Improving the hepatitis C virus care cascade with the in-hospital Reflex tEsting ALarm-C (REAL-C) model.

BACKGROUND: The World Health Organization (WHO) has set targets to eliminate viral hepatitis, including hepatitis C virus (HCV) infection, by 2030. We present the results of the in-hospital Reflex tEsting ALarm-C (REAL-C) model, which incorporates reflex HCV RNA testing and sending alerts to physicians. METHODS: We conducted a retrospective study analysing the data of 1730 patients who newly tested positive for anti-HCV between March 2020 and June 2023. Three distinct periods were defined: pre-REAL-C (n = 696), incomplete REAL-C (n = 515) and complete REAL-C model periods (n = 519). The primary outcome measure was the HCV RNA testing rate throughout the study period. Additionally, we assessed the referral rate to the gastroenterology department, linkage time for diagnosis and treatment and the treatment rate. RESULTS: The rate of HCV RNA testing increased significantly from 51.0% (pre-REAL-C) to 95.6% (complete REAL-C). This improvement was consistent across clinical departments, regardless of patients' comorbidities. Among patients with confirmed HCV infection, the gastroenterology referral rate increased from 57.1% to 81.1% after the REAL-C model. The treatment rate among treatment-eligible patients was 92.4% during the study period. The mean interval from anti-HCV positivity to HCV RNA testing decreased from 45.1 to 1.9 days. The mean interval from the detection of anti-HCV positivity to direct-acting antiviral treatment also decreased from 89.5 to 49.5 days with the REAL-C model. CONCLUSION: The REAL-C model, featuring reflex testing and physician alerts, effectively increased HCV RNA testing rates and streamlined care cascades. Our model facilitated progress towards achieving WHO's elimination goals for HCV infection.

Clinical decision support systems to optimize adherence to anticoagulant guidelines in patients with atrial fibrillation: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Clinical decision support systems (CDSS) have been utilized as a low-cost intervention to improve healthcare process measures. Thus, we aim to estimate CDSS efficacy to optimize adherence to oral anticoagulant guidelines in eligible patients with atrial fibrillation (AF). METHODS: A systematic review and meta-analysis of randomized controlled trials (RCTs) retrieved from PubMed, WOS, SCOPUS, EMBASE, and CENTRAL through August 2023. We used RevMan V. 5.4 to pool dichotomous data using risk ratio (RR) with a 95% confidence interval (CI). PROSPERO ID: CRD42023471806. RESULTS: We included nine RCTs with a total of 25,573 patients. There was no significant difference, with the use of CDSS compared to routine care, in the number of patients prescribed anticoagulants (RR: 1.06, 95% CI [0.98, 1.14], P = 0.16), the number of patients prescribed antiplatelets (RR: 1.01 with 95% CI [0.97, 1.06], P = 0.59), all-cause mortality (RR: 1.19, 95% CI [0.31, 4.50], P = 0.80), major bleeding (RR: 0.84, 95% CI [0.21, 3.45], P = 0.81), and clinically relevant non-major bleeding (RR: 1.05, 95% CI [0.52, 2.16], P = 0.88). However, CDSS was significantly associated with reduced incidence of myocardial infarction (RR: 0.18, 95% CI [0.06, 0.54], P = 0.002) and cerebral or systemic embolic event (RR: 0.11, 95% CI [0.01, 0.83], P = 0.03). CONCLUSION: We report no significant difference with the use of CDSS compared to routine care in anticoagulant or antiplatelet prescription in eligible patients with AF. CDSS was associated with a reduced incidence of myocardial infarction and cerebral or systemic embolic events.

Utilizing electronic medical records alert to improve documentation of neonatal acute kidney injury.

