The causal relationship between gut microbiota and biliary tract cancer: comprehensive bidirectional Mendelian randomization analysis.

Background: Growing evidence has shown that gut microbiome composition is associated with Biliary tract cancer (BTC), but the causality remains unknown. This study aimed to explore the causal relationship between gut microbiota and BTC, conduct an appraisal of the gut microbiome's utility in facilitating the early diagnosis of BTC. Methods: We acquired the summary data for Genome-wide Association Studies (GWAS) pertaining to BTC (418 cases and 159,201 controls) from the Biobank Japan (BBJ) database. Additionally, the GWAS summary data relevant to gut microbiota (N = 18,340) were sourced from the MiBioGen consortium. The primary methodology employed for the analysis consisted of Inverse Variance Weighting (IVW). Evaluations for sensitivity were carried out through the utilization of multiple statistical techniques, encompassing Cochrane's Q test, the MR-Egger intercept evaluation, the global test of MR-PRESSO, and a leave-one-out methodological analysis. Ultimately, a reverse Mendelian Randomization analysis was conducted to assess the potential for reciprocal causality. Results: The outcomes derived from IVW substantiated that the presence of Family Streptococcaceae (OR = 0.44, P = 0.034), Family Veillonellaceae (OR = 0.46, P = 0.018), and Genus Dorea (OR = 0.29, P = 0.041) exerted a protective influence against BTC. Conversely, Class Lentisphaeria (OR = 2.21, P = 0.017), Genus Lachnospiraceae FCS020 Group (OR = 2.30, P = 0.013), and Order Victivallales (OR = 2.21, P = 0.017) were associated with an adverse impact. To assess any reverse causal effect, we used BTC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between BTC and five different types of gut microbiota. The sensitivity analysis disclosed an absence of empirical indicators for either heterogeneity or pleiotropy. Conclusion: This investigation represents the inaugural identification of indicative data supporting either beneficial or detrimental causal relationships between gut microbiota and the risk of BTC, as determined through the utilization of MR methodologies. These outcomes could hold significance for the formulation of individualized therapeutic strategies aimed at BTC prevention and survival enhancement.

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study.

Background: Numerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables. Methods: The genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation. Results: The IVW method's findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results' reliability was confirmed by thorough assessments of heterogeneity and pleiotropy. Conclusions: For the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD.

Diagnostic accuracy of magnetically guided capsule endoscopy with a detachable string for detecting oesophagogastric varices in adults with cirrhosis: prospective multicentre study.

OBJECTIVE: To evaluate the diagnostic accuracy and safety of using magnetically guided capsule endoscopy with a detachable string (ds-MCE) for detecting and grading oesophagogastric varices in adults with cirrhosis. DESIGN: Prospective multicentre diagnostic accuracy study. SETTING: 14 medical centres in China. PARTICIPANTS: 607 adults (>18 years) with cirrhosis recruited between 7 January 2021 and 25 August 2022. Participants underwent ds-MCE (index test), followed by oesophagogastroduodenoscopy (OGD, reference test) within 48 hours. The participants were divided into development and validation cohorts in a ratio of 2:1. MAIN OUTCOME MEASURES: The primary outcomes were the sensitivity and specificity of ds-MCE in detecting oesophagogastric varices compared with OGD. Secondary outcomes included the sensitivity and specificity of ds-MCE for detecting high risk oesophageal varices and the diagnostic accuracy of ds-MCE for detecting high risk oesophagogastric varices, oesophageal varices, and gastric varices. RESULTS: ds-MCE and OGD examinations were completed in 582 (95.9%) of the 607 participants. Using OGD as the reference standard, ds-MCE had a sensitivity of 97.5% (95% confidence interval 95.5% to 98.7%) and specificity of 97.8% (94.4% to 99.1%) for detecting oesophagogastric varices (both P<0.001 compared with a prespecified 85% threshold). When using the optimal 18% threshold for luminal circumference of the oesophagus derived from the development cohort (n=393), the sensitivity and specificity of ds-MCE for detecting high risk oesophageal varices in the validation cohort (n=189) were 95.8% (89.7% to 98.4%) and 94.7% (88.2% to 97.7%), respectively. The diagnostic accuracy of ds-MCE for detecting high risk oesophagogastric varices, oesophageal varices, and gastric varices was 96.3% (92.6% to 98.2%), 96.9% (95.2% to 98.0%), and 96.7% (95.0% to 97.9%), respectively. Two serious adverse events occurred with OGD but none with ds-MCE. CONCLUSION: The findings of this study suggest that ds-MCE is a highly accurate and safe diagnostic tool for detecting and grading oesophagogastric varices and is a promising alternative to OGD for screening and surveillance of oesophagogastric varices in patients with cirrhosis. TRIAL REGISTRATION: ClinicalTrials.gov NCT03748563.

