Comparative effects of glucose-lowering agents on endothelial function and arterial stiffness in patients with type 2 diabetes: A network meta-analysis

BACKGROUND AND AIMS

Despite accumulating evidence on the potential of glucose-lowering agents (GLAs) to prevent cardiovascular events, the comparative effects of GLAs on vascular function remain unclear. This study utilized validated indicators such as flow-mediated dilation (FMD; positive value favors) and pulse wave velocity (PWV; negative value favors) to uncover the comparative effects of GLAs on vascular function.

METHODS

Randomized controlled trials (RCTs) comparing the effects of GLAs on FMD or PWV with placebo or other GLAs in patients with type 2 diabetes (T2DM) were searched through PubMed and Embase. The frequentist method of network meta-analysis (NMA) was conducted using a random effects model, and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated.

RESULTS

The NMA included 38 RCTs with 2,065 patients. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium glucose cotransporter-2 inhibitors (SGLT-2Is) had significantly more positive effects on FMD improvement and PWV reduction than placebo. Thiazolidinedione (TZD) treatment resulted in significantly improved FMD compared to other GLAs as well as placebo (SMD: 1.14; 95% CI: 0.84 to 1.43). Both pioglitazone and rosiglitazone were discovered to have considerably more favorable effects on improving FMD and reducing PWV compared to placebo and other GLAs, as a result of the analysis incorporating each drug in the TZD class. The sensitivity analysis results corroborated the main findings.

CONCLUSIONS

This NMA showed more favorable effects of GLP-1RAs and SGLT-2Is than placebo in improving both arterial stiffness and endothelial function in patients with T2DM. In addition, TZDs showed superior effects in improving endothelial function as compared with the other GLAs and placebo.

Cardioprotective Effects of PARP Inhibitors: A Re-Analysis of a Meta-Analysis and a Real-Word Data Analysis Using the FAERS Database

Objective: This study aimed to assess the potential of PARP inhibitors to prevent cardiotoxicity. Methods: First, a re-analysis and update of a previously published study was conducted. Additional searches were conducted of the PubMed and Cochrane Central Register of Controlled Trials databases on 2 June 2023. After the selection process, the pooled odds ratio (OR) for cardiac adverse events (AEs) was calculated. Second, the FAERS database was examined for 10 frequently co-administered anticancer agents. The reporting odds ratio (ROR) was calculated based on the occurrence of cardiac AEs depending on the co-administration of PARP inhibitors. Results: Seven studies were selected for the meta-analysis. Although not statistically significant, co-administration of PARP inhibitors with chemotherapy/bevacizumab decreased the risk of cardiac AEs (Peto OR = 0.61; p = 0.36), while co-administration with antiandrogens increased the risk of cardiac AEs (Peto OR = 1.83; p = 0.18). A total of 19 cases of cardiac AEs were reported with co-administration of PARP inhibitors in the FAERS database. Co-administration of PARP inhibitors with chemotherapy/bevacizumab significantly decreased the risk of cardiac AEs (ROR = 0.352; 95% confidence interval (CI), 0.194-0.637). On the other hand, for antiandrogens co-administered with PARP inhibitors, the ROR was 3.496 (95% CI, 1.539-7.942). The ROR for immune checkpoint inhibitors co-administered with PARP inhibitors was 0.606 (95% CI, 0.151-2.432), indicating a non-significant effect on cardiac AEs. Conclusion: This study reports that PARP inhibitors show cardioprotective effects when used with conventional anticancer agents.

Integration of the Natural Language Processing of Structural Information Simplified Molecular-Input Line-Entry System Can Improve the In Vitro Prediction of Human Skin Sensitizers

Natural language processing (NLP) technology has recently used to predict substance properties based on their Simplified Molecular-Input Line-Entry System (SMILES). We aimed to develop a model predicting human skin sensitizers by integrating text features derived from SMILES with in vitro test outcomes. The dataset on SMILES, physicochemical properties, in vitro tests (DPRA, KeratinoSensTM, h-CLAT, and SENS-IS assays), and human potency categories for 122 substances sourced from the Cosmetics Europe database. The ChemBERTa model was employed to analyze the SMILES of substances. The last hidden layer embedding of ChemBERTa was tested with other features. Given the modest dataset size, we trained five XGBoost models using subsets of the training data, and subsequently employed bagging to create the final model. Notably, the features computed from SMILES played a pivotal role in the model for distinguishing sensitizers and non-sensitizers. The final model demonstrated a classification accuracy of 80% and an AUC-ROC of 0.82, effectively discriminating sensitizers from non-sensitizers. Furthermore, the model exhibited an accuracy of 82% and an AUC-ROC of 0.82 in classifying strong and weak sensitizers. In summary, we demonstrated that the integration of NLP of SMILES with in vitro test results can enhance the prediction of health hazard associated with chemicals.

