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Abstract Background Gestational diabetes mellitus (GDM) is a complication of pregnancy associated with numerous adverse outcomes. There may be a potential link between GDM and arsenic (As) exposure, but this hypothesis remains controversial. This meta-analysis summarizes the latest studies evaluating the association between As and GDM. Methods A comprehensive search of the PubMed, Embase, and Scopus databases up to September 2023 was performed. The pooled estimates with 95% CIs were presented using forest plots. Estimates were calculated with random effects models, and subgroup and sensitivity analyses were conducted to address heterogeneity. Results A total of 13 eligible studies involving 2575 patients with GDM were included in this meta-analysis. The results showed that women exposed to As had a significantly increased risk of GDM (OR 1.47, 95% CI: 1.11 to 1.95, P = 0.007). Subgroup analyses suggested that the heterogeneity might be attributed to the years of publication. In addition, sensitivity analysis confirmed the robust and reliable results. Conclusions This analysis suggested that women exposed to As have a greater risk of GDM. However, the significant heterogeneity across studies requires careful interpretation. Registration The PROSPERO registration ID is CRD42023461820.

Epidermal growth factor receptor inhibitors (EGFRIs) induced cutaneous toxicity is a common adverse event (AE), although it is not as severe as major cancers, we still need to pay enough attention to them. Therefore, it is necessary to evaluate the diversity of EGFRI class drugs. The objective of this study was to conduct a scientific and systematic investigation into the correlation between EGFRI and cutaneous toxicities. The data accessed from the FDA adverse event reporting system database (FAERS) encompass a time frame spanning from January 2013 to March 2023. By utilizing reporting odds ratios (RORs), information components (ICs), proportional reporting ratios (PRRs), and chi-squared (χ2), the relationship between drugs and adverse reactions was evaluated through disproportionality analysis. Within the FAERS database, a total of 29,559 skin adverse events were recorded. A robust indication of the correlation between EGFRI and elderly patients (≥65 years) was identified. Among EGFRIs, erlotinib accounted for the largest proportion of skin adverse events (39.72%). Rash, dry skin, and pruritus ranked top among all preferred terms, and signals such as rash, skin lesions, and acneiform dermatitis were detected in every single drug. Clinicians should guide patients customize the treatment plan for each patient.

Abstract Background Combinations of drugs are becoming increasingly common in oncology treatment. In some cases, patients can benefit from the interaction between two drugs, although there is usually a higher risk of developing toxicity. Due to drug–drug interactions, multidrug combinations often exhibit different toxicity profiles than those of single drugs, leading to a complex trial scenario. Numerous methods have been proposed for the design of phase I drug combination trials. For example, the two-dimensional Bayesian optimal interval design for combination drug (BOINcomb) is simple to implement and has desirable performance. However, in scenarios where the lowest and starting dose is close to being toxic, the BOINcomb design may tend to allocate more patients to overly toxic doses, and select an overly toxic dose combination as the maximum tolerated dose combination. Method To improve the performance of BOINcomb in the above extreme scenarios, we widen the range of variation of the boundaries by setting the self-shrinking dose escalation and de-escalation boundaries. We refer to the new design as adaptive shrinking Bayesian optimal interval design for combination drug (asBOINcomb). We conduct a simulation study to evaluate the performance of the proposed design using a real clinical trial example. Results Our simulation results show that asBOINcomb is more accurate and stable than BOINcomb, especially in some extreme scenarios. Specifically, in all ten scenarios, the percentage of correct selection is higher than the BOINcomb design within 30 to 60 patients. Conclusion The proposed asBOINcomb design is transparent and simple to implement and can reduce the trial sample size while maintaining accuracy compared with the BOINcomb design.