BACKGROUND: Neonatal acute kidney injury (AKI) is a common yet underdiagnosed condition in neonates with significant implications for long-term kidney health. Lack of timely recognition and documentation of AKI contributes to missed opportunities for nephrology consultation and follow-up, potentially leading to adverse outcomes. METHODS: We conducted a quality improvement (QI) project to address this by incorporating an automated real-time electronic medical record (EMR)-AKI alert system in the Neonatal Intensive Care Unit (NICU) at Le Bonheur Children's Hospital. Our primary objective was to improve documentation of neonatal AKI (defined as serum creatinine (SCr) > 1.5 mg/dL) by 25% compared to baseline levels. The secondary goal was to increase nephrology consultations and referrals to the neonatal nephrology clinic. We designed an EMR-AKI alert system to trigger for neonates with SCr > 1.5 mg/dL, automatically adding AKI diagnosis to the problem list. This prompted physicians to consult nephrology, refer neonates to the nephrology clinic, and consider medication adjustments. RESULTS: Our results demonstrated a significant improvement in AKI documentation after implementing the EMR-AKI alert, reaching 100% compared with 7% at baseline (p < 0.001) for neonates with SCr > 1.5 mg/dL. Although the increase in nephrology consultations was not statistically significant (p = 0.5), there was a significant increase in referrals to neonatal nephrology clinics (p = 0.005). CONCLUSIONS: Integration of an EMR alert system with automated documentation offers an efficient and economical solution for improving neonatal AKI diagnosis and documentation. This approach enhances healthcare provider engagement, streamlines workflows, and supports QI. Widespread adoption of similar approaches can lead to improved patient outcomes and documentation accuracy in neonatal AKI care.

Evaluation of a scoring system for vein suitability in platelet apheresis donors.

INTRODUCTION: Donor vein assessment for the selection of good quality veins is crucial for a successful apheresis procedure. This study intends to find out the effectiveness of a vein assessment scoring tool (VST) used and found to be effective in selecting whole blood donors to reduce the difficulty in identifying good quality veins for the plateletpheresis procedure. MATERIALS AND METHODS: This was a prospective observational study on platelet apheresis donors with the application of a VST consisting of three vein descriptor parameters (vein visibility, vein palpability, and vein size) with 5 Likert-type responses constituting a score of 0-12 for each arm. Two vein assessors independently evaluated the vein in both arms and marked their responses blinded from each other as well from the principal investigator. The scores were then calculated and analyzed at the end of the study for their association with phlebotomy and procedural outcomes. RESULTS: A total of 190 donors were recruited. The mean scores for the arms with successful and failed phlebotomy were 9.1 and 9.4 (SD 2.3), respectively. The intra-class correlation Alpha Cronbach value was 0.834 and 0.837 for total scoring in the left arm and right arm, respectively, between the two assessors. Scores neither showed a correlation with other outcomes like low flow alarms, hematoma formation, number of phlebotomy attempts, and procedure completion. CONCLUSION: The study showed that the vein score tool did not truly predict the phlebotomy outcome in apheresis donors, though there was a good degree of inter-assessor reliability.

A comprehensive prediction system for silkworm acute toxicity assessment of environmental and in-silico pesticides.

The excessive application and loss of pesticides poses a great risk to the ecosystem, and the environmental safety assessment of pesticides is time-consuming and expensive using traditional animal toxicity tests. In this work, a pesticide acute toxicity dataset was created for silkworm integrating extensive experiments and various common pesticide formulations considering the sensitivity of silkworm to adverse environment, its economic value in China, and a gap in machine learning (ML) research on the toxicity prediction of this species, which addressed the previous limitation of only being able to predict toxicity classification without specific toxicity values. A new comprehensive voting model (CVR) was developed based on ML, combined with three regression algorithms, namely, Bayesian Ridge (BR), K Neighbors Regressor (KNN), Random Forest Regressor (RF) to accurately calculate lethal concentration 50 % (LC50). Three conformal models were successfully constructed, marking the first combination of conformal models with confidence intervals to predict silkworm toxicity. Further, the mechanism by analyzing structural alerts was summarized, and identified 25 warning structures, 24 positive compounds and 14 negative compounds. Importantly, a novel comprehensive prediction system was constructed that can provide LC50 and confidence intervals, structural alerts analysis, lipid-water partition coefficient (LogP) and similarity analysis, which can comprehensively evaluate the ecological toxicity risk of substances to make up for the incomplete toxicity data of new pesticides. The validity and generalization of the CVR model were verified by an external validation set. In addition, five new, low-toxic and green pesticide alternatives were designed through 50,000 cycles. Moreover, our software and ST Profiler can provide low-cost information access to accelerate environmental risk assessment, which can predict not only a single chemical, but also batches of chemicals, simply by inputting the SMILES / CAS / (Chinese / English) name of chemicals.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

What Are We Eating? Surveying the Presence of Toxic Molecules in the Food Supply Chain Using Chromatographic Approaches.

