Search

Delivery of hydrophilic small molecule therapeutics by traditional drug delivery systems is challenging. Herein, we have used the specific interaction between DNA aptamers and drugs to create simple and effective drug depot systems. The specific binding of a phosphorothioate-modified aptamer to drugs formed non-covalent aptamer/drug complexes, which created a sustained release system. We demonstrated the effectiveness of this system with small hydrophilic molecules, the site 1 sodium channel blockers tetrodotoxin and saxitoxin. The aptamer-based delivery system greatly prolonged the duration of local anesthesia and reduced systemic toxicity. The beneficial effects of the aptamers were restricted to the compounds they were specific to. These studies establish aptamers as a class of highly specific, modifiable drug delivery systems, and demonstrate potential usefulness in the management of postoperative pain.

To describe the imaging findings of intimate partner violence (IPV)–related injury and to evaluate the role of longitudinal imaging review in detecting IPV. Radiology studies were reviewed in chronological order and IPV-related injuries were recorded among 400 victims of any type of abuse (group 1) and 288 of physical abuse (group 2) from January 2013 to June 2018. The likelihood of IPV was assessed as low/moderate/high based on the review of (1) current and prior anatomically related studies only and (2) longitudinal imaging history consisting of all prior studies. The first radiological study date with moderate/high suspicion was compared to the self-reported date by the victim. A total of 135 victims (33.8%) in group 1 and 144 victims (50%) in group 2 demonstrated IPV-related injuries. Musculoskeletal injury was most common (58.2% and 44.5% in groups 1 and 2, respectively; most commonly lower/upper extremity fractures), followed by neurologic injury (20.9% and 32.9% in groups 1 and 2, respectively; most commonly facial injury). With longitudinal imaging history, radiologists were able to identify IPV in 31% of group 1 and 46.5% of group 2 patients. Amongst these patients, earlier identification by radiologists was provided compared to the self-reported date in 62.3% of group 1 (median, 64 months) and in 52.6% of group 2 (median, 69.3 months). Musculoskeletal and neurological injuries were the most common IPV-related injuries. Knowledge of common injuries and longitudinal imaging history may help IPV identification when victims are not forthcoming. • Musculoskeletal injuries were the most common type of IPV-related injury, followed by neurological injuries. • With longitudinal imaging history, radiologists were able to better raise the suspicion of IPV compared to the selective review of anatomically related studies only. • With longitudinal imaging history, radiologists were able to identify IPV earlier than the self-reported date by a median of 64 months in any type of abuse, and a median of 69.3 months in physical abuse.

Manipulating topological arrangement is a powerful tool for tuning energy migration in natural photosynthetic proteins and artificial polymers. Here, we report an inorganic optical nanosystem composed of NaErF4 and NaYbF4, in which topological arrangement enhanced upconversion luminescence. Three architectures are designed for considerations pertaining to energy migration and energy transfer within nanoparticles: outside-in, inside-out, and local energy transfer. The outside-in architecture produces the maximum upconversion luminescence, around 6-times brighter than that of the inside-out at the single-particle level. Monte Carlo simulation suggests a topology-dependent energy migration favoring the upconversion luminescence of outside-in structure. The optimized outside-in structure shows more than an order of magnitude enhancement of upconversion brightness compared to the conventional core-shell structure at the single-particle level and is used for long-term single-particle tracking in living cells. Our findings enable rational nanoprobe engineering for single-molecule imaging and also reveal counter-intuitive relationships between upconversion nanoparticle structure and optical properties.

Site-1 sodium channel blockers (S1SCBs) act as potent local anaesthetics, but they can cause severe systemic toxicity. Delivery systems can be used to reduce the toxicity, but the hydrophilicity of S1SCBs makes their encapsulation challenging. Here, we report a self-assembling delivery system for S1SCBs whose design is inspired by the specific interactions of S1SCBs with two peptide sequences on the sodium channel. Specifically, the peptides were modified with hydrophobic domains so that they could assemble into nanofibres that facilitated specific binding with the S1SCBs tetrodotoxin, saxitoxin and dicarbamoyl saxitoxin. Injection of S1SCB-carrying nanofibres at the sciatic nerves of rats led to prolonged nerve blockade and to reduced systemic toxicity, with benign local-tissue reaction. The strategy of mimicking a molecular binding site via supramolecular interactions may be applicable more broadly to the design of drug delivery systems for receptor-mediated drugs.