Objective: To get the standard system for evaluating the effectiveness of emergency response to food safety emergencies and the index for evaluating the response to food safety emergencies. Method: Each specific index content of the evaluation system was determined with the Delphi method. The interquartile distance and Kendall's consistency coefficient were applied as the statistical quantities for judging the consistency of experts’ opinions. The importance ratings of the indicators by experts were calculated to confirm the weights of each indicator, and the formula for calculating the food safety emergency response evaluation index was derived. Result: Consensus was obtained, if the IQR was less than 1 and Kendall’ W was larger than 0.3 (statistical significance was set at P

The complex processes of action of Chinese medicines and their formulations can be studied in terms of complex heterogeneous networks, however multiple diseases are different from one disease. In this study, we proposed a graph neural network-based modelling strategy to explore the material basis and mechanisms of the formulae under multiple disease conditions, exemplified by Xuefu Zhuyu Decoction for stable angina pectoris and unstable angina pectoris. Our modelling approach consists of two parts, a feature part and a model part. In the feature part, we calculated four similarities and their combinations in the similarity network as features of nodes of the heterogeneous network, using a variety of algorithms. In the model part, both the multi-label classification model for compounds and the link prediction model for compounds-targets were performed in six model comparison experiments and four partial ablation experiments. We confirmed that the GraphSAGE-based model was the best performing multi-label classification model while the GAT-based model was the best performing link prediction model, and that the full model with various similarity contents was optimal. Good generalization ability of the model was demonstrated through generalization ability experiments. In the case study, the model was used to predict potential compound-target interactions for marker targets and the reliability of the predictions was demonstrated by molecular docking and literature mining. In conclusion, the model successfully predicted the known material basis and mechanisms and also predicted unknown compound-marker target interactions. The proposed modelling approach can be extended to the study of other formulations.

Adverse events(AEs) related to hepatotoxicity have been reported in patients treated with immune checkpoint inhibitors (ICIs). As the number of adverse events increases, it is necessary to assess the differences in each immune checkpoint inhibitor regimen. The purpose of this study was to examine the relationship between ICIs and hepatotoxicity in a scientific and systematic manner. Data were obtained from the FDA Adverse Event Reporting System database (FAERS) and included data from the first quarter of 2014 to the fourth quarter of 2021. Disproportionality analysis assessed the association between drugs and adverse reactions based on the reporting odds ratio (ROR) and information components (IC). 9,806 liver adverse events were reported in the FAERS database. A strong signal was detected in older patients (≥65 years) associated with ICIs. hepatic adverse events were most frequently reported with Nivolumab (36.17%). Abnormal liver function, hepatitis, and autoimmune hepatitis were most frequently reported, and hepatitis and immune-mediated hepatitis signals were generated in all regimens. In clinical use, patients should be alert to these adverse effects, especially in elderly patients, who may be aggravated by the use of ICI.

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability.

Diterpenoid alkaloids are extracted from plants. These compounds have broad biological activities, including effects on the cardiovascular system, anti-inflammatory and analgesic actions, and anti-tumor activity. The anti-inflammatory activity was determined by carrageenan-induced rat paw edema and experimental trauma in rats. The number of studies focused on the determination, quantitation and pharmacological properties of these alkaloids has increased dramatically during the past few years. In this work we built a dataset composed of 15 diterpenoid alkaloid compounds with diverse structures, of which 11 compounds were included in the training set and the remaining compounds were included in the test set. The quantitative chemistry parameters of the 15 diterpenoid alkaloids compound were calculated using the HyperChem software, and the quantitative structure–activity relationship (QSAR) of these diterpenoid alkaloid compounds were assessed in an anti-inflammation model based on half maximal effective concentration (EC50) measurements obtained from rat paw edema data. The QSAR prediction model is as follows: log ( E C 50 ) = − 0.0260 × SAA + 0.0086 × SAG + 0.0011 × VOL − 0.0641 × HE − 0.2628 × LogP − 0.5594 × REF − 0.2211 × POL − 0.1964 × MASS + 0.088 × BE + 0.1398 × HF (R2 = 0.981, Q2 = 0.92). The validated consensus EC50 for the QSAR model, developed from the rat paw edema anti-inflammation model used in this study, indicate that this model was capable of effective prediction and can be used as a reliable computational predictor of diterpenoid alkaloid activity.

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.