Consumers in developed and Western European countries are becoming more aware of the impact of food on their health, and they demand clear, transparent, and reliable information from the food industry about the products they consume. They recognise that food safety risks are often due to the unexpected presence of contaminants throughout the food supply chain. Among these, mycotoxins produced by food-infecting fungi, endogenous toxins from certain plants and organisms, pesticides, and other drugs used excessively during farming and food production, which lead to their contamination and accumulation in foodstuffs, are the main causes of concern. In this context, the goals of this review are to provide a comprehensive overview of the presence of toxic molecules reported in foodstuffs since 2020 through the Rapid Alert System for Food and Feed (RASFF) portal and use chromatography to address this challenge. Overall, natural toxins, environmental pollutants, and food-processing contaminants are the most frequently reported toxic molecules, and liquid chromatography and gas chromatography are the most reliable approaches for their control. However, faster, simpler, and more powerful analytical procedures are necessary to cope with the growing pressures on the food chain supply.

Improving a regional project on diagnostic reference levels for interventional procedures (OPRIPALC) with the support of a dose management system for the protection of patients and staff.

Interventional radiology is a clinical practice with important benefits for patients, but which involves high radiation doses. The optimisation of radiation protection (RP) for paediatric interventional cardiology is a priority for both patients and staff. The use of diagnostic reference levels (DRLs) has been proposed by the International Commission on Radiological Protection to improve RP in imaging procedures. Dose management systems (DMSs) allow the automatic collection of dosimetric, geometric and technical data to assist the optimisation process, with a continuous audit of the procedures, generating alerts to implement corrective actions when necessary. Patient dose indicators may be analysed individually and for different radiation events (fluoroscopy and cine runs). Occupational doses per procedure may be analysed (if electronic dosimeters are available) and linked with patient doses for an integrated approach to RP. Regional optimisation programmes require data collection and processing from several countries to set and periodically update the DRLs. Patient data is anonymised, and each participating hospital has access to their data in a central computer server. Using DMSs may be one of the best ways to support these programs in the collection and analysis of data, raising alerts about high patient and occupational doses and suggesting optimisation actions.

G6 continuous glucose monitoring system feature use and its associations with glycaemia in Europe.

AIMS: Current continuous glucose monitoring (CGM) devices provide features that alert individuals with diabetes about their current and impending adverse glycaemic events. The use of these features has been associated with glycaemic improvements. However, how these features are utilised under real-world conditions has not been well studied. We queried a large database to quantify utilisation of the Dexcom G6 system features and how utilisation impacted glycaemic outcomes within a cohort of European users. METHODS: This 6-month retrospective, observational, large database analysis utilised anonymised data from a sample of 47,784 Europe-based G6 users. Primary outcome measures were associations between utilisation and customisation of High/Low threshold alerts, 'urgent low soon' (ULS) alert, and established CGM metrics. RESULTS: Users in the Germany, Austria, Switzerland region (n = 20,257), the Nordic countries (n = 10,314), United Kingdom (n = 9006), Italy (n = 4747), France (n = 2130) and Spain (1330) were included. All alert features were utilised by >75% of the cohort across all regions/countries and age groups. Enabling the Low alert and ULS alert was associated with lower percentage of time below range compared to disabling the Low alert (p < 0.001). Enabling the High alert was associated with higher percentage of time in range (%TIR) and lower percentage of time above range (%TAR) %TAR compared to disabling the High alert (p < 0.001). Paediatric patients and older adults tended to set a higher threshold for High/Low alerts, while younger adults tended to use lower threshold values for High/Low alerts. CONCLUSIONS: Individuals who utilised the Dexcom G6 features showed better glycaemic control, particularly among those who utilised more sensitive High alert and Low alert settings, than users who did not utilise the system features.