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

To evaluate the effectiveness of the BNT162b2 messenger RNA vaccine in pregnant women, we conducted an observational cohort study of pregnant women aged 16 years or older, with no history of SARS-CoV-2, who were vaccinated between 20 December 2020 and 3 June 2021. A total of 10,861 vaccinated pregnant women were matched to 10,861 unvaccinated pregnant controls using demographic and clinical characteristics. Study outcomes included documented infection with SARS-CoV-2, symptomatic COVID-19, COVID-19-related hospitalization, severe illness and death. Estimated vaccine effectiveness from 7 through to 56 d after the second dose was 96% (95% confidence interval 89-100%) for any documented infection, 97% (91-100%) for infections with documented symptoms and 89% (43-100%) for COVID-19-related hospitalization. Only one event of severe illness was observed in the unvaccinated group and no deaths were observed in either group. In summary, the BNT162b2 mRNA vaccine was estimated to have high vaccine effectiveness in pregnant women, which is similar to the effectiveness estimated in the general population.

The effect of local anesthetics, particularly those which are hydrophilic, such as tetrodotoxin, is impeded by tissue barriers that restrict access to individual nerve cells. Methods of enhancing penetration of tetrodotoxin into nerve include co-administration with chemical permeation enhancers, nanoencapsulation, and insonation with very low acoustic intensity ultrasound and microbubbles. In this study, we examined the effect of acoustic intensity on nerve block by tetrodotoxin and compared it to the effect on nerve block by bupivacaine, a more hydrophobic local anesthetic. Anesthetics were applied in peripheral nerve blockade in adult Sprague-Dawley rats. Insonation with 1-MHz ultrasound at acoustic intensity greater than 0.5 W/cm2 improved nerve block effectiveness, increased nerve block reliability, and prolonged both sensory and motor nerve blockade mediated by the hydrophilic ultra-potent local anesthetic, tetrodotoxin. These effects were not enhanced by microbubbles. There was minimal or no tissue injury from ultrasound treatment. Insonation did not enhance nerve block from bupivacaine. Using an in vivo model system of local anesthetic delivery, we studied the effect of acoustic intensity on insonation-mediated drug delivery of local anesthetics to the peripheral nerve. We found that insonation alone (at intensities greater than 0.5 W/cm2) enhanced nerve blockade mediated by the hydrophilic ultra-potent local anesthetic, tetrodotoxin. Graphical abstract.