Nephrotoxicity occasionally occurs during treatment with immune checkpoint inhibitors (ICIs). Few related studies compare the differences between these drugs. This study aimed to systematically characterize nephrotoxicity after ICI initiation. Data were extracted from the US FDA Adverse Event Reporting System (FAERS) database. Disproportionality analysis, including information components (ICs) and reporting odds ratios (RORs), was performed to determine the potential renal toxicity of ICIs. A total of 7,204 reports of renal adverse events (AEs) were identified in the FAERS database. Renal AEs were most commonly reported for nivolumab (46.84%). Strong signals were detected in male patients combined with ICIs. In the clinical application of ICIs, attention should be paid to patients, especially male patients, with acute kidney injury, nephritis, autoimmune nephritis and other nephrotoxic AEs. The use of ICIs is likely to aggravate their condition. [ABSTRACT FROM AUTHOR]

Currently, liver transplantation has reached a level of maturity where it is considered an effective treatment for end-stage liver disease and can significantly prolong the survival time of patients. However, acute and chronic rejection remain major obstacles to its efficacy. Although long-term use of immunosuppressants can prevent rejection, it is associated with serious side effects and significant economic burden for patients. Therefore, the investigation of induced immune tolerance holds crucial theoretical significance and socio-economic value. In fact, the establishment of immune tolerance in liver transplantation is intricately linked to the unique innate immune system of the liver. Kupffer cells, as a crucial component of this system, play a pivotal role in maintaining the delicate balance between inflammatory response and immune tolerance following liver transplantation. The important roles of different functions of Kupffer cells, such as phagocytosis, cell polarization, antigen presentation and cell membrane proteins, in the establishment of immune tolerance after transplantation is comprehensively summarized in this paper. Providing theoretical basis for further study and clinical application of Kupffer cells in liver transplantation.

Abstract. Background:. As a non-invasive and effective diagnostic method for small intestinal bacterial overgrowth (SIBO), wild-use of breath test (BT) has demonstrated a high comorbidity rate in patients with diarrhea-predominant irritable bowel syndrome (IBS-D) and SIBO. Patients overlapping with SIBO respond better to rifaximin therapy than those with IBS-D only. Gut microbiota plays a critical role in both of these two diseases. We aimed to determine the microbial difference between IBS-D overlapping with/without SIBO, and to study the underlying mechanism of its sensitivity to rifaximin. Methods:. Patients with IBS-D were categorized as BT-negative (IBSN) and BT-positive (IBSP). Healthy volunteers (BT-negative) were enrolled as healthy control. The patients were clinically evaluated before and after rifaximin treatment (0.4 g bid, 4 weeks). Blood, intestine, and stool samples were collected for cytokine assessment and gut microbial analyses. Results:. Clinical complaints and microbial abundance were significantly higher in IBSP than in IBSN. In contrast, severe systemic inflammation and more active bacterial invasion function that were associated with enrichment of opportunistic pathogens were seen in IBSN. The symptoms of IBSP patients were relieved in different degrees after therapy, but the symptoms of IBSN rarely changed. We also found that the presence of IBSN-enriched genera (Enterobacter and Enterococcus) are unaffected by rifaximin therapy. Conclusions:. IBS-D patients overlapping with SIBO showed noticeably different fecal microbial composition and function compared with IBS-D only. The better response to rifaximin in those comorbid patients might associate with their different gut microbiota, which suggests that BT is necessary before IBS-D diagnosis and use of rifaximin. Registration:. Chinese Clinical Trial Registry, ChiCTR1800017911.

Irritable bowel syndrome (IBS) is the most prevalent functional gastrointestinal disorder presenting a high comorbidity with depressive disorder (DD). Many studies have confirmed that these two disease share the similar pathophysiological process, but evidence of the genetic risks is limited. This study aimed to analyze the genetic susceptibilities for IBS and DD in Chinese patients. Pooled whole-exome sequencing (pooled-WES) was performed to identify the candidate variants in the group of diarrhea predominant IBS (IBS-D) patients, DD patients, and healthy controls (HC). Then, targeted sequencing was used to validate the candidate variants in three additional cohorts of IBS-D, DD, and HC. Four variants associated with both IBS-D and DD were identified through pooled-WES, and three of them were validated in targeted sequencing. SYT8 rs3741231 G allele and SSPO rs12536873 TT genotype were associated with both IBS-D and DD. The genes of these variants are important in neurogenesis and neurotransmission. In addition, we found COL6A1 rs13051496, a unique risk variation for IBS-D. It increased the IBS-D risk and had a positive correlation with the scores of abdominal bloating and dissatisfaction of bowel habits. Through the results of this study, it provides a genetic basis for the high comorbidity of IBS-D and DD. Keywords: Irritable bowel syndrome, Depressive disorder, Pooled-sequencing, Whole-exome sequencing, Genetic susceptibilities