First-in-human phase I study of BEBT-109 in previously treated EGFR exon 20 insertion-mutated advanced non-small cell lung cancer.

BACKGROUND: BEBT-109 is an oral pan-mutant-selective inhibitor of epidermal growth factor receptor (EGFR) that demonstrated promising antitumor potency in preclinical models. METHODS: This first-in-human study was a single-arm, open-label, two-stage study. Phase Ia dose-escalation study evaluated the safety and pharmacokinetics of BEBT-109 in 11 patients with EGFR T790M-mutated advanced non-small cell lung cancer (aNSCLC). Phase Ib dose-expansion study evaluated the safety and efficacy of BEBT-109 in 18 patients with EGFR exon 20 insertion (ex20ins)-mutated treatment-refractory aNSCLC. The primary outcomes were adverse events and antitumor activity. Clinical trial registration number CTR20192575. FINDINGS: The phase Ia study demonstrated no dose-limiting toxicity, no observation of the maximum tolerated dose, and no new safety signals with BEBT-109 in the dose range of 20-180 mg/d, suggesting that BEBT-109 had an acceptable safety profile among patients with EGFR T790M-mutated aNSCLC. Plasma pharmacokinetics of BEBT-109 showed a dose-proportional increase in the area under the curve and maximal concentration, with no significant drug accumulation. The dose-expansion study demonstrated that BEBT-109 treatment was tolerable across the three dose levels. The three most common treatment-related adverse events were diarrhea (100%; 22.2% ≥Grade 3), rash (66.7%; 5.6% ≥Grade 3), and anemia (61.1%; 0% ≥Grade 3). The objective response rate was 44.4% (8 of 18). Median progression-free survival was 8.0 months (95% confidence intervals, 1.33-14.67). CONCLUSION: Preliminary findings showed that BEBT-109 had an acceptable safety profile and favorable antitumor activity in patients with refractory EGFR ex20ins-mutated aNSCLC. FUNDING: National Natural Science Foundation of China.

Unsupervised domain adaptation for histopathology image segmentation with incomplete labels.

Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving reliable model selection and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module, which adjusts the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.

Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.

Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.

Analysis of Factors Affecting Concentrations and Concentration-To-Dose Ratios of Trazodone.

