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Radiology Radiology

Imaging of OA - From disease modification to clinical utility.

In Best practice & research. Clinical rheumatology

Multiple disease-modifying osteoarthritis drug (DMOAD) trials were done in the last two decades, but no pharmacological agent has yet been approved by regulatory agencies as an effective therapy to date. Given the fact that we have seen the recent discontinuation of several late-stage drug development trials, a careful strategy is needed in formulating a plan for a successful DMOAD trial - including the various roles of imaging. This narrative review article will summarize how imaging is utilized in osteoarthritis from the perspective of disease modification to clinical utility. We will describe how semi-quantitative and quantitative magnetic resonance imaging approaches have been deployed in DMOAD trials. We will then review the utility of musculoskeletal ultrasound in research and clinical settings. Finally, novel hybrid positron emission tomography/MRI techniques and current research using artificial intelligence will be discussed, focusing on original research. Older publications are included for the discussion of the previous DMOAD trials and other relevant topics where deemed appropriate.

Hayashi Daichi, Roemer Frank W, Eckstein Felix, Samuels Jonathan, Guermazi Ali


Clinical trials, DMOAD, MRI, Osteoarthritis

Surgery Surgery

Molecular T-cell‒mediated rejection in transbronchial and mucosal lung transplant biopsies is associated with future risk of graft loss.

In The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation

BACKGROUND : We previously developed molecular assessment systems for lung transplant transbronchial biopsies (TBBs) with high surfactant and bronchial mucosal biopsies, identifying T-cell‒mediated rejection (TCMR) on the basis of the expression of rejection-associated transcripts, but the relationship of rejection to graft loss is unknown. This study aimed to develop molecular assessments for TBBs and mucosal biopsies and to establish the impact of molecular TCMR on graft survival.

METHODS : We used microarrays and machine learning to assign TCMR scores to an expanded cohort of 457 TBBs (367 high surfactant plus 90 low surfactant) and 314 mucosal biopsies. We tested the score agreement between TBB-TBB, mucosal-mucosal, and TBB-mucosal biopsy pairs in the same patient. We also assessed the association of molecular TCMR scores with graft loss (death or retransplantation) and compared it with the prognostic associations for histology and donor-specific antibodies.

RESULTS : The molecular TCMR scores assigned in all the TBBs performed similarly to those in high-surfactant TBBs, indicating that variation in alveolation in TBBs does not prevent the detection of TCMR. Mucosal biopsy pieces showed less piece-to-piece variation than TBBs. TCMR scores in TBBs agreed with those in mucosal biopsies. In both TBBs and mucosal biopsies, molecular TCMR was associated with graft loss, whereas histologic rejection and donor-specific antibodies were not.

CONCLUSIONS : Molecular TCMR can be detected in TBBs regardless of surfactant and in mucosal biopsies, which show less variability in the sampled tissue than TBBs. On the basis of these findings, molecular TCMR appears to be an important predictor of the risk of future graft failure.


Halloran Kieran, Parkes Michael D, Timofte Irina, Snell Gregory, Westall Glen, Havlin Jan, Lischke Robert, Hachem Ramsey, Kreisel Daniel, Levine Deborah, Kubisa Bartosz, Piotrowska Maria, Juvet Stephen, Keshavjee Shaf, Jaksch Peter, Klepetko Walter, Hirji Alim, Weinkauf Justin, Halloran Philip F


graft loss, lung biopsy, lung transplant, microarray, rejection

General General

Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion.

In Stroke ; h5-index 83.0

BACKGROUND AND PURPOSE : Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility of electroencephalography for acute stroke/transient ischemic attack (TIA) and for LVO.

METHODS : Patients (n=100) with suspected acute stroke in an emergency department underwent clinical exam then electroencephalography using a dry-electrode system. Four models classified patients, first as acute stroke/TIA or not, then as acute stroke with LVO or not: (1) clinical data, (2) electroencephalography data, (3) clinical+electroencephalography data using logistic regression, and (4) clinical+electroencephalography data using a deep learning neural network. Each model used a training set of 60 randomly selected patients, then was validated in an independent cohort of 40 new patients.

RESULTS : Of 100 patients, 63 had a stroke (43 ischemic/7 hemorrhagic) or TIA (13). For classifying patients as stroke/TIA or not, the clinical data model had area under the curve=62.3, whereas clinical+electroencephalography using deep learning neural network model had area under the curve=87.8. Results were comparable for classifying patients as stroke with LVO or not.

