CTGF expression was detected with immunohistochemistry. Clinical and pathological data were collected. Parameters of conventional ultrasound combined with SWE were also collected. The relationship among CTGF expression, ultrasound indicators, the elastic modulus and the clinicopathological parameters were analyzed. RESULTS: Univariate analysis showed that patients with high risk of PTC were characterized with male, Uygur ethnicity, increased expression of CTGF, convex lesions, calcified, incomplete capsule, intranodular blood flow, rear echo attenuation, cervical lymph node metastasis, lesions larger than 1 cm, psammoma bodies, advanced clinical stage, increased TSH and high value in the shear modulus (P < 0.05). Multivariate analysis demonstrated that the risk factors of high expression of CTGF according to contribution size order were irregular shape, aspect ratio ≥ 1, and increased TSH. The logistic regression model equation was Logit (P) = 1.153 + 1.055 × 1 + 0.926 × 2 + 1.190 × 3 and the Area Under Curve value of the logistic regression was calculated to be 0.850, with a 95% confidence interval of 0.817 to 0.883. CONCLUSION: SWE and CTGF are of great value in the risk assessment of PTC. The degree of fibrosis of PTC is closely related to the prognosis. The hardness of PTC lesions and the expression level of CTGF are correlated with the main indexes of conventional ultrasound differentiating benign or malignant nodules. Irregular shape, aspect ratio ≥ 1, and increased TSH are independent factors of CTGF.

Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis.

Background: The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies. Methods: This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes. Results: In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells. Conclusions: The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.

Introduction: We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods: Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results: We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion: The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer.

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Jianpi Jiedu decoction reverses 5-fluorouracil resistance in colorectal cancer by suppressing the xCT/GSH/GPX4 axis to induce ferroptosis.

Introduction: Innate and acquired chemoresistance in colorectal cancer (CRC) often results in 5-fluorouracil (5-FU) treatment failure. This study aimed to investigate the potential of Jianpi Jiedu (JPJD) decoction to reverse 5-FU resistance in CRC and clarify its potential mechanism of action. Methods: The CCK-8 assay was employed to assess cell activity. Flow cytometry was employed to assess various parameters including cell apoptosis, cell cycle distribution, P-glycoprotein (P-gp) activity, reactive oxygen species levels, and lipid peroxidation. Metabolomics analysis was conducted to identify differentially expressed metabolites. Western blotting was utilized for protein expression analysis. Results: In this study, we demonstrated that the combined JPJD and 5-FU treatment reversed 5-FU resistance in HCT8/5-FU cells, inducing cell apoptosis, causing G2/M-phase cell cycle arrest, and reducing P-gp protein expression and activity. Metabolomics analysis revealed ferroptosis as a key pathway in the development of 5-FU resistance. Furthermore, the combination treatment reversed drug resistance primarily by impacting ferroptosis and triggering critical ferroptosis events through the suppression of the cystine/glutamate transporter (xCT)/glutathione (GSH)/glutathione peroxidase (GPX4) axis. Conclusion: JPJD decoction primarily suppressed the xCT/GSH/GPX4 axis to trigger ferroptosis, thereby effectively reversing 5-FU resistance in colorectal cancer (CRC).

Comparison of therapeutic effects between big-bubble deep anterior lamellar keratoplasty and penetrating keratoplasty for medically unresponsive Acanthamoeba keratitis.

PURPOSE: To compare the outcomes of big-bubble deep anterior lamellar keratoplasty (BB-DALK) and penetrating keratoplasty (PKP) in the management of medically unresponsive Acanthamoeba keratitis (AK). METHODS: This retrospective study included 27 eyes of BB-DALK and 24 eyes of PKP from a tertiary ophthalmology care centre. Glucocorticoid eye drops were subsequently added to the treatment plan 2 months postoperatively based on the evaluation using confocal laser scanning microscopy. The clinical presentations, best-corrected visual acuity (BCVA), postoperative refractive outcomes, graft survival, and Acanthamoeba recurrence were analyzed. RESULTS: The AK patients included in the study were in stage 2 or stage 3, and the percentage of patients in stage 3 was higher in the PKP group (P = 0.003). Clinical presentations were mainly corneal ulcers and ring infiltrates, and endothelial plaques, hypopyon, uveitis and glaucoma were more common in the PKP group (P = 0.007). The BCVA and the graft survival rate showed no statistically significant differences between the two groups at 1 year after surgery. However, 3 years postoperatively, the BCVA of 0.71 ± 0.64 logMAR, the graft survival rate of 89.5%, and the endothelial cell density of 1899 ± 125 cells per square millimeter in the BB-DALK group were significantly better than those of the PKP group (P = 0.010, 0.046, and 0.032, respectively). 3 eyes (11.1%) in the BB-DALK group and 2 eyes (8.3%) in the PKP group experienced Acanthamoeba recurrence, but the rates showed no statistically significant difference between the two groups (P = 1.000). In the PKP group, immune rejection and elevated intraocular pressure were observed in 5 and 6 eyes, respectively. CONCLUSION: Corneal transplantation is recommended for AK patients unresponsive to antiamoebic agents. The visual acuity and graft survival can be maintained after BB-DALK surgery. Acanthamoeba recurrence is not related to the surgical approach performed, whereas complete dissection of the infected corneal stroma and delayed prescribing of glucocorticoid eye drops were important to prevent recurrence.