Enhancement of the therapeutic efficacy of the MAP regimen using thiamine pyrophosphate-decorated albumin nanoclusters in osteosarcoma treatment

Recent studies on osteosarcoma regimens have mainly focused on modifying the combination of antineoplastic agents rather than enhancing the therapeutic efficacy of each component. Here, an albumin nanocluster (NC)-assisted methotrexate (MTX), doxorubicin (DOX), and cisplatin (MAP) regimen with improved antitumor efficacy is presented. Human serum albumin (HSA) is decorated with thiamine pyrophosphate (TPP) to increase the affinity to the bone tumor microenvironment (TME). MTX or DOX (hydrophobic MAP components) is adsorbed to HSA-TPP via hydrophobic interactions. MTX- or DOX-adsorbed HSA-TPP NCs exhibit 20.8- and 1.64-fold higher binding affinity to hydroxyapatite, respectively, than corresponding HSA NCs, suggesting improved targeting ability to the bone TME via TPP decoration. A modified MAP regimen consisting of MTX- or DOX-adsorbed HSA-TPP NCs and free cisplatin displays a higher synergistic anticancer effect in HOS/MNNG human osteosarcoma cells than conventional MAP. TPP-decorated NCs show 1.53-fold higher tumor accumulation than unmodified NCs in an orthotopic osteosarcoma mouse model, indicating increased bone tumor distribution. As a result, the modified regimen more significantly suppresses tumor growth in vivo than solution-based conventional MAP, suggesting that HSA-TPP NC-assisted MAP may be a promising strategy for osteosarcoma treatment.

Effects of very low-carbohydrate ketogenic diets on lipid profiles in normal-weight (body mass index < 25 kg/m2) adults: a meta-analysis

CONTEXT

Very low-carbohydrate diets or ketogenic diets (KDs) have garnered attention for weight loss in patients with overweight or obesity as well as for normal-weight adults, yet the adverse effects of KDs, such as dyslipidemia in normal-weight adults, have not been studied extensively.

OBJECTIVE

This meta-analysis aimed to identify the effects of KDs on the lipid profile in normal-weight (body mass index [BMI] < 25 kg/m2) adults from randomized controlled trials.

DATA SOURCES

PubMed and Embase databases were searched on November 21, 2021, using search terms representing KDs and lipid profiles. Two researchers independently screened articles according to PICOS inclusion criteria.

DATA EXTRACTION

General study information, dietary data, and lipid profiles were extracted from eligible studies. Risk of bias was assessed using the Cochrane risk of bias 2 tool.

DATA ANALYSIS

Fixed- or random-effects meta-analysis was performed to estimate the effects of KDs on total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), triglycerides, apolipoprotein A (apoA), and apolipoprotein B (apoB), considering heterogeneity across studies. The certainty of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach.

RESULTS

Three studies were selected for meta-analysis. A KD significantly increased TC by 1.47 mmol/L (95%CI, 0.72-2.22 mmol/L), LDL-C by 1.08 mmol/L (95%CI, 0.37-1.79 mmol/L), and apoB by 0.35 g/L (95%CI, 0.06-0.65 g/L). In addition, a KD significantly increased HDL-C by 0.35 mmol/L (95%CI, 0.27-0.42 mmol/L) and apoA by 0.34 g/L (95%CI, 0.28-0.41 g/L) compared with control diets. Triglyceride levels were not significantly different between KDs and control diets (P = 0.63).

CONCLUSION

This study suggests unfavorable effects of KDs on TC and LDL-C in normal-weight adults. Although an increase in HDL-C can compensate for unfavorable changes in lipids, normal-weight individuals should consider the risk of hypercholesterolemia when consuming a KD. Results for triglycerides were inconsistent.

