DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.

Background: No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting. Methods: A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24-48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24-48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric. Results: The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585-0.743) at baseline, 0.739 (95% CI: 0.703-0.775) at 12 months, and 0.775 (95% CI: 0.686-0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees. Conclusions: This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative.

Background: The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis. Methods: Data used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC). Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics. Results: The comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively). Conclusions: Our results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.

Remdesivir for Severe Coronavirus Disease 2019 (COVID-19) Versus a Cohort Receiving Standard of Care.

BACKGROUND: We compared the efficacy of the antiviral agent, remdesivir, versus standard-of-care treatment in adults with severe coronavirus disease 2019 (COVID-19) using data from a phase 3 remdesivir trial and a retrospective cohort of patients with severe COVID-19 treated with standard of care. METHODS: GS-US-540-5773 is an ongoing phase 3, randomized, open-label trial comparing two courses of remdesivir (remdesivir-cohort). GS-US-540-5807 is an ongoing real-world, retrospective cohort study of clinical outcomes in patients receiving standard-of-care treatment (non-remdesivir-cohort). Inclusion criteria were similar between studies: patients had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, were hospitalized, had oxygen saturation ≤94% on room air or required supplemental oxygen, and had pulmonary infiltrates. Stabilized inverse probability of treatment weighted multivariable logistic regression was used to estimate the treatment effect of remdesivir versus standard of care. The primary endpoint was the proportion of patients with recovery on day 14, dichotomized from a 7-point clinical status ordinal scale. A key secondary endpoint was mortality. RESULTS: After the inverse probability of treatment weighting procedure, 312 and 818 patients were counted in the remdesivir- and non-remdesivir-cohorts, respectively. At day 14, 74.4% of patients in the remdesivir