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General General

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

In Journal of computer-aided molecular design

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Roy Dipankar, Dutta Devjyoti, Wishart David S, Kovalenko Andriy


3D-RISM-KH, Classification, Machine learning, Molecular solvation theory, PAMPA

Radiology Radiology

A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis.

METHODS : We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson's disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices.

RESULTS : The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively.

CONCLUSION : The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

Iwabuchi Yu, Kameyama Masashi, Matsusaka Yohji, Narimatsu Hidetoshi, Hashimoto Masahiro, Seki Morinobu, Ito Daisuke, Tabuchi Hajime, Yamada Yoshitake, Jinzaki Masahiro


123I-FP-CIT, 123I-Ioflupane, Artificial intelligence, CART, Data mining

Surgery Surgery

Factors associated with rebound pain after peripheral nerve block for ambulatory surgery.

In British journal of anaesthesia ; h5-index 72.0

BACKGROUND : Rebound pain is a common, yet under-recognised acute increase in pain severity after a peripheral nerve block (PNB) has receded, typically manifesting within 24 h after the block was performed. This retrospective cohort study investigated the incidence and factors associated with rebound pain in patients who received a PNB for ambulatory surgery.

METHODS : Ambulatory surgery patients who received a preoperative PNB between March 2017 and February 2019 were included. Rebound pain was defined as the transition from well-controlled pain (numerical rating scale [NRS] ≤3) while the block is working to severe pain (NRS ≥7) within 24 h of block performance. Patient, surgical, and anaesthetic factors were analysed for association with rebound pain by univariate, multivariable, and machine learning methods.

RESULTS : Four hundred and eighty-two (49.6%) of 972 included patients experienced rebound pain as per the definition. Multivariable analysis showed that the factors independently associated with rebound pain were younger age (odds ratio [OR] 0.98; 95% confidence interval [CI] 0.97-0.99), female gender (OR 1.52 [1.15-2.02]), surgery involving bone (OR 1.82 [1.38-2.40]), and absence of perioperative i.v. dexamethasone (OR 1.78 [1.12-2.83]). Despite a high incidence of rebound pain, there were high rates of patient satisfaction (83.2%) and return to daily activities (96.5%).

CONCLUSIONS : Rebound pain occurred in half of the patients and showed independent associations with age, female gender, bone surgery, and absence of intraoperative use of i.v. dexamethasone. Until further research is available, clinicians should continue to use preventative strategies, especially for patients at higher risk of experiencing rebound pain.

Barry Garrett S, Bailey Jonathan G, Sardinha Joel, Brousseau Paul, Uppal Vishal


ambulatory surgical procedures, dexamethasone, pain management, peripheral nerve block, rebound pain, regional anaesthesia

General General

Deep Phenotyping of Headache in Hospitalized COVID-19 Patients via Principal Component Analysis.

In Frontiers in neurology

Objectives: Headache is a common symptom in systemic infections, and one of the symptoms of the novel coronavirus disease 2019 (COVID-19). The objective of this study was to characterize the phenotype of COVID-19 headache via machine learning. Methods: We performed a cross-sectional study nested in a retrospective cohort. Hospitalized patients with COVID-19 confirmed diagnosis who described headache were included in the study. Generalized Linear Models and Principal Component Analysis were employed to detect associations between intensity and self-reported disability caused by headache, quality and topography of headache, migraine features, COVID-19 symptoms, and results from laboratory tests. Results: One hundred and six patients were included in the study, with a mean age of 56.6 ± 11.2, including 68 (64.2%) females. Higher intensity and/or disability caused by headache were associated with female sex, fever, abnormal platelet count and leukocytosis, as well as migraine symptoms such as aggravation by physical activity, pulsating pain, and simultaneous photophobia and phonophobia. Pain in the frontal area (83.0% of the sample), pulsating quality, higher intensity of pain, and presence of nausea were related to lymphopenia. Pressing pain and lack of aggravation by routine physical activity were linked to low C-reactive protein and procalcitonin levels. Conclusion: Intensity and disability caused by headache attributed to COVID-19 are associated with the disease state and symptoms. Two distinct headache phenotypes were observed in relation with COVID-19 status. One phenotype seems to associate migraine symptoms with hematologic and inflammatory biomarkers of severe COVID-19; while another phenotype would link tension-type headache symptoms to milder COVID-19.

Planchuelo-Gómez Álvaro, Trigo Javier, de Luis-García Rodrigo, Guerrero Ángel L, Porta-Etessam Jesús, García-Azorín David


COVID-19, headache disorders, machine learning, migraine, tension-type headache

Pathology Pathology

An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.

In International journal of legal medicine

Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specific expertise. In this study, we developed an artificial intelligence (AI)-based system as a substitute for manual morphological examination capable of identifying and classifying diatoms at the species level. Within two days, the system collected information on diatom profiles in the Huangpu and Suzhou Rivers of Shanghai, China. In an animal experiment, the similarities of diatom profiles between lung tissues and water samples were evaluated through a modified Jensen-Shannon (JS) divergence measure for drowning site inference, reaching a prediction accuracy of 92.31%. Considering its high efficiency and simplicity, our proposed method is believed to be more applicable than existing methods for seasonal or monthly water monitoring of diatom populations from sections of interconnected rivers, which would help police narrow the investigation scope to confirm the identity of an immersed body.

Zhang Ji, Zhou Yuanyuan, Vieira Duarte Nuno, Cao Yongjie, Deng Kaifei, Cheng Qi, Zhu Yongzheng, Zhang Jianhua, Qin Zhiqiang, Ma Kaijun, Chen Yijiu, Huang Ping


Convolutional neutral network, Deep learning, Diatom, Digital pathology, Drowning, Site of drowning

General General

Artificial intelligence in emergency medicine: A scoping review.

In Journal of the American College of Emergency Physicians open

Introduction : Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine.

Methods : The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation.

Results : Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out-of-hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome.

Conclusion : AI-related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction-based diagnoses. Higher quality evidence is needed to further assess both short- and long-term clinical outcomes.

Kirubarajan Abirami, Taher Ahmed, Khan Shawn, Masood Sameer


algorithm, artificial intelligence, artificial neural networks, emergency department, emergency medicine, machine learning, technology