Effects of Omega-3 Fatty Acids on Flow-mediated Dilatation and Carotid Intima Media Thickness: A Meta-analysis

PURPOSE OF REVIEW

To investigate the effects of omega-3 fatty acids on flow-mediated dilatation (FMD) and carotid intima-media thickness (CIMT) and explore the factors influencing these effects.

RECENT FINDINGS

FMD was significantly higher in the omega-3 fatty acid group compared to the control group (mean difference = 0.90%; p = 0.0003). In particular, the subgroup with CHD (both EPA + DHA < 1 g/day and ≥ 1 g/day) and the subgroup without CHD but with CHD risk factors (only EPA + DHA ≥ 1 g/day) showed significantly increased FMD after supplementation of omega-3 fatty acids. CIMT was not significantly different between the omega-3 fatty acid and control groups (standardized mean difference = -0.08; p = 0.26). Subgroup analysis of CHD patients was not conducted because of the limited number of studies. Intake of omega-3 fatty acids improved FMD in patients with CHD and patients with risk factors for CHD. Further research is needed on the effects of omega-3 fatty acids on CIMT.

Risk of type 2 diabetes mellitus in adult patients with atopic dermatitis

AIMS

This study aimed to investigate the subsequent risk of type 2 diabetes mellitus (T2D) in adults newly diagnosed with atopic dermatitis (AD).

METHODS

This propensity score-matching cohort study used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) 2.0 database in South Korea from 2002 to 2015. The adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using a Cox proportional hazards model, for the new onset of T2D (ICD-10 code, E11) in AD patients compared to the matched controls. Subgroup and sensitivity analyses were also conducted.

RESULTS

Each of the 36,692 individuals in the AD group and matched control group was included in the analysis. The risk of T2D in the AD group was significantly higher than that of the matched controls in the adjusted model (adjusted HR 1.44; 95% CI 1.27-1.63, P <.001). The results of subgroup analysis by sex, age, and body mass index were consistent with the results of the primary analysis. Sensitivity analyses using different T2D and/or AD definitions also showed consistent results.

CONCLUSIONS

The significant risk of subsequent T2D in adult AD patients suggested the necessity for efforts to prevent T2D in AD patients.

Novel innovative computer-based test (Inno-CBT) item types for national licensing examinations for health care professionals

BACKGROUND

An effective test mechanism to evaluate clinical knowledge and skills of the entry-level healthcare professionals is important for providing clinical competency and improving patient care. This study aimed to develop novel, innovative computer-based test (Inno-CBT) item types for application in the national examination of Korean healthcare professionals.

METHODS

This exploratory study was conducted from May 2021 to March 2022 by a team of faculty members from pharmacy schools in South Korea. A literature search using PubMed, Google Scholar, RISS, Web of Science, and KoreaMed was performed. Forum presentations, media articles, and previous reports by the Korea Health Personnel Licensing Examination Institute (KHPLEI) were included. Workshops were held, information and ideas were collected and conceptualized, and item types were designed, drafted, and refined. By repeating this process, the Inno-CBT item types were finalized.

RESULTS

Forty-one Inno-CBT item types with 28 subtypes were developed. New digital technologies, such as a reactive responsive media interface, an animation insertion, multimedia embedding, and network surfing, were utilized in these novel types. It was anticipated that these Inno-CBT item types would effectively measure abilities in healthcare knowledge, problem-solving skills, and professional behaviors. Some potential barriers to implementing the Inno-CBT item types include item difficulty, operational unfamiliarity, complexity in scoring protocols, and network security.

CONCLUSIONS

A variety of styles of novel Inno-CBT item types were developed to evaluate the multifaceted and in-depth professional abilities required for healthcare professionals. Prior to implementing these item types in the national examination, item validation and technical support should be conducted.

Clinical impacts of the concomitant use of L-asparaginase and total parenteral nutrition containing L-aspartic acid in patients with acute lymphoblastic leukemia

Introduction

L-asparaginase (ASNase) depletes L-asparagine and causes the death of leukemic cells, making it a mainstay for the treatment of acute lymphoblastic leukemia (ALL). However, ASNase's activity can be inhibited by L-aspartic acid (Asp), which competes for the same substrate and reduces the drug's efficacy. While many commercially used total parenteral nutrition (TPN) products contain Asp, it is unclear how the concomitant use of TPNs containing Asp (Asp-TPN) affects ALL patients treated with ASNase. This propensity-matched retrospective cohort study evaluated the clinical effects of the interaction between ASNase and Asp-TPN.