Background: In investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR). Results: We considered five LFDR estimators for detecting enriched GO terms: a binomial-based estimator (BBE), a maximum likelihood estimator (MLE), a normalized MLE (NMLE), a histogram-based estimator assuming a theoretical null hypothesis (HBE), and a histogram-based estimator assuming an empirical null hypothesis (HBE-EN). Since NMLE depends not only on the data but also on the specified value of π0, the proportion of non-enriched GO terms, it is only advantageous when either π0 is already known with sufficient accuracy or there are data for only 1 GO term. By contrast, the other estimators work without specifying π0 but require data for at least 2 GO terms. Our simulation studies yielded the following summaries of the relative performance of each of those four estimators. HBE and HBE-EN produced larger biases for 2, 4, 8, 32, and 100 GO terms than BBE and MLE. BBE has the lowest bias if π0 is 1 and if the number of GO terms is between 2 and 32. The bias of MLE is no worse than that of BBE for 100 GO terms even when the ideal number of components in its underlying mixture model is unknown, but has high bias when the number of GO terms is small compared to the number of estimated parameters. For unknown values of π0, BBE has the lowest bias for a small number of GO terms (2-32 GO terms), and MLE has the lowest bias for a medium number of GO terms (100 GO terms). Conclusions: For enrichment detection, we recommend estimating the LFDR by MLE given at least a medium number of GO terms, by BBE given a small number of GO terms, and by NMLE given either only 1 GO term or precise knowledge of π0. [ABSTRACT FROM AUTHOR]

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA - 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability. [ABSTRACT FROM AUTHOR]

Objective: To assess the long-term effectiveness of Huangqi ( Radix Astragali Mongolici , HQ)-based Traditional Chinese Medicine (TCM) in the treatment of diabetic peripheral neuropathy (DPN)., Methods: Nine databases were searched to retrieve available randomized controlled trials that compared HQ-based TCM and Western Medicines in the treatment of DPN. The methodological quality of the included studies was assessed using the Cochrane bias risk tool, and RevMan 5.4 was used for data analysis. The effect estimates of interest were risk ratio ( RR ), mean difference ( MD ) or standardized mean difference ( SMD ) with 95% confidence interval ( CI )., Results: The results from 48 available studies assessing 3759 patients demonstrated that cases administered HQ-based TCM [ RR = 1.30, 95% CI (1.21, 1.40), P < 0.000 01] or HQ-based TCM combined with Western Medicines [ RR = 1.25, 95% CI (1.19, 1.31), P < 0.000 01] exhibited higher total efficacy rates than individuals who received Western Medicine alone. The results showed that the HQ-based TCM group had decreased Toronto Clinical Scoring System scores [ MD =－1.50, 95% CI (－1.83, －1.17), P < 0.000 01], and reduced serum interleukin 6 [S MD = －0.57, 95% CI (－0.87, －0.27), P = 0.0002] and tumor necrosis factors-α levels [S MD = －0.60, 95% CI (－0.95, －0.25), P = 0.0009]. In addition, both HQ-based TCM and HQ-based TCM combined with Western Medicine increased nerve conduction velocity and decreased glycaemia compared with Western Medicine alone. In terms of blood lipids, oxidative stress and adverse drug reactions, there were no significant differences between the HQ-based TCM groups and the Western Medicine control group., Conclusion: The current Meta-analysis revealed that HQ-based TCM yields higher efficacy and safety than Western Medicine alone for the treatment of DPN, although further well-designed RCTs are required to validate these findings.