Longitudinal analysis of users transitioning from the Dexcom G5 to the G6 RT-CGM system in Germany, Sweden and the United Kingdom (2018-2020).

AIMS: Regional variations in the adoption of diabetes technology may be reflected in population-level metrics of glycaemic control. In this observational study, we aimed to assess the glycaemic impacts of transitioning from the Dexcom G5 Real-Time Continuous Glucose Monitoring (RT-CGM) System to the Dexcom G6 in three European countries. METHODS: Anonymised RT-CGM data (uploaded to the Dexcom Clarity app) were from users in Germany, Sweden, and the United Kingdom (UK) who transitioned from G5 to G6 between 9-12 months after G6 launched in 2018. Primary endpoints were percent time in hypoglycaemia, percent time in range (TIR), user retention rates, device utilisation, and urgent low soon (ULS) alert utilisation. Metrics were computed for 3-month intervals in the 2-year study window. RESULTS: In all three countries, the transition from G5 to G6 was associated with a clear decrease in hypoglycaemia. In months 0-3 after transitioning, the median percent time 〈3 mmol/L (54 mg/dL) and 〈3.9 mmol/L (70 mg/dL) decreased by [0.12-0.28] and [0.40-0.43] percentage points, respectively, with another [0.11-0.21] and [0.34-0.65] percentage point decrease in months 3-6 in the three countries analysed. TIR and CGM utilisation were sustained or improved slightly across all countries. At the end of the study window, the retention rate was [88.8-94.8%] and ULS utilization was [83.9-86.9%] in the three countries analysed. CONCLUSIONS: Similar RT-CGM trends were observed across Germany, Sweden, and the UK. Improvements in hypoglycaemia occurred in all countries. The high retention of users may lead to sustained glycaemic benefits associated with RT-CGM use.

Clinic-based interventions to increase preexposure prophylaxis awareness and uptake among United States patients attending an obstetrics and gynecology clinic in Baltimore, Maryland.

BACKGROUND: Cisgender women account for 1 of every 5 new US HIV diagnoses, with most cases (85%) attributed to heterosexual contact. HIV preexposure prophylaxis is an effective prevention strategy; however, preexposure prophylaxis awareness and prescriptions among women are low. OBJECTIVE: This study aimed to increase preexposure prophylaxis counseling and uptake among cisgender women attending obstetrics and gynecology clinics. STUDY DESIGN: The study included 3 obstetrics and gynecology clinics within a single health system in a high HIV prevalence region. There were 3 phases: baseline (the 3-month period before the clinical trial that included provider education and training of a registered nurse about preexposure prophylaxis), clinical trial (the 3-month period during which eligible patients were randomized to an active control or preexposure prophylaxis registered nurse intervention), and maintenance (the 3-month period after the trial ended). Electronic medical record clinical decision support tools were available to both arms during the clinical trial, which included best practice alerts, order sets, progress note templates, and written and video preexposure prophylaxis educational materials for patients. In the intervention arm, a preexposure prophylaxis nurse contacted and counseled patients and was equipped to prescribe preexposure prophylaxis. Moreover, this study evaluated the phases through the "reach, effectiveness, adoption, implementation, and maintenance" framework. The primary outcome of the study was effectiveness (eg, percentage of eligible patients with documented HIV prevention counseling in the electronic medical record or preexposure prophylaxis prescriptions). The secondary outcomes included reach (eg, percentage of best practice alerts that providers acted on or the percentage of eligible patients who spoke with the preexposure prophylaxis registered nurse), adoption (eg, percentage of eligible patients with a best practice alert that triggered or the percentage of eligible patients the preexposure prophylaxis registered nurse attempted to contact), and maintenance (eg, percentage of patients with documented HIV prevention counseling or preexposure prophylaxis prescriptions during the maintenance phase). RESULTS: There were 904 unique patients in all phases with a mean age of 28.8±7.7 years, and 416 patients (46%) were pregnant; moreover, 436 patients were randomized in the clinical trial phase. Concerning reach and adoption, best practice alerts were triggered for 100% of eligible encounters; however, the providers acted on 52% of them. The preexposure prophylaxis nurse attempted to contact every patient and successfully spoke with 81.2% of them in the preexposure prophylaxis registered nurse arm. Concerning effectiveness, there were significantly more patients counseled about preexposure prophylaxis in the preexposure prophylaxis registered nurse group than in the active control group (66.5% vs 12.3%, respectively; P<.001), although preexposure prophylaxis prescriptions were equivalent (P=1.0). Among the subgroup of patients who were counseled about preexposure prophylaxis, 18.5% of patients in the active control arm and 3.4% in the preexposure prophylaxis registered nurse arm were prescribed preexposure prophylaxis (P=.02). Concerning maintenance, clinical decision support tools alone resulted in preexposure prophylaxis counseling of 1.0% of patients during the maintenance phase vs 0.6% of patients during the baseline phase and 11.2% of patients during the clinical trial phase (P<.001). Preexposure prophylaxis prescriptions were not statistically different among the 3 phases (P=.096). CONCLUSION: A preexposure prophylaxis nurse effectively increased HIV prevention discussions but did not lead to more preexposure prophylaxis prescriptions than the preexposure prophylaxis-focused clinical decision support tools used by providers. The decrease in preexposure prophylaxis counseling after the trial phase suggests that persistent interventions are needed to maintain effects.