Methods

The study population included newly diagnosed adult Korean ALL patients who received VPDL induction therapy consisting of vincristine, prednisolone, daunorubicin, and Escherichia coli L-asparaginase between 2004 and 2021. Patients were divided into two groups based on their exposure to Asp-TPN: (1) Asp-TPN group and (2) control group. Data, including baseline characteristics, disease information, medication information, and laboratory data, were collected retrospectively. The primary outcomes for the effectiveness were overall and complete response rates. Relapse-free survival at six months and one year of treatment were also evaluated. The safety of both TPN and ASNase was evaluated by comparing liver function test levels between groups. A 1:1 propensity score matching analysis was conducted to minimize potential selection bias.

Results

The analysis included a total of 112 ALL patients, and 34 of whom received Asp-TPN and ASNase concomitantly. After propensity score matching, 30 patients remained in each group. The concomitant use of Asp-TPN and ASNase did not affect the overall response rate (odds ratio [OR] 0.53; 95% confidence interval [CI] = 0.17–1.62) or the complete response rate (OR 0.86; 95% CI = 0.29–2.59) of the ASNase-including induction therapy. The concomitant use of Asp-TPN and ASNase also did not impact relapse-free survival (RFS) at six months and one year of treatment (OR 1.00; 95% CI = 0.36–2.78 and OR 1.24; 95% CI, 0.50–3.12, respectively). The peak levels of each liver function test (LFT) and the frequency of LFT elevations were evaluated during induction therapy and showed no difference between the two groups.

Conclusion

There is no clear rationale for avoiding Asp-TPN in ASNase-treated patients.

Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost

Skin irritation test is an essential part of the safety assessment of chemicals. Recently, computational models to predict the skin irritation draw attention as alternatives to animal testing. We developed prediction models on skin irritation/corrosion of liquid chemicals using machine learning algorithms, with 34 physicochemical descriptors calculated from the structure. The training and test dataset of 545 liquid chemicals with reliable in vivo skin hazard classifications based on UN Globally Harmonized System [category 1 (corrosive, Cat 1), 2 (irritant, Cat 2), 3 (mild irritant, Cat 3), and no category (nonirritant, NC)] were collected from public databases. After the curation of input data through removal and correlation analysis, every model was constructed to predict skin hazard classification for liquid chemicals with 22 physicochemical descriptors. Seven machine learning algorithms [Logistic regression, Naïve Bayes, k-nearest neighbor, Support vector machine, Random Forest, Extreme gradient boosting (XGB), and Neural net] were applied to ternary and binary classification of skin hazard. XGB model demonstrated the highest accuracy (0.73-0.81), sensitivity (0.71-0.92), and positive predictive value (0.65-0.81). The contribution of physicochemical descriptors to the classification was analyzed using Shapley Additive exPlanations plot to provide an insight into the skin irritation of chemicals.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43188-022-00168-8.

Electrocatalytic Properties of Co3O4 Prepared on Carbon Fibers by Thermal Metal-Organic Deposition for the Oxygen Evolution Reaction in Alkaline Water Electrolysis

Cobalt oxide (Co₃O₄) serves as a promising electrocatalyst for oxygen evolution reactions (OER) in water-electrolytic hydrogen production. For more practical applications, advances in dry-deposition processes for the high-throughput fabrication of such Co₃O₄ electrocatalysts are needed. In this work, a thermal metal-organic deposition (MOD) technique is developed to form Co₃O₄ deposits on microscale-diameter carbon fibers constituting a carbon fiber paper (CFP) substrate for high-efficiency OER electrocatalyst applications. The Co₃O₄ electrocatalysts are deposited while uniformly covering the surface of individual carbon fibers in the reaction temperature range from 400 to 800 °C under an ambient Ar atmosphere. It is found that the microstructure of deposits is dependent on the reaction temperature. The Co₃O₄ electrocatalysts prepared at 500 °C and over exhibit values of 355-384 mV in overpotential (η₁₀) required to reach a current density of 10 mA cm^-2 and 70-79 mV dec^-1 in Tafel slope, measured in 1 M KOH aqueous solution. As a result, it is highlighted that the improved crystallinity of the Co₃O₄ electrocatalyst with the increased reaction temperature leads to an enhancement in electrode-level OER activity with the high electrochemically active surface area (ECSA), low charge transfer resistance (R_ct), and low η₁₀, due to the enhanced electrical conductivity. On the other hand, it is found that the inherent catalytic activity of the surface sites of the Co₃O₄, represented by the turnover frequency (TOF), decreases with reaction temperature due to the high-temperature sintering effect. This work provides the groundwork for the high-throughput fabrication and rational design of high-performance electrocatalysts.

