Association between chemotherapy for surgically treated rectal cancer and second primary endometrial cancer.

To examine the potential correlation between chemotherapy and the risk of individual of second primary endometrial cancer (SEC) in patients with rectal cancer (RC) and assess survival outcomes. The study employed the Surveillance, Epidemiology, and End Results database (SEER) as the primary data source, it encompasses a substantial cohort of patients diagnosed with RC between 1975 and 2018. This study involved a total of 30,847 individuals diagnosed with RC, of whom 168 individuals (5.45‰) experienced SEC. Among them, 107 patients (3.47‰) received chemotherapy treatment, while 61 patients (1.98‰) did not receive chemotherapy. The analysis of the overall occurrence of SEC revealed a significant association between SEC and chemotherapy treatment. Univariate and multivariate analyses confirmed a significant association between chemotherapy treatment and an increased risk of developing SEC in RC patients. Upon implementation of a dynamic analysis on the variables of relative risk and standardized incidence ratios, the results revealed that the likelihood of SEC escalated in tandem with advancing age. The examination of patients who developed SEC after receiving and not receiving chemotherapy revealed no substantial disparities in the 10-year overall survival (OS) and (cancer-specific survival) CSS rates. The results were the same after propensity score matching. Nevertheless, a notable discrepancy emerged when comparing the OS and CSS rates at 10 years between patients afflicted with SEC subsequent to chemotherapy and those afflicted with primary endometrial cancer, and the result was the same situation in the no-chemotherapy group. The use of chemotherapy in RC patients has been associated with an increased probability of developing specific SEC. Therefore, it is imperative to prioritize efforts aimed at reducing chemotherapy-related SEC occurrences and improving the prognosis of affected individuals.

The signature of the gut microbiota associated with psoriatic arthritis revealed by metagenomics.

Background: Gut microbiota is involved in the development of psoriatic arthritis (PsA), but until now, there has been a lack of understanding of the PsA host-bacteria interaction. Objectives: To reveal the labels of gut microbiota in PsA patients and the species and functions related to disease activity. Design: Observational research (cross-sectional) with an exploratory nature. Methods: Metagenomics sequencing was used to analyze stool samples from 20 treatment-naïve PsA patients and 10 age-matched healthy individuals. All samples were qualified for subsequent analysis. Results: Compared with the healthy group, α-diversity was reduced in the PsA group, and ß-diversity could distinguish the two groups. Two bacteria with high abundance and correlation with PsA disease activity were identified, Bacteroides sp. 3_1_19 and Blautia AF 14-40. In different functions, K07114 (calcium-activated chloride channel (CaCC) homolog) showed a positive correlation with PsA disease activity (disease activity in psoriatic arthritis, DAPSA) and Tet32 (an antibiotic-resistant gene), and carbohydrate-binding module family 50 was negatively correlated with erythrocyte sedimentation rate. A bacterial co-expression network associated with DAPSA was constructed. The network was centered on the bacteria in the Bacteroides genus, which formed a closely related network and were positively correlated with DAPSA. As another core of the network, K07114 was closely related to multiple bacteria in the Bacteroides genus and is also positively correlated with disease activity. Conclusion: The network composed of Bacteroides is associated with PsA disease activity, and its therapeutic value needs to be further explored. CaCCs may be a key channel for the interaction between Bacteroides and PsA-host.

Metal ions-anchored bacterial outer membrane vesicles for enhanced ferroptosis induction and immune stimulation in targeted antitumor therapy.