BACKGROUND: Trazodone is prescribed for several clinical conditions. Multiple factors may affect trazodone to reach its therapeutic reference range. The concentration-to-dose (C/D) ratio can be used to facilitate the therapeutic drug monitoring of trazodone. The study aimed to investigate factors on the concentrations and C/D ratio of trazodone. METHODS: This study analyzed the therapeutic drug monitoring electronic case information of inpatients in the First Hospital of Hebei Medical University from October 2021 to July 2023. Factors that could affect the concentrations and C/D ratio of trazodone were analyzed, including body mass index, sex, age, smoking, drinking, drug manufacturers, and concomitant drugs. RESULTS: A total of 255 patients were analyzed. The mean age was 52.44 years, and 142 (55.69%) were women. The mean dose of trazodone was 115.29 mg. The mean concentration of trazodone was 748.28 ng/mL, which was in the therapeutic reference range (700-1000 ng/mL). 50.20% of patients reached the reference range, and some patients (36.86%) had concentrations below the reference range. The mean C/D ratio of trazodone was 6.76 (ng/mL)/(mg/d). A significant positive correlation was found between daily dose and trazodone concentrations (r 2 = 0.2885, P < 0.001). Trazodone concentrations were significantly affected by dosage, sex, smoking, drinking, and concomitant drugs of duloxetine or fluoxetine. After dosage emendation, besides the above factors, it was influenced by age ( P < 0.05, P < 0.01, or P < 0.001). CONCLUSIONS: This study identified factors affecting trazodone concentrations and C/D ratio. The results can help clinicians closely monitor patients on trazodone therapy and maintain concentrations within the reference range.

CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer.

Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.

Lipid alterations play a role in the integration of PD-1/PD-L1 inhibitors and anlotinib for the treatment of advanced non-small-cell lung cancer.

BACKGROUND: Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non-small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC. METHODS: The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment. RESULTS: Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides. CONCLUSION: This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.

Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence.

BACKGROUND: Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. METHODS: The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. RESULTS: Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. CONCLUSIONS: In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study.

BACKGROUND: Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques. METHODS: Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed. RESULTS: CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively). CONCLUSION: The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

Predictors of body image dissatisfaction among women at different stages of pregnancy:A cross-sectional study.

OBJECTIVE: To explore the levels and predictors of body image dissatisfaction among women at different stages of pregnancy. DESIGN: This was a cross-sectional study design. SETTING AND PARTICIPANTS: A total of 863 Chinese pregnant women were recruited from a tertiary hospital via a convenience sampling method. MEASUREMENT AND FINDINGS: Eligible participants completed a demographic questionnaire and self-reported measures of body image dissatisfaction, pregnancy-related anxiety, prenatal depression, and appearance comparison. Results showed no statistical difference in body image dissatisfaction levels among early-mid pregnancy (47.6 ± 6.17), late-mid pregnancy (47.3 ± 7.56), and late pregnancy stages (48.4 ± 6.22). The generalized linear model showed that gestational weight gain, pregnancy-related anxiety, own/family's perception of pregnancy weight, and current ideal weight change were predictors of body image dissatisfaction in the early-mid pregnancy stage. In addition, pre-pregnancy BMI, appearance comparison, own /family's perception of pregnancy weight, current ideal weight change, and overeating during pregnancy significantly predicted body image dissatisfaction in the late-mid pregnancy stage. Predictors of body image dissatisfaction in the late pregnancy stage comprised planned pregnancy, pre-pregnancy eating disorders, own perception of pregnancy weight, current ideal weight change, pregnancy-related anxiety, and prenatal depression. KEY CONCLUSION AND IMPLICATION FOR PRACTICE: The findings suggest that predictors of body image dissatisfaction differed according to pregnancy stage. Self-perception of pregnancy weight was primary predictor of body image dissatisfaction. Healthcare professionals are recommended to provide prenatal health education to reduce own/family's negative perception of pregnancy weight, so as to alleviate the body image dissatisfaction level of pregnant women.

Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study.