2023 HRS/EHRA/APHRS/LAHRS Expert Consensus Statement on Practical Management of the Remote Device Clinic.

Remote monitoring is beneficial for the management of patients with cardiovascular implantable electronic devices by impacting morbidity and mortality. With increasing numbers of patients using remote monitoring, keeping up with higher volume of remote monitoring transmissions creates challenges for device clinic staff. This international multidisciplinary document is intended to guide cardiac electrophysiologists, allied professionals, and hospital administrators in managing remote monitoring clinics. This includes guidance for remote monitoring clinic staffing, appropriate clinic workflows, patient education, and alert management. This expert consensus statement also addresses other topics such as communication of transmission results, use of third-party resources, manufacturer responsibilities, and programming concerns. The goal is to provide evidence-based recommendations impacting all aspects of remote monitoring services. Gaps in current knowledge and guidance for future research directions are also identified.

Remote monitoring of implantable cardiac monitors in patients with unexplained syncope: Predictors of false-positive alert episodes.

BACKGROUND: Remote monitoring is recommended for patients with implantable cardiac monitors (ICMs), but compared to other cardiac implantable devices, ICMs are less accurate and transmit a higher number of alerts. OBJECTIVE: The aim of this study was to investigate the predictors of false-positive (FP) arrhythmic alerts in patients with unexplained syncope who were implanted with ICM and followed by an automatic remote monitoring system. METHODS: We retrospectively evaluated all consecutive patients who received a long-sensing vector ICM for unexplained syncope between January 2019 to September 2021 at our Syncope Unit. The primary endpoint was the incidence of the first FP episode. The secondary endpoints included assessing the incidence of FP episodes for all types of algorhythms and indentifying the reasons for the misdetection of these episodes. RESULTS: Among 105 patients (44.8% males, median age 51 years), 51 (48.6%) transmitted at least one FP alert during a median follow-up of 301 days. The presence of pre-ventricular complexes (PVCs) on the resting electrocardiogram was the only clinical characteristic associated with an increased risk of FP alerts (adjusted Hazard ratio [HR] 5.76 [2.66-12.4], p = 0.010). The other significant device-related variables were a low-frequency filter at 0.05 Hz versus the default 0.5 Hz (adjusted HR 3.82 [1.38-10.5], p = 0.010) and the R-wave amplitude (adjusted HR 0.35 [0.13-0.99], p = 0.049). CONCLUSION: Patients who have PVCs are at higher risk of inappropriate ICM activations. To reduce the occurrence of FP alerts, it may be beneficial to target a large R-wave amplitude during device insertion and avoid programming a low-frequency filter at 0.05 Hz.

Rebuilding relationships on coral reefs: Coral bleaching knowledge-sharing to aid adaptation planning for reef users: Bleaching emergence on reefs demonstrates the need to consider reef scale and accessibility when preparing for, and responding to, coral bleaching.