Comparison of glucagon-like peptide-1 receptor agonists and thiazolidinediones on treating nonalcoholic fatty liver disease: A network meta-analysis

Background/Aims

Previous studies have revealed that glucagon-like peptide-1 receptor agonist (GLP-1RA) and thiazolidinedione (TZD) can improve nonalcoholic fatty liver disease (NAFLD) or nonalcoholic steatohepatitis (NASH). However, comprehensive research comparing the effects of GLP-1RA and TZD is limited. Thus, this study aimed to compare the effects of GLP-1RA and TZD on NAFLD or NASH through a network meta-analysis.

Methods

The PubMed, Embase, Web of Science, and Scopus databases were searched for randomized controlled trials (RCTs) that explored the efficacy of GLP-1RAs or TZDs in adult patients with NAFLD or NASH. The outcomes were liver biopsy-based (NAFLD activity score [NAS], fibrosis stage, and NASH resolution), noninvasive technique-based (liver fat content on proton magnetic resonance spectroscopy [1H-MRS] and controlled attenuation parameter [CAP]), biological, and anthropometric indicators. A random effects model was used to calculate the mean difference (MD) and relative risk with 95% confidence interval (CI).

Results

Twenty-five RCTs with 2,237 overweight or obese patients were included. GLP-1RA was significantly superior in reducing liver fat content evaluated using 1H-MRS (MD -2.42, 95% CI -3.84 to -1.00), body mass index (MD -1.60, 95% CI -2.41 to -0.80), and waist circumference (MD -4.89, 95% CI -8.17 to -1.61) than TZD. In liver biopsy-based evaluation and liver fat content assessment using CAP, GLP-1RA tended to surpass TZD, albeit not significantly. Sensitivity analysis showed consistent results with the main results.

Conclusions

Compared with TZD, GLP-1RA had better effects on liver fat content, body mass index, and waist circumference in overweight or obese patients with NAFLD or NASH.

Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as 'misinformation' and 'other.' We fine-tuned five BERT models (BERT_BASE, BERT_LARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897-0.911, while BERT_BASE and BERT_LARGE achieved accuracy of 0.887-0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter.

The risk of pulmonary adverse drug reactions of rebamipide and other drugs for acid-related diseases: An analysis of the national pharmacovigilance database in South Korea

OBJECTIVE

The objective of this case/non-case study was to detect rebamipide-related pulmonary adverse events (AE) compared with other drugs for acid-related disorders based on population-level data.

METHODS

From 2009 to 2018, AE reports on drugs for acid-related disorders, which are anatomical therapeutic chemical code A02B drugs, in the Korea Adverse Events Reporting System (KAERS) database were examined. The reporting odds ratio (ROR) was calculated, and the odds of reporting pulmonary AE for rebamipide and all other A02B drugs were compared. Furthermore, a stratified analysis according to patients' age and sex was conducted.

RESULTS

Altogether 13 (0.05%) and 157 (0.11%) cases of pulmonary AE were reported for rebamipide and all other A02B drugs, respectively. The risk of reporting pulmonary AE was significantly lower for rebamipide than for all other A02B drugs (ROR 0.49, 95% confidence interval [CI] 0.28-0.87). The number of reports of pulmonary AE for rebamipide was significantly higher among patients aged ≥65 years than those aged <65 years (ROR 19.36, 95% CI 2.50-149.97).

CONCLUSIONS

Rebamipide was less often reported for pulmonary AE. However, healthcare professionals need to be aware of the risk of pulmonary AE in elderly patients.

Participation in and withdrawal from cancer clinical trials: A survey of clinical research coordinators

Objective

Poor accrual and withdrawal are the main reasons for the failure of cancer clinical trials. As clinical research coordinators (CRCs) work at the frontlines of clinical trials, CRCs can best identify the main factors that influence patient participation and dropout and suggest potential remedial measures. This study aimed to investigate participation and withdrawal in cancer clinical trials through a survey of CRCs. Furthermore, we collected suggestions of CRCs to increase patient participation and reduce withdrawal from cancer clinical trials.