GCN2-eIF2α signaling pathway negatively regulates the growth of triploid crucian carp.

GCN2-eIF2α signaling pathway plays crucial roles in cell growth,development, and protein synthesis. However, in polyploid fish, the function of this pathway is rarely understood. In this study, genes associated with the GCN2-eIF2α pathway (pkr, pek, gcn2, eif2α) are founded lower expression levels in the triploid crucian carp (3nCC) muscle compared to that of the red crucian carp (RCC). In muscle effect stage embryos of the 3nCC, the mRNA levels of this pathway genes are generally lower than those of RCC, excluding hri and fgf21. Inhibiting gcn2 in 3nCC embryos downregulates downstream gene expression (eif2α, atf4, fgf21), accelerating embryonic development. In contrast, overexpressing of eif2α can alter the expression levels of downstream genes (atf4 and fgf21), and decelerates the embryonic development. These results demonstrate the GCN2-eIF2α pathway's regulatory impact on 3nCC growth, advancing understanding of fish rapid growth genetics and offering useful molecular markers for breeding of excellent strains.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Association between cancer health literacy and cancer-related behavior of residents in Shandong, China.

Health literacy is closely related to the incidence of major chronic diseases and its related behaviors such as cancer-related behaviors. This study explored how the cancer health literacy level affects cancer-related behaviors. About one to two villages from six cities of Shandong province were selected as sample areas. Professionals conducted face-to-face interviews with the participants. Finally, 1200 residents completed 1085 effective questionnaires. Data were analysed from a cross-sectional survey in 2019, which included 1085 residents in six cities/counties of Shandong province, China. The result showed that residents with high cancer health literacy were more likely to eat fruits and vegetables frequently, avoid eating moldy food and take exercise. Besides, they were more likely to engage in health education and have a higher willingness to pay for cancer screenings. Most residents in Shandong province have a basic level of cancer health literacy. Improving the cancer health literacy of the population can be an effective strategy to promote a healthier lifestyle, thereby reducing the incidence rates related to cancers.

Distribution characteristics, source analysis and risk assessment of polycyclic aromatic hydrocarbons in sediments of Kuye River: a river in a typical energy and chemical industry zone.

This study systematically analyzed the distribution characteristics, sources, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) in Kuye River sediments, located in an energy and chemical industry base in northern Shaanxi, China. The results that revealed the concentrations of 16 PAHs in the sediment ranged from 1090.04 to 32,175.68 ng∙g-1 dw, with the four-ring PAHs accounting for the highest proportion. Positive matrix factorization analysis (PMF) revealed the main sources of PAHs as incomplete fossil fuel combustion, biomass combustion, and traffic emissions. The total toxic equivalent concentration of BaP, risk quotient, and lifetime carcinogenic risk of PAHs suggested moderate to high contamination of PAHs in the area. The higher incremental lifetime carcinogenic risk (ILCR) indicated that PAH ingestion was the primary route of impact on public health, with children potentially being more susceptible to PAH exposure. This study can provide valuable theoretical support for implementing pollution prevention measures and ecological restoration strategies for rivers in energy and chemical industry areas.

[Single Kocher-Langenbeck approach combined with anterograde channel screw technique in treatment of acetabular transverse and posterior wall fractures].

Objective: To assess the effectiveness of the single Kocher-Langenbeck approach combined with anterograde channel screw technique for the treatment of acetabular transverse and posterior wall fractures. Methods: Between March 2020 and October 2022, 17 cases of acetabular transverse and posterior wall fractures were treated with the single Kocher-Langenbeck approach combined with anterograde channel screw technique. There were 11 males and 6 females, with an average age of 53.6 years (range, 42-64 years). Causes of injury included traffic accident in 12 cases, and falling from height in 5 cases. The time from injury to operation ranged from 4 to 16 days with an average of 8.8 days. The operation time, intraoperative blood loss, and fluoroscopy frequency were recorded; X-ray films were reviewed regularly after operation to observe the fracture healing, and postoperative complications were recorded. At last follow-up, Matta score was used to evaluate the reduction of fracture, Harris score and modified Merle D'Aubigné-Postel scores system were used to evaluate the hip joint function. Results: The operation time was 150-230 minutes (mean, 185.9 minutes), the intraoperative blood loss was 385-520 mL (mean, 446.2 mL), and the fluoroscopy frequency was 18-34 times (mean, 27.5 times). Postoperative fat liquefaction occurred in 1 case and the other incisions healed by first intention; 3 cases had limb numbness after operation, and the symptoms disappeared after active symptomatic treatment; no urogenital system and intestinal injury occurred. All patients were followed up 12-28 months (mean, 19.9 months). Bone union was achieved in all cases with an average healing time of 10.8 weeks (range, 8-14 weeks). There was no complication such as loosening and breakage of internal fixators. At last follow-up, according to Matta score, 12 cases achieved anatomic reduction, 3 satisfactory reduction, and 2 fair reduction, the satisfactory rate was 88.2%; according to Harris hip function score, 12 cases were excellent, 3 good, and 2 fair, the excellent and good rate was 88.2%; according to the modified Merle D'Aubign Aubigné-Postel scoring system, the results were excellent in 11 cases, good in 3 cases, and fair in 3 cases, with an excellent and good rate of 82.4%. Conclusion: The single Kocher-Langenbeck approach combined with anterograde channel screw technique is a minimally invasive method for the treatment of acetabular transverse and posterior wall fractures with less complications, simple operation, and satisfactory effectiveness.