Coral bleaching has impacted reefs worldwide and the predictions of near-annual bleaching from over two decades ago have now been realized. While technology currently provides the means to predict large-scale bleaching, predicting reef-scale and within-reef patterns in real-time for all reef users is limited. In 2020, heat stress across the Great Barrier Reef underpinned the region's third bleaching event in 5 years. Here we review the heterogeneous emergence of bleaching across Heron Island reef habitats and discuss the oceanographic drivers that underpinned variable bleaching emergence. We do so as a case study to highlight how reef end-user groups who engage with coral reefs in different ways require targeted guidance for how, and when, to alter their use of coral reefs in response to bleaching events. Our case study of coral bleaching emergence demonstrates how within-reef scale nowcasting of coral bleaching could aid the development of accessible and equitable bleaching response strategies on coral reefs. Also see the video abstract here: https://youtu.be/N9Tgb8N-vN0.

Metabolism-based category formation for the prioritisation of genotoxicity hazard assessment for plant protection product residues (part 3): Strobilurins.

In dietary risk assessment of plant protection products, residues of active ingredients and their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity, in terms of metabolism, a metabolic similarity profiling scheme has been developed from an analysis of 46 chemicals of strobilurin fungicides and their metabolites for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This profiling scheme consists of a set of ten sub-structures, each linked to a key metabolic transformation present in the strobilurin metabolic space. This metabolic similarity profiling scheme was combined with covalent chemistry profiling and physico-chemistry properties to develop chemical categories suitable for chemical prioritisation via read-across. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for metabolism data and individual groups of plant protection products as the basis for the development of such profiling schemes.

Mutagenic impurities in pharmaceuticals: A critical assessment of the cohort of concern with a focus on N-nitrosamines.

The TTC (Threshold of Toxicological Concern; set at 1.5 µg/day for pharmaceuticals) defines an acceptable patient intake for any unstudied chemical posing a negligible risk of carcinogenicity or other toxic effects. A group of high potency mutagenic carcinogens, defined solely by the presence of particular structural alerts, are referred to as the "cohort of concern" (CoC); aflatoxin-like-, N-nitroso-, and alkyl-azoxy compounds are considered to pose a significant carcinogenic risk at intakes below the TTC. Kroes et al. (2004) derived values for the TTC and CoC in the context of food components, employing a non-transparent dataset never placed in the public domain. Using a reconstructed all-carcinogen dataset from relevant publications, it is now clear that there are exceptions for all three CoC structural classes. N-Nitrosamines represent 62% of the N-nitroso class in the reconstructed dataset. Employing a contemporary dataset, 20% are negative in rodent carcinogenicity bioassays with less than 50% of all N-nitrosamines estimated to fall into the highest risk category. It is recommended that CoC nitrosamines are identified by compound-specific data rather than structural alerts. Thus, it should be possible to distinguish CoC from non-CoC N-nitrosamines in the context of mutagenic impurities described in ICH M7 (R1).

In silico prediction of hERG blockers using machine learning and deep learning approaches.

The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking property of compounds in early drug discovery. In this study, a dataset containing 4556 compounds with IC50 values determined by patch clamp techniques on mammalian lineage cells was collected, and hERG blockers and non-blockers were distinguished according to three single thresholds and two binary thresholds. Four machine learning (ML) algorithms combining four molecular fingerprints and molecular descriptors as well as graph convolutional neural networks (GCNs) were used to construct a series of binary classification models. The results showed that the best models varied for different thresholds. The ML models implemented by support vector machine and random forest performed well based on Morgan fingerprints and molecular descriptors, with AUCs ranging from 0.884 to 0.950. GCN showed superior prediction performance with AUCs above 0.952, which might be related to its direct extraction of molecular features from the original input. Meanwhile, the classification of binary threshold was better than that of single threshold, which could provide us with a more accurate prediction of hERG blockers. At last, the applicability domain for the model was defined, and seven structural alerts that might generate hERG blockage were identified by information gain and substructure frequency analysis. Our work would be beneficial for identifying hERG blockers in chemicals.

Quality of medicines in Sri Lanka: a retrospective review of safety alerts.