Background: A number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis. Methods: An investigative undertaking encompassing a genome-wide association study (GWAS) comprising 18,340 participants was undertaken with the aim of discerning genetic instrumental variables that exhibit associations with gut microbiota, The aggregated statistical data pertaining to acute pancreatitis (AP), alcohol-induced AP (AAP), chronic pancreatitis (CP), and alcohol-induced CP (ACP) were acquired from the FinnGen Consortium. The two-sample bidirectional Mendelian randomization (MR) approach was utilized. Utilizing the Inverse-Variance Weighted (IVW) technique as the cornerstone of our primary analysis. The Bonferroni analysis was used to correct for multiple testing, In addition, a number of sensitivity analysis methodologies, comprising the MR-Egger intercept test, the Cochran's Q test, MR polymorphism residual and outlier (MR-PRESSO) test, and the leave-one-out test, were performed to evaluate the robustness of our findings. Results: A total of 28 intestinal microflora were ascertained to exhibit significant associations with diverse outcomes of pancreatitis. Among them, Class Melainabacteria (OR = 1.801, 95% CI: 1.288-2.519, p = 0.008) has a strong causality with ACP after the Bonferroni-corrected test, in order to assess potential reverse causation effects, we used four types of pancreatitis as the exposure variable and scrutinized its impact on gut microbiota as the outcome variable, this analysis revealed associations between pancreatitis and 30 distinct types of gut microflora. The implementation of Cochran's Q test revealed a lack of substantial heterogeneity among the various single nucleotide polymorphisms (SNP). Conclusion: Our first systematic Mendelian randomization analysis provides evidence that multiple gut microbiota taxa may be causally associated with four types of pancreatitis disease. This discovery may contribute significant biomarkers conducive to the preliminary, non-invasive identification of Pancreatitis. Additionally, it could present viable targets for potential therapeutic interventions in the disease's treatment.

Gut microbiota controls the development of chronic pancreatitis: A critical role of short-chain fatty acids-producing Gram-positive bacteria.

Chronic pancreatitis (CP) is a progressive and irreversible fibroinflammatory disorder, accompanied by pancreatic exocrine insufficiency and dysregulated gut microbiota. Recently, accumulating evidence has supported a correlation between gut dysbiosis and CP development. However, whether gut microbiota dysbiosis contributes to CP pathogenesis remains unclear. Herein, an experimental CP was induced by repeated high-dose caerulein injections. The broad-spectrum antibiotics (ABX) and ABX targeting Gram-positive (G+) or Gram-negative bacteria (G-) were applied to explore the specific roles of these bacteria. Gut dysbiosis was observed in both mice and in CP patients, which was accompanied by a sharply reduced abundance for short-chain fatty acids (SCFAs)-producers, especially G+ bacteria. Broad-spectrum ABX exacerbated the severity of CP, as evidenced by aggravated pancreatic fibrosis and gut dysbiosis, especially the depletion of SCFAs-producing G+ bacteria. Additionally, depletion of SCFAs-producing G+ bacteria rather than G- bacteria intensified CP progression independent of TLR4, which was attenuated by supplementation with exogenous SCFAs. Finally, SCFAs modulated pancreatic fibrosis through inhibition of macrophage infiltration and M2 phenotype switching. The study supports a critical role for SCFAs-producing G+ bacteria in CP. Therefore, modulation of dietary-derived SCFAs or G+ SCFAs-producing bacteria may be considered a novel interventive approach for the management of CP.

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges.

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

Lipidomic analyses of five Carya illinoinensis cultivars.

Carya illinoinensis (Wangenh.) K. Koch, nuts are a renowned health food. However, there are many cultivars of this nut tree, and their mature kernel lipid composition has not been thoroughly studied. Therefore, we used liquid chromatography-mass spectrometry (LC-MS) to analyze the lipid composition of mature nuts of five C. illinoinensis cultivars. In the mature kernels of all cultivars, there were 58 lipid types which were mainly composed of glycerolipids (c. 65%) and phospholipids (>30%). Triacylglycerol (TG) accounted for the largest proportion of mature nuts of all cultivars, exceeding 50%; and diacylglycerol (DG), ceramide (Cer), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) were also relatively high. Additionally, nuts contain fatty acids, mainly oleic, linoleic, and linolenic acids. Our research provides a new perspective for the processing and utilization of plant and edible oils, and for the use of C. illinoinensis kernels in the development of medicine and food science.

Evaluation of a clinical pharmacist-led antimicrobial stewardship program in a neurosurgical intensive care unit: a pre-and post-intervention cohort study.