CONCLUSIONS : Adding electroencephalography data to clinical measures improves diagnosis of acute stroke/TIA and of acute stroke with LVO. Rapid acquisition of dry-lead electroencephalography is feasible in the emergency department and merits prehospital evaluation.

Erani Fareshte, Zolotova Nadezhda, Vanderschelden Benjamin, Khoshab Nima, Sarian Hagop, Nazarzai Laila, Wu Jennifer, Chakravarthy Bharath, Hoonpongsimanont Wirachin, Yu Wengui, Shahbaba Babak, Srinivasan Ramesh, Cramer Steven C


brain, deep learning, early diagnosis, electroencephalography, transient ischemic attack

Radiology Radiology

Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting.

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.

METHODS : Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 2:1 for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS : 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. First prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). Second model developed has same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on H-L test (p = 0.781 and 0.155 respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.

CONCLUSION : Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.

Ng Ming-Yen, Wan Eric Yuk Fai, Wong Ho Yuen Frank, Leung Siu Ting, Lee Jonan Chun Yin, Chin Thomas Wing-Yan, Lo Christine Shing Yen, Lui Macy Mei-Sze, Chan Edward Hung Tat, Fong Ambrose Ho-Tung, Yung Fung Sau, Ching On Hang, Chiu Keith Wan-Hang, Chung Tom Wai Hin, Vardhanbhuti Varut, Lam Hiu Yin Sonia, To Kelvin Kai Wang, Chiu Jeffrey Long Fung, Lam Tina Poy Wing, Khong Pek Lan, Liu Raymond Wai To, Man Chan Johnny Wai, Ka Lun Alan Wu, Lung Kwok-Cheung, Hung Ivan Fan Ngai, Lau Chak Sing, Kuo Michael D, Ip Mary Sau-Man


COVID-19, Nomogram, Prediction Model, chest x-ray, white cell count

General General

Classification and quantification of microplastic (< 100 µm) using FPA-FTIR imaging system and machine learning.

In Analytical chemistry

Microplastics are defined as microscopic plastic particles in the range from few µm and up to 5 mm. These small particles are classified as primary microplastic when they are manufactured in this size range, whereas secondary microplastics arise from the fragmentation of larger objects. Microplastics are a widespread emerging pollutant and investigations are underway to determine potential harmfulness to biota and human health. However, progress is hindered by the lack of suitable analytical methods for rapid, routine and unbiased measurements. This work aims to develop an automated analytical method for the characterization of small microplastic (< 100 µm) using micro Fourier Transform Infrared (µ-FTIR) hyper-spectral imaging and machine learning tools. Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were evaluated, applying different data pre-processing strategies for classification of nine of the most common polymers produced worldwide. The hyperspectral images were also analyzed to quantify particle abundance and size automatically. PLS-DA presented a better analytical performance in comparison with SIMCA models with higher sensitivity, sensibility and lower misclassification error. PLS-DA was less sensitive to edge effects on spectra and poorly focused regions of particles. The approach was tested on a seabed sediment sample (Roskilde Fjord, Denmark) to demonstrate the method efficiency. The proposed method offers an efficient automated approach for microplastic polymer characterization, abundance numeration and size distribution with substantial benefits for methods standardization.

da Silva Vitor Hugo, Murphy Fionn, Amigo Jose Manuel, Stedmon Colin Andrew, Strand Jakob


Pathology Pathology

A Single-Cell RNA Expression Map of Human Coronavirus Entry Factors.

In Cell reports ; h5-index 119.0

To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell transcriptomics across various healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Intestinal goblet cells, enterocytes, and kidney proximal tubule cells appear highly permissive to SARS-CoV-2, consistent with clinical data. Our analysis also predicts non-canonical entry paths for lung and brain infections. Spermatogonial cells and prostate endocrine cells also appear to be permissive to SARS-CoV-2 infection, suggesting male-specific vulnerabilities. Both pro- and anti-viral factors are highly expressed within the nasal epithelium, with potential age-dependent variation, predicting an important battleground for coronavirus infection. Our analysis also suggests that early embryonic and placental development are at moderate risk of infection. Lastly, SCARF expression appears broadly conserved across a subset of primate organs examined. Our study establishes a resource for investigations of coronavirus biology and pathology.

Singh Manvendra, Bansal Vikas, Feschotte C├ędric


COVID-19, SARS-CoV-2, coronaviruses, restriction factors, scRNA-seq, viral receptors