BACKGROUND: Many medicine quality problems are detected after they arrive at health facilities. Thus, critically defective medicines that may pose health risks to patients need to be withheld or recalled. AIMS: To investigate the withheld and recalled medicines in relation to the types of defects, their total numbers, therapeutic categories, pharmaceutical dosage forms, and country of manufacturer during the study period. METHODS: A retrospective review was performed on withheld and recalled medicines published on the publicly available National Medicines Regulatory Authority (NMRA) official website in Sri Lanka between June 2018 and August 2021. Details on substandard medicines (SM) were extracted and documented. Each record of SM was individually reviewed to determine the type of defect, subsequent action taken by NMRA, therapeutic category, pharmaceutical dosage form, and country of manufacturer. RESULTS: A total of 163 defects were identified in 143 defective medicines, among which the most common types of defects were contamination (n = 59, 36.2%), stability defects (n = 41, 25.2%), packaging and labelling defects (n = 27, 16.6%) and active pharmaceutical ingredient defects (n = 26, 15.9%). Out of 143 total defective medicines identified, anti-infectives accounted for 41.9%, while parenteral preparations (44.0%) were found to be frequently defective. Nearly 70% of the recalled and withheld medicines were of Indian origin, and some manufacturers were identified to be repeatedly involved. CONCLUSIONS: This study revealed that contamination was the most frequent cause of defective medicines, while parenteral preparations and anti-infectives were the most susceptible pharmaceutical dosage form and therapeutic category found to be substandard, respectively. In addition, the findings show that some manufacturers were accountable for repetitive withholdings and recalls, which reflects the ignorance of quality control measures and weak regulatory inspections as a violation of Good Manufacturing Practice (GMP).

Provider Interaction With an Electronic Health Record Notification to Identify Eligible Patients for a Cluster Randomized Trial of Advance Care Planning in Primary Care: Secondary Analysis.

BACKGROUND: Advance care planning (ACP) improves patient-provider communication and aligns care to patient values, preferences, and goals. Within a multisite Meta-network Learning and Research Center ACP study, one health system deployed an electronic health record (EHR) notification and algorithm to alert providers about patients potentially appropriate for ACP and the clinical study. OBJECTIVE: The aim of the study is to describe the implementation and usage of an EHR notification for referring patients to an ACP study, evaluate the association of notifications with study referrals and engagement in ACP, and assess provider interactions with and perspectives on the notifications. METHODS: A secondary analysis assessed provider usage and their response to the notification (eg, acknowledge, dismiss, or engage patient in ACP conversation and refer patient to the clinical study). We evaluated all patients identified by the EHR algorithm during the Meta-network Learning and Research Center ACP study. Descriptive statistics compared patients referred to the study to those who were not referred to the study. Health care utilization, hospice referrals, and mortality as well as documentation and billing for ACP and related legal documents are reported. We evaluated associations between notifications with provider actions (ie, referral to study, ACP not documentation, and ACP billing). Provider free-text comments in the notifications were summarized qualitatively. Providers were surveyed on their satisfaction with the notification. RESULTS: Among the 2877 patients identified by the EHR algorithm over 20 months, 17,047 unique notifications were presented to 45 providers in 6 clinics, who then referred 290 (10%) patients. Providers had a median of 269 (IQR 65-552) total notifications, and patients had a median of 4 (IQR 2-8). Patients with more (over 5) notifications were less likely to be referred to the study than those with fewer notifications (57/1092, 5.2% vs 233/1785, 13.1%; P<.001). The most common free-text comment on the notification was lack of time. Providers who referred patients to the study were more likely to document ACP and submit ACP billing codes (P<.001). In the survey, 11 providers would recommend the notification (n=7, 64%); however, the notification impacted clinical workflow (n=9, 82%) and was difficult to navigate (n=6, 55%). CONCLUSIONS: An EHR notification can be implemented to remind providers to both perform ACP conversations and refer patients to a clinical study. There were diminishing returns after the fifth EHR notification where additional notifications did not lead to more trial referrals, ACP documentation, or ACP billing. Creation and optimization of EHR notifications for study referrals and ACP should consider the provider user, their workflow, and alert fatigue to improve implementation and adoption. TRIAL REGISTRATION: ClinicalTrials.gov NCT03577002; https://clinicaltrials.gov/ct2/show/NCT03577002.

Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.