Made available in DSpace on 2022-04-29T08:35:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Division of Intramural Research, National Institute of Allergy and Infectious Diseases Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases National Institute of Allergy and Infectious Diseases National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institute of Environmental Health Sciences U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Division of Cancer Prevention, National Cancer Institute Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Department of Biomedical Informatics Harvard Medical School Department of Neurology University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue Department of Pediatrics Wake Forest School of Medicine Department of Critical Care National University Health Systems Department of Preventive Medicine Northwestern University Department of Biomedical Informatics University of Pittsburgh Department of Surgery National University Health Systems Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Computational Medicine and Bioinformatics University of Michigan Department of Biomedical Informatics University of Kentucky Department of Legal Economic and Social Sciences University Magna Graecia of Catanzaro Health Catalyst INC. Department of Surgery ASST Pavia Lombardia Region Health System Clinical Research Unit of Botucatu Medical School São Paulo State University Pediatric Emergency Department Hôpital Necker-Enfants Malades Assistance Publique-Hôpitaux de Paris National Center for Infectious Diseases Tan Tock Seng Hospital Departments of Biomedical Informatics Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati Department of Pediatrics Medical University of South Carolina Department of Surgery St. Luke’s University Health Network Department of Medicine David Geffen School of Medicine at UCLA UOC Ricerca Innovazione e Brand Reputation ASST Papa Giovanni XXIII Department of Electrical Computer and Biomedical Engineering University of Pavia IT Department Innovation & Data APHP Greater Paris University Hospital I.T. Department ASST Pavia Health Informatics Hospital Universitario, 12 de Octubre Strategy and Transformation Department APHP Greater Paris University Hospital Faculty of Medicine and Medical Center University of Freiburg Digital Research Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children Scientific Direction IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano North Carolina Translational and Clinical Sciences (NC TraCS) Institute University of North Carolina BIOMERIS (BIOMedical Research Informatics Solutions) Department of Biomedical Informatics HEGP APHP Greater Paris University Hospital Department of Medical and Surgical Sciences Data Analytics Research Center University Magna Graecia of Catanzaro Department of Anesthesia St. Luke’s University Health Network Université Paris-Saclay Inria CEA INRIA Sophia-Antipolis–ZENITH Team LIRMM Computational Health Informatics Program Boston Children’s Hospital Department of Internal Medicine University of Kentucky Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Department of Internal Medicine and Therapeutics University of Pavia Informatics Institute University of Alabama at Birmingham IAM Unit Bordeaux University Hospital/ERIAS-Inserm U1219 BPH Biomedical Informatics Center Medical University of South Carolina Clinical Research Informatics Boston Children’s Hospital Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia SED/SIERRA Inria Centre de Paris Health Information Technology & Services University of Michigan Internal Medicine Department Botucatu Medical School São Paulo State University Heinrich-Lanz-Center for Digital Health University Medicine Mannheim Heidelberg University Department of Anesthesiology Critical Care and Pain Medicine Boston Children’s Hospital Department of Learning Health Sciences University of Michigan Medical School MSHI Medical University of South Carolina Department of Medicine Massachusetts General Hospital Division of Human Genetics Department of Pediatrics The Children’s Hospital of Philadelphia Center for Medical Information and Communication Technology University Hospital Renaissance Computing Institute/Department of Computer Science University of North Carolina Clinical Research Unit Saint Antoine Hospital APHP Greater Paris University Hospital Clevy.io Department of Biostatistics Harvard T.H. Chan School of Public Health VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System MICHR Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Harvard Catalyst Harvard Medical School Clinical Research Unit Paris Saclay APHP Greater Paris University Hospital Department of Anesthesiology and Critical Care Children’s Hospital of Philadelphia VA Informatics and Computing Infrastructure Tennessee Valley Healthcare System Veterans Affairs Medical Center École Normale Supérieure PSL Université Paris BIG-ARC The University of Texas Health Science Center at Houston School of Biomedical Informatics Pediatric Infectious Disease Department Hospital Universitario, 12 de Octubre Department of Infectious Diseases Great Ormond Street Hospital for Children Department of Neurology Massachusetts General Hospital Internal Medicine Department of Botucatu Medical School São Paulo State University Department of Pediatrics Boston Children’s Hospital Center for Biomedical Informatics Wake Forest School of Medicine Department of Medical Informatics University of Erlangen-Nürnberg Department of Veterans Affairs Office of Research and Development Biomedical Data Science Lab ITACA Institute Universitat Politècnica de València Nurse Department of FMB-Medicine School of Botucatu Management Engineering ASST Pavia Lombardia Region Health System Department of Anesthesiology University Hospital Erlangen FAU Erlangen-Nürnberg Critical Care Medicine Department of Medicine St. Luke’s University Health Network Department of Medicine National University Hospital Department of Information Management Chang Gung University Clinical & Translational Science Institute Medical College of Wisconsin Montréal Neurological Institute McGill University SequeL Inria Lille Respiratory Department ICS S Maugeri IRCCS Department of Health Management and Informatics University of Missouri Department of Oncology ASST Papa Giovanni XXIII Clinical Research Unit of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University Internal Medicine Department of Botucatu Medical School São Paulo State University Division of Intramural Research, National Institute of Allergy and Infectious Diseases: AI11679 Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases: AI11679 National Institute of Allergy and Infectious Diseases: AI11679 National Center for Advancing Translational Sciences: CTSA Award #UL1TR001878 National Center for Advancing Translational Sciences: CTSA Award #UL1TR002366 National Heart, Lung, and Blood Institute: K23HL148394 National Institute of Environmental Health Sciences: P30ES017885 U.S. National Library of Medicine: R01LM012095 National Institute of Neurological Disorders and Stroke: R01NS098023 Division of Cancer Prevention, National Cancer Institute: U24CA210967 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001857

Immune checkpoint inhibitors have demonstrated significant survival benefits in treating many types of cancers. However, their immune-related adverse events (irAEs) have not been systematically evaluated across cancer types in large-scale real-world populations. To address this gap, we conducted real-world data analyses using nationwide insurance claims data with 85.97 million enrollees across 8 years. We identified a significantly increased risk of developing irAEs among patients receiving immunotherapy agents in all seven cancer types commonly treated with immune checkpoint inhibitors. By six months after treatment initialization, those receiving immunotherapy were 1.50–4.00 times (95% CI, lower bound from 1.15 to 2.16, upper bound from 1.69 to 20.36) more likely to develop irAEs in the first 6 months of treatment, compared to matched chemotherapy or targeted therapy groups, with a total of 92,858 patients. The risk of developing irAEs among patients using nivolumab is higher compared to those using pembrolizumab. These results confirmed the need for clinicians to assess irAEs among cancer patients undergoing immunotherapy as part of management. Our methods are extensible to characterizing the effectiveness and adverse effects of novel treatments in large populations in an efficient and economical fashion.