Methods

This cross-sectional survey among 100 CRC nurses currently coordinating cancer clinical trials and having more than six months of experience was conducted at four hospitals in South Korea between March and August 2021. We designed a questionnaire based on prior studies, and the key items included characteristics of respondents, characteristics of clinical trials, clinical trial participation, and withdrawal.

Results

Patients refused to participate due to concern about adverse events (46.5%) and negative perception of clinical trials (44.4%). The main reasons for study withdrawal were disease progression (71.5%), adverse events (10.6%), and withdrawal of consent due to personal issues (5.5%). The provision of sufficient explanation was suggested as a remedial measure for increasing consent to participate (67.4%) and reducing withdrawal (21.8%).

Conclusions

A survey of CRCs revealed the reasons governing patient participation and withdrawal in cancer clinical trials, thereby providing a novel insight into strategies for promoting subject enrollment and reducing withdrawal from cancer clinical trials.

Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models

This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C‐index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C‐index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C‐index comparable to the Cox proportional hazard model. The C‐index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women.

Antihypertensive drug use and psoriasis: A systematic review, meta- and network meta-analysis

AIMS

Diverse genetic and/or external factors may induce psoriasis. Drug exposure is 1 such prominent external factor; antihypertensive drugs are reportedly associated with psoriasis, but study results have been inconsistent. In this context, we investigated the associations between antihypertensive drugs and incidence if psoriasis via a systematic literature review and meta-analysis.

METHODS

Literature search in databases such as PubMed, Embase and Web of Science was conducted on 8 January 2021, and obtained data were pooled for meta- and network meta-analysis. Fixed- or random effect models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for evaluating the strength of the associations between antihypertensive drugs and psoriasis incidence. In addition to meta-analysis, Bayesian network meta-analysis was performed. ResultsThirteen eligible studies were included for meta-analysis with 6 378 116 individuals and 8 studies for network meta-analysis with 5 615 918 individuals. All antihypertensive drugs were significantly associated with psoriasis incidence. In a meta-analysis, the pooled ORs were 1.67 (95% CI: 1.31-2.13) for angiotensin-converting enzyme (ACE) inhibitors, 1.40 (95% CI: 1.20-1.63) for β-blockers, 1.53 (95% CI: 1.23-1.89) for calcium-channel blockers (CCBs), and 1.70 (95% CI: 1.40-2.06) for thiazide diuretics. For the comparative risks of psoriasis among antihypertensive drugs in the network meta-analysis, ORs were 2.09 (95% CI: 1.39-3.18) for ACE inhibitors, 1.35 (95% CI: 0.99-1.91) for BBs, 1.53 (95% CI: 1.07-2.24) for CCBs and 1.80 (95% CI: 1.23-2.66) for thiazide diuretics.

CONCLUSION

This study confirmed the associations between antihypertensive drugs and psoriasis; ACE inhibitors, BBs, CCBs and thiazide diuretics increased the risk of psoriasis. Therefore, antihypertensive drug users should be carefully monitored for psoriasis.

The Use of Social Media in Detecting Drug Safety-Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review

Background

Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required.

Objective

This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance.

Methods

This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized.

Results

A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing.

Conclusions

Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.

Lipid-modifying effects of krill oil vs fish oil: a network meta-analysis

CONTEXT

Krill oil is a good source of n-3 phospholipids and has greater bioavailability than fish oil, which contains n-3 triglycerides. However, it is unclear whether krill oil affects circulating lipid concentrations more beneficially than fish oil.

OBJECTIVE

A network meta-analysis was conducted to compare the lipid-modifying effects of krill oil and fish oil.

DATA SOURCES

PubMed and Embase databases were searched.

STUDY SELECTION

A total of 64 randomized controlled trials that determined the lipid-modifying effects of krill oil or fish oil were selected.

DATA EXTRACTION

The MetaXL program was used for meta-analysis. A subgroup analysis and a network meta-regression were conducted to investigate the dose-response effect of the n-3 fatty acid content of fish oil and krill oil.