Background: Antimicrobial resistance poses a significant challenge in neurosurgical intensive care units (ICU). The excessive use of broad-spectrum antibiotics is closely linked to the emergence and dissemination of drug-resistant bacteria within neurosurgical ICUs. This study assessed the effects of implementing a comprehensive Antimicrobial Stewardship (AMS) program in a neurosurgical ICU setting. Methods: From April 2022 to September 2022, an AMS program was implemented in the neurosurgical ICU. The program involved the regular presence of a pharmacist and an infectious disease physician who conducted prospective audits and provided feedback. To assess the impact of the AMS program, the outcome measures were compared between the AMS period and the 6 months before AMS implementation (pre-AMS period). The primary outcome was the use of antibacterial agents, including anti-pseudomonal beta-lactams (APBLs), polymyxin, and tigecycline. Additionally, the study evaluated the appropriateness of antimicrobial de-escalation and the susceptibility of Gram-negative bacilli to antimicrobial agents. Results: A total of 526 were included during the AMS period, while 487 patients were included in the pre-AMS period. The two groups had no significant differences in disease severity and mortality rates. During the AMS period, there was a notable decrease in the use of APBLs as empiric treatment (43.92% vs. 60.99%, p < 0.001). Multi-drug resistant organism (MDRO) infections decrease significantly during AMS period (11.03% vs. 18.48%, p < 0.001). The number of prescription adjustment increased significantly in all patients (0 item vs. 0 item, p < 0.001) and MDRO-positive patients (3 items vs. 2 items, p < 0.001) during the AMS period. Additionally, appropriate antimicrobial de-escalation for patients with MDRO showed improvement during the AMS period (39.66% vs. 20%, p = 0.001). Polymyxin utilization also decreased during the AMS period (15.52% vs. 31.11%, p = 0.034). Furthermore, the susceptibility of Gram-negative Bacilli isolates to APBLs was significantly higher during the AMS period. Conclusion: Implementing a comprehensive pharmacist-led AMS program led to a decrease in the use of antibacterial agents. This reduction in usage is significant because it can potentially delay the emergence of bacterial resistance.

A network pharmacology and molecular docking approach to reveal the mechanism of Chaihu Anxin Capsule in depression.

Introduction: As one of the most frequently diagnosed mental disorders, depression is expected to become the most common disease worldwide by 2030. Previous studies have shown that Chaihu Anxin Capsule has powerful antidepressant effects. However, its mechanisms are not fully understood. The aim of our research is to reveal the mechanisms of Chaihu Anxin Capsule in treating depression. Methods: Information about the ingredients of the herb was gathered using the TCMSP. Genes associated with antidepressants were gathered from the GeneCards database. An "herbal-ingredient-target" network was constructed and analyzed using Cytoscape software. The PPI network of the antidepressant targets of Chaihu Anxin Capsule was constructed using the STRING database. KEGG pathway and GO enrichment were used to analyze the antidepressant targets. Molecular docking technology was used to confirm the capacity of the primary active ingredients of Chaihu Anxin Capsule to bind to central targets using AutoDock Vina and PyMOL software. Results: Network analysis showed that five targets might be therapeutic targets of Chaihu Anxin Capsule in depression, namely, JUN, IL6, AKT1, TP53, and STAT3. The gene enrichment analysis implied that Chaihu Anxin Capsule benefits patients with depression by modulating pathways related to lipids and atherosclerosis and the AGE-RAGE signaling pathway in diabetic complications. Molecular docking analyses revealed that JUN, IL6, AKT1, TP53, and STAT3 had good affinities for quercetin, beta-sitosterol and kaempferol. Conclusion: According to the bioinformatics data, the antidepressant effects of Chaihu Anxin Capsule may be primarily linked to cholesterol and atherosclerosis as well as the AGE-RAGE signaling pathway in diabetic complications. These results emphasize that the expected therapeutic targets may be possible indicators for antidepressant activity.

Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.

BACKGROUND: To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP). RESULTS: This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set. CONCLUSION: NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.