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1–2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no). PheCAP takes structured data and narrative notes from electronic medical records and enables patients with a particular clinical phenotype to be identified.

Patients with pre-existing autoimmune diseases have been excluded from clinical trials of immune checkpoint inhibitors (ICIs) for cancer. Real-world evidence is necessary to understand ICI safety in this population. Patients treated with ICIs from 2011 to 2017 were identified using data from a large health insurer. Outcomes included time to (1) any hospitalization; (2) any hospitalization with an irAE diagnosis; and (3) outpatient corticosteroid treatment. The key exposure was pre-existing autoimmune disease, ascertained within 12 months before starting ICI treatment, and defined either by strict criteria (one inpatient or two outpatient claims at least 30 days apart) or relaxed criteria only (any claim, without meeting strict criteria). Of 4438 ICI-treated patients, pre-existing autoimmune disease was present among 179 (4%) by strict criteria, and another 283 (6%) by relaxed criteria only. In multivariable models, pre-existing autoimmune disease by strict criteria was not associated with all-cause hospitalization (HR 1.27, 95% CI 0.998–1.62), but it was associated with hospitalization with an irAE diagnosis (HR 1.81, 95% CI 1.21–2.71) and with corticosteroid treatment (HR 1.93, 95% CI 1.35–2.76). Similarly, pre-existing autoimmune disease by relaxed criteria only was not associated with all-cause hospitalization (HR 1.11, 95% CI 0.91–1.34), but was associated with hospitalization with an irAE diagnosis (HR 1.46, 95% CI 1.06–2.01) and corticosteroid treatment (HR 1.46, 95% CI 1.13–1.88). Pre-existing autoimmune disease was not associated with time to any hospitalization after initiating ICI therapy, but it was associated with a modest increase in hospitalizations with irAE diagnoses and with corticosteroid treatment.

Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

It is unknown whether there are sex differences in response to free or encapsulated local anesthetics. We examined nerve block duration and toxicity following peripheral nerve blockade in male and female rats. We studied the local anesthetic bupivacaine (free or encapsulated) as well as tetrodotoxin, which acts on a different site of the same voltage-gated channel. Sensory nerve blockade was 158.5 [139–190] minutes (median [interquartile range]) (males) compared to 173 [134–171] minutes (females) (p = 0.702) following bupivacaine injection, N = 8 male, 8 female. Motor nerve blockade was 157 [141–171] minutes (males) compared to 172 [146–320] minutes (females) (p = 0.2786). Micellar bupivacaine (N = 8 male, 8 female) resulted in sensory nerve blockade of 266 [227–320] minutes (males) compared to 285 [239–344] minutes (females) (p = 0.6427). Motor nerve blockade was 264 [251–264] minutes (males) compared to 287 [262–287] minutes (females) (p = 0.3823). Liposomal bupivacaine (N = 8 male, 8 female) resulted in sensory nerve blockade of 240 [207–277] minutes (males) compared to 289 [204–348] minutes (females) (p = 0.1654). Motor nerve blockade was 266 [237–372] minutes (males) compared to 317 [251–356] minutes (females) (p = 0.6671). Following tetrodotoxin injection (N = 12 male,12 female) sensory nerve blockade was 54.8 [5–117] minutes (males) compared to 54 [14–71] minutes (females) (p = 0.6422). Motor nerve blockade was 72 [40–112] minutes (males) compared to 64 [32–143] minutes (females) (p = 0.971). We found no statistically significant sex differences associated with the formulations tested. In both sexes, durations of nerve block were similar between micellar and liposomal bupivacaine formulations, despite the micellar formulation containing less drug.

Background Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher. Results We describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project. Conclusions Our framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package. Electronic supplementary material The online version of this article (10.1186/s12859-019-2855-9) contains supplementary material, which is available to authorized users.

Following publication of this article, the authors noticed an error in Supplementary Table 1. In the original Supplementary Table 1, one of the criteria for control participants was incorrectly given as ‘Report extensive recent symptoms of depression: less than 14 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’. This has now been corrected to: ‘Report extensive recent symptoms of depression: less than 5 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.