The activation of ferroptosis presents a versatile strategy for enhancing the antitumor immune responses in cancer therapy. However, developing ferroptosis inducers that combine high biocompatibility and therapeutic efficiency remains challenging. In this study, we propose a novel approach using biological nanoparticles derived from outer membrane vesicles (OMVs) of Escherichia coli for tumor treatment, aiming to activate ferroptosis and stimulate the immune responses. Specifically, we functionalize the OMVs by anchoring them with ferrous ions via electrostatic interactions and loading them with the STING agonist-4, followed by tumor-targeting DSPE-PEG-FA decoration, henceforth referred to as OMV/SaFeFA. The anchoring of ferrous ions endows the OMVs with peroxidase-like activity, capable of inducing cellular lipid peroxidation by catalyzing H2O2 to •OH. Furthermore, OMV/SaFeFA exhibits pH-responsive release of ferrous ions and the agonist, along with tumor-targeting capabilities, enabling tumor-specific therapy while minimizing side effects. Notably, the concurrent activation of the STING pathway and ferroptosis elicits robust antitumor responses in colon tumor-bearing mouse models, leading to exceptional therapeutic efficacy and prolonged survival. Importantly, no acute toxicity was observed in mice receiving OMV/SaFeFA treatments, underscoring its potential for future tumor therapy and clinical translation.

Experience with immediate internal fixation combined with primary wound closure in Gustilo-Anderson type IIIA open ankle fractures.

Open ankle fractures, especially Gustilo-Anderson type III fractures are challenging to manage with controversy over the "best" or "superior" treatment strategy. This study aimed to evaluate the treatment outcome of immediate internal fixation combined with primary wound closure in the management of Gustilo-Anderson type III open ankle fractures. We retrospectively assessed the outcomes of thirty-two patients treated using immediate internal fixation combined with primary wound closure with a minimum follow-up of twenty-four months. At the median follow-up of 38 (24 to 62) months, the mean American Orthopaedic Foot and Ankle Society scale score was 87.22±4.05. The physical component summary score of Short-Form 36 Health Status Survey was 66.63±11.42 and the mental component summary score was 67.31±7.20. Range of motion of Ankle/Foot injured side was 64.56±4.30 degrees, and Range of motion of Ankle/Foot uninjured side was 72.31±3.12 degrees. Visual analog pain scale score was 1.5±0.88 at rest and 3.09±1.17 during activity. According to American Orthopaedic Foot and Ankle Society scale score, the rate of excellent and good outcomes was 90.6%. Postoperative complications were documented, comprising two (6.4%) cases of infection, five (15.6%) cases of wound skin necrosis, one (3.2%) case of postoperative ankle traumatic arthritis, and one (3.2%) case requiring reoperation due to suboptimal fibula fracture reduction. The study results demonstrated that immediate internal fixation combined with primary wound closure for Gustilo-Anderson type III open ankle fractures achieve good functional outcomes and lower complication rates. Level of Evidence: Level IV, retrospective case series.

Microglia specific alternative splicing alterations in multiple sclerosis.

Several aberrant alternative splicing (AS) events and their regulatory mechanisms are widely recognized in multiple sclerosis (MS). Yet the cell-type specific AS events have not been extensively examined. Here we assessed the diversity of AS events using web-based RNA-seq data of sorted CD15-CD11b+ microglia in white matter (WM) region from 10 patients with MS and 11 control subjects. The GSE111972 dataset was downloaded from GEO and ENA databases, aligned to the GRCh38 reference genome from ENSEMBL via STAR. rMATS was used to assess five types of AS events, alternative 3'SS (A3SS), alternative 5'SS (A5SS), skipped exon (SE), retained intron (RI) and mutually exclusive exons (MXE), followed by visualizing with rmats2sashimiplot and maser. Differential genes or transcripts were analyzed using the limma R package. Gene ontology (GO) analysis was performed with the clusterProfiler R package. 42,663 raw counts of AS events were identified and 132 significant AS events were retained based on the filtered criteria: 1) average coverage >10 and 2) delta percent spliced in (ΔPSI) >0.1. SE was the most common AS event (36.36%), followed by MXE events (32.58%), and RI (18.94%). Genes related to telomere maintenance and organization primarily underwent SE splicing, while genes associated with protein folding and mitochondrion organization were predominantly spliced in the MXE pattern. Conversely, genes experiencing RI were enriched in immune response and immunoglobulin production. In conclusion, we identified microglia-specific AS changes in the white matter of MS patients, which may shed light on novel pathological mechanisms underlying MS.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

The ATP-binding cassette transporter subfamily G member 4 mediates cuticular hydrocarbon transport to regulate drought tolerance in Acyrthosiphon pisum.