RESULTS

Krill oil was associated with significantly lower triglyceride levels than control supplements (weighted mean difference [WMD] -23.26 [95%CI, -38.84 to -7.69]). However, the net differences in triglycerides (WMD -4.07 [95%CI, -15.22 to 7.08]), low-density lipoprotein cholesterol (WMD 3.01 [95%CI, -5.49 to 11.51]), high-density lipoprotein cholesterol (WMD 1.37 [95%CI, -3.73 to 6.48]), and total cholesterol (WMD 1.69 [95%CI, -6.62 to 10.01]) were not significantly different between the krill oil and fish oil groups. One gram of n-3 fatty acids contained in fish oil and krill oil lowered median triglycerides by 8.971 mg/dL (95% credible interval [CrI], 2.27 to 14.04) and 9.838 mg/dL (95%CrI, 0.72 to 19.40), respectively.

CONCLUSIONS

The lipid-modifying effects of krill oil and fish oil do not differ. The reduction in triglycerides depends on the dose of n-3 fatty acids consumed.

Gender Equity and Vertically Transmitted Infections: A Country-Level Analysis Across 153 Countries

Purpose: Gender inequality is a barrier to education toward women and accessibility to health facilities, which are important for preventing vertical transmission. This study was conducted to analyze the impact of gender equity on vertically transmitted infections (hepatitis viruses, human immunodeficiency virus [HIV], and syphilis) using country-level indicators.

Methods: The relationship between the Global Gender Gap Index (GGGI), which is indicator of gender equity, and vertical transmission was analyzed. GGGI scores were collected from 153 countries in 2020. Vertical transmission included 10 outcomes for hepatitis viruses, HIV, and syphilis. Generalized linear model (GLM) was used for analyzing the relationship. Other predictors included skilled birth attendant and country income.

Results: The median GGGI score was 0.706 (interquartile range, 0.664–0.736). GLM showed that the GGGI score was significantly associated with the incidence of both chronic hepatitis B and C in under 5 years (both p<0.001). For HIV, GGGI score was significantly associated with the pregnant women with unknown HIV status (p=0.001), no early infant diagnosis (p=0.027), and final transmission rate (p=0.005). There was no significant predictor for pregnant women who have not received antiretroviral therapy for prevention of mother-to-child transmission. All syphilis indicators have improved in high-income countries compared to low-income countries. GGGI score had a significant association only with no syphilis screening (p<0.001).

Conclusions: A lower GGGI score was associated with higher vertical transmission of hepatitis and HIV. The improvement of gender equity might prevent vertical transmission of these viruses. Further intervention studies are warranted to verify the results.

Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study

Background

Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter.

Objective

The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach.

Methods

A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for “methylphenidate” and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F₁ scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects.

Results

Of the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F₁ scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%).

Conclusions

The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.

Analysis of Issues and Future Trends Impacting Drug Safety in South Korea

New drug safety issues are emerging that are beyond the existing medication safety management system. To pre-empt these problems, forecasting future drug safety trends and issues is a necessity. The objective of this study was to identify issues and future trends impacting drug safety using foresight methodologies. The study started by identifying global megatrends, trends in safety management of medicines, and key issues in drug safety. A total of 25 global megatrends were selected by extracting and clustering keywords from 26 reports concerning the future. Using the text-mining method, 10 trends in drug safety were identified from 3593 news articles. This study derived 60 issues which can arise from the trends, and finally, the 20 key issues with the highest urgency and impact scores were selected. Some examples of issues with high scores were as follows: illegal distribution of medicines, lack of technology for managing and utilizing big data, change in the pharmaceutical trade environment, lack of education and safety management for specific populations, lack of artificial intelligence-based technology for the safety management of medicines, and the prevalence of drug advertisements through social network services. The key issues could be used to establish plans for medication safety management.

Spin-liquid-like state in pure and Mn-doped TbInO3 with a nearly triangular lattice

Inelastic neutron scattering studies in single crystals of TbInO3 and TbIn0.95Mn0.05O3 with nearly triangular antiferromagnetic lattice are reported. At low energies, a broad and apparently gapless continuum of magnetic excitations, located at the triangular lattice (TL) Brillouin zone boundary, is observed. The data are well described by the uncorrelated nearest-neighbor valence bonds model. At higher energies, a broad excitation branch dispersing from the TL zone boundary is observed. No signs of static magnetic order are found down to the temperatures two orders of magnitude smaller than the effective interaction energy. The fluctuating magnetic moment exceeds two-thirds of the Tb3+ free-ion value and is confined to the TL plane. These observations are consistent with a TL-based spin liquid state in TbInO3.