Exploring the potential mechanism and molecular targets of Taohong Siwu Decoction against deep vein thrombosis based on network pharmacology and analysis docking.

This study aims to investigate the mechanism of Taohong Siwu Decoction (THSWD) against deep vein thrombosis (DVT) using network pharmacology and molecular docking technology. We used the Traditional Chinese Medicine Systems Pharmacology database and reviewed literature to identify the main chemical components of THSWD. To find targets for DVT, we consulted GeneCards, Therapeutic Target Database, and PharmGKB databases. We used Cytoscape 3.8.2 software to construct herb-disease-gene-target networks. Additionally, we integrated drug targets and disease targets on the STRING platform to create a protein-protein interaction network. Then, we conducted Kyoto Encyclopedia of Genes and Genomes and gene ontology analysis. Finally, We employed the molecular docking method to validate our findings. We identified 56 potential targets associated with DVT and found 61 effective components. beta-sitosterol, quercetin, and kaempferol were the most prominent among these components. Our analysis of the protein-protein interaction network revealed that IL6, L1B, and AKT1 had the highest degree of association. Gene ontology analysis showed that THSWD treatment for DVT may involve response to inorganic substances, negative regulation of cell differentiation, plasma membrane protein complex, positive regulation of phosphorylation, and signaling receptor regulator activity. Kyoto Encyclopedia of Genes and Genomes analysis indicated that lipid and atherosclerosis, pathways in cancer, as well as the PI3K-Akt pathway are the main signal pathways involved. Molecular docking results demonstrated strong binding affinity between beta-sitosterol, quercetin, kaempferol, and AKT1 proteins as well as IL1B and IL6 proteins. The main targets for THSWD treatment of DVT may include AKT1, IL1B, and IL6. Beta-sitosterol, quercetin, and kaempferol may be the active ingredients responsible for producing this effect. These compounds may slow down the progression of DVT by regulating the inflammatory response through the PI3K/Akt pathway.

Engineering of a Substrate Affinity Reduced S-Adenosyl-methionine Synthetase as a Novel Biosensor for Growth-Coupling Selection of L-Methionine Overproducers.

Biosensors are powerful tools for monitoring specific metabolites or controlling metabolic flux towards the products in a single cell, which play important roles in microbial cell factory construction. Despite their potential role in metabolic flux monitoring, the development of biosensors for small molecules is still limited. Reported biosensors often exhibit bottlenecks of poor specificity and a narrow dynamic range. Moreover, fine-tuning the substrate binding affinity of a crucial enzyme can decrease its catalytic activity, which ultimately results in the repression of the corresponding essential metabolite biosynthesis and impairs cell growth. However, increasing intracellular substrate concentration can elevate the availability of the essential metabolite and may lead to restore cellular growth. Herein, a new strategy was proposed for constructing whole-cell biosensors based on enzyme encoded by essential gene that offer inherent specificity and universality. Specifically, S-adenosyl-methionine synthetase (MetK) in E. coli was chosen as the crucial enzyme, and a series of MetK variants were identified that were sensitive to L-methionine concentration. This occurrence enabled the engineered cell to sense L-methionine and exhibit L-methionine dose-dependent cell growth. To improve the biosensor's dynamic range, an S-adenosyl-methionine catabolic enzyme was overexpressed to reduce the intracellular availability of S-adenosyl-methionine. The resulting whole-cell biosensor effectively coupled the intracellular concentration of L-methionine with growth and was successfully applied to select strains with enhanced L-methionine biosynthesis from random mutagenesis libraries. Overall, our study presents a universal strategy for designing and constructing growth-coupled biosensors based on crucial enzyme, which can be applied to select strains overproducing high value-added metabolites in cellular metabolism.

Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.

INTRODUCTION: Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. METHODS: This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. RESULTS: Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. CONCLUSION: The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.