ABC transporters are a highly conserved membrane protein class that promote the transport of substances across membranes. Under drought conditions, insects primarily regulate the content of cuticular hydrocarbon (CHC) to retain water and prevent evaporative loss. Involvement of ABC transporter protein G (ABCG) subfamily genes in insect CHC transport has been relatively understudied. In this study, we demonstrated that ABCG4 gene in Acyrthosiphon pisum (ApABCG4) is involved in CHC transport and affects drought tolerance by regulating CHC accumulation. ApABCG4 is strongly expressed in the abdominal cuticle and embryonic stages of A. pisum. Effective silencing of ApABCG4 was achieved using RNAi, and the silencing duration was analyzed. ApABCG4 silencing resulted in a significant decrease in the total and component contents of the CHC and cuticular waxy coatings of A. pisum. Nevertheless, the internal hydrocarbon content remained unchanged. The lack of cuticular hydrocarbons significantly reduced the drought tolerance of A. pisum, shortening its survival time under drought stress. Drought stress caused significant upregulation of ApABCG4. Molecular docking showed that ApABCG4 has a high binding affinity for nine n-alkanes of CHC through electrostatic interactions. These results indicate that ApABCG4 is a novel RNAi target with key applications in aphid biological control.

Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.

OBJECTIVE: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC). MATERIALS AND METHODS: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set. RESULTS: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively. CONCLUSION: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training.

Advances in attenuating opioid-induced respiratory depression: A narrative review.

Opioids exert analgesic effects by agonizing opioid receptors and activating signaling pathways coupled to receptors such as G-protein and/or ß-arrestin. Concomitant respiratory depression (RD) is a common clinical problem, and improvement of RD is usually achieved with specific antagonists such as naloxone; however, naloxone antagonizes opioid analgesia and may produce more unknown adverse effects. In recent years, researchers have used various methods to isolate opioid receptor-mediated analgesia and RD, with the aim of preserving opioid analgesia while attenuating RD. At present, the focus is mainly on the development of new opioids with weak respiratory inhibition or the use of non-opioid drugs to stimulate breathing. This review reports recent advances in novel opioid agents, such as mixed opioid receptor agonists, peripheral selective opioid receptor agonists, opioid receptor splice variant agonists, biased opioid receptor agonists, and allosteric modulators of opioid receptors, as well as in non-opioid agents, such as AMPA receptor modulators, 5-hydroxytryptamine receptor agonists, phosphodiesterase-4 inhibitors, and nicotinic acetylcholine receptor agonists.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay. METHODS: Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets. RESULTS: The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay. CONCLUSIONS: The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

Preparation and Evaluation of Clarithromycin Taste-Masking Dry Suspension Using Hot Melt Extrusion Based on Solid Dispersion Technology.

Clarithromycin (CLA) is the preferred drug for treating respiratory infections in pediatric patients, but it has the drawbacks of extreme bitterness and poor water solubility. The purpose of this study was to improve solubility and mask the extreme bitterness of CLA. We use Hot Melt Extrusion (HME) to convert CLA and Eudragit® E100 into Solid Dispersion (SD). Differential scanning calorimetry (DSC) and Powder X-ray diffraction (PXRD) were used to identify the crystalline form of the prepared SDs, which showed that the crystalline CLA was converted to an amorphous form. At the same time, an increase in dissolution rate was observed, which is one of the properties of SD. The results showed that the prepared SD significantly increased the dissolution rate of crystalline CLA. Subsequently, the SD of CLA was prepared into a dry suspension with excellent suspending properties and a taste-masking effect. The bitterness bubble chart and taste radar chart showed that the SD achieved the bitter taste masking of CLA. Principal components analysis (PCA) of the data generated by the electronic tongue showed that the bitter taste of CLA was significantly suppressed using the polymer Eudragit® E100. Subsequently, a dry suspension was prepared from the SD of CLA. In conclusion, this work illustrated the importance of HME for preparing amorphous SD of CLA, which can solve the problems of bitterness-masking and poor solubility. It is also significant for the development of compliant pediatric formulations.

Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation.

Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, [Formula: see text], SDRatio, [Formula: see text], and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study.

PURPOSE: To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models. METHODS: A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated. RESULTS: Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical. CONCLUSIONS: Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

Inhibiting the CB1 receptor in CIH-induced animal model alleviates colon injury.

Obstructive sleep apnea (OSA) can lead to intestinal injury, endotoxemia, and disturbance of intestinal flora. Additionally, as a crucial component of the endocannabinoid system, some studies have demonstrated that cannabinoid 1 (CB1) receptors are closely linked to the multiple organ dysfunction triggered by OSA. However, the role of the CB1 receptor in alleviating OSA-induced colon injury remains unclear. Here, through the construction of the OSA classic model, we found that the colon tissue of chronic intermittent hypoxia (CIH)-induced mice exhibited an overexpression of the CB1 receptor. The results of hematoxylin-eosin staining and transmission electron microscopy revealed that inhibition of the CB1 receptor could decrease the gap between the mucosa and muscularis mucosae, alleviate mitochondrial swelling, reduce microvilli shedding, and promote the recovery of tight junctions of CIH-induced mice. Furthermore, CB1 receptor inhibition reduced the levels of metabolic endotoxemia and inflammatory responses, exhibiting significant protective effects on the colon injury caused by CIH. At the molecular level, through western blotting and real-time polymerase chain reaction techniques, we found that inhibiting the CB1 receptor can significantly increase the expression of ZO-1 and Occludin proteins, which are closely related to the maintenance of intestinal mucosal barrier function. Through 16S rRNA high-throughput sequencing and short-chain fatty acid (SCFA) determination, we found that inhibition of the CB1 receptor increased the diversity of the microbial flora and controlled the makeup of intestinal flora. Moreover, butyric acid concentration and the amount of SCFA-producing bacteria, such as Ruminococcaceae and Lachnospiraceae, were both markedly elevated by CB1 receptor inhibition. The results of the spearman correlation study indicated that Lachnospiraceae showed a positive association with both ZO-1 and Occludin but was negatively correlated with the colon CB1 receptor, IL-1ß, and TNF-α. According to this study, we found that inhibiting CB1 receptor can improve CIH-induced colon injury by regulating gut microbiota, reducing mucosal damage and promoting tight junction recovery. KEY POINTS: •CIH leads to overexpression of CB1 receptor in colon tissue. •CIH causes intestinal flora disorder, intestinal mucosal damage, and disruption of tight junctions. •Inhibition of CB1 receptor can alleviate the colon injury caused by CIH through regulating the gut microbiota, reducing mucosal injury, and promoting tight junction recovery.

Association between indicators of visceral lipid accumulation and infertility: a cross-sectional study based on U.S. women.

BACKGROUND: Evidence on the association between visceral lipid accumulation and infertility remains limited and controversial. Therefore, the current investigation is the first investigation to unveil this correlation by utilizing novel indicators of visceral lipid accumulation. METHODS: The present study utilized the NHANES 2013-2020 dataset. Researchers utilized multiple logistic regression, smoothed curve fitting, and subgroup analysis to investigate the associations of waist circumference (WC), metabolic score for visceral fat (METS-VF), lipid accumulation product (LAP), visceral adiposity index (VAI) with infertility. Additionally, the eXtreme Gradient Boosting (XGBoost) algorithm model was utilized to evaluate the relative importance of the factors. RESULTS: After adjusting for potential factors that could influence the results, researchers discovered that all these four indicators of visceral lipid accumulation exhibited strong positive correlations with the probability of infertility. The subgroup analysis demonstrated that the correlations remained consistent in the majority of subgroups (P for interaction > 0.05). The results of XGBoost algorithm model indicate that METS-VF is the most meaningful factor in infertility. The ROC curve research revealed that while METS-VF had the greatest AUC values, there was no variation in the AUC value of different markers of visceral fat accumulation (P > 0.05). CONCLUSIONS: The present investigation discovered that increased WC, METS-VF, LAP, and VAI were associated with a heightened prevalence of infertility.

Electron irradiation enhanced wear resistance and hardness of polytetrafluoroethylene (PTFE).

The surface of polytetrafluoroethylene (PTFE) bulk materials was modified by irradiation at high temperatures using a 1.2 MeV electron beam. The mass wear rate was decreased from 2.5 × 10-6 g N-1 m-1 to 0.08 × 10-6 g N-1 m-1, and the hardness was increased from 33.2 MPa to 93.9 MPa. The yield strength was increased from 12.1 MPa to 25.8 MPa and Young's modulus was enhanced from 101 MPa to 261 MPa. The use of electron beam irradiation on PTFE at high temperature is an effective modification method to significantly improve its wear resistance and hardness, and increase the elastic response range of PTFE. The chemical reaction induced by electron irradiation at high temperature changes the molecular structure of the PTFE bulk material from highly linear to a network, thus improving the surface mechanical properties of PTFE.

I-FABP protein/mRNA and IL-6 as biomarkers of intestinal barrier dysfunction in neonates with necrotizing enterocolitis and SPF BALB/c mouse models.

OBJECTIVE: Neonatal necrotizing enterocolitis (NEC) is a serious intestinal inflammatory disease. We investigated intestinal fatty acid binding protein (I-FABP), I-FABP mRNA, and interleukin-6 (IL-6) as potential diagnostic biomarkers in NEC. METHODS: Forty mice were subjected to hypoxic-ischemic intestinal injury, and then serum I-FABP protein and mRNA levels were quantified. Ileal tissue pathological scores were determined by hematoxylin and eosin staining. I-FABP expression levels and translocation in these tissues were detected using western blotting and immunofluorescence, respectively. Samples from 30 human neonates with NEC and 30 healthy neonates had serum I-FABP protein/mRNA and IL-6 levels measured. RESULTS: The mouse ileal tissue pathological score and I-FABP levels, as well as serum I-FABP and I-FABP mRNA levels, were significantly higher in the model group than in the control group. Serum I-FABP, I-FABP mRNA, and IL-6 levels were significantly higher in human neonates with NEC than in the healthy group. Logistic regression and receiver operating curve analyses revealed that I-FABP protein/mRNA and IL-6 levels could be diagnostic biomarkers for NEC. CONCLUSIONS: I-FABP protein/mRNA and IL-6 levels are useful biomarkers of intestinal ischemic injury in neonates with NEC. The combined detection of I-FABP protein/mRNA and IL-6 is recommended rather than using a single biomarker.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

OBJECTIVE: The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). MATERIALS AND METHODS: A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. RESULTS: In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. CONCLUSIONS: By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Phosphorescent iridium (III) complex with covalent organic frameworks as scaffolds for highly selective and sensitive detection of homocysteine.

Introduction: Developing a convenient and cost-effective platform for detecting homocysteine (Hcy) is of great interest as Hcy has been found to be a biomarker for Alzheimer's disease, gastric cancer, and other diseases. Methods: In this study, we synthesized five phosphorescent Ir(C∧N)2(N∧N)+ compounds (Irn, n = 1-5) with various substituents (-CHO or -CHO/-NH2), which were then doped into a covalent organic framework (COF) host via covalent bonding. Results and Discussion: The resulting optimal composites (denoted as Ir4/5@EBCOF) with -CHO/-NH2 substituents not only overcame the self-quenching issue of the bare Ir4/5 complexes but also showed rapid, highly selective, and sensitive detection of Hcy, with a limit of detection (LOD) of 0.23 µM and reaction time of 88 s. The sensing mechanism was revealed as the unique cyclization reaction between Ir(III) and Hcy that forms a six-membered ring. During the process, the color changes in the composites can be observed visually. It is expected that these phosphorescent Iridium (III) complexes with COFs will have the potential to serve as promising platforms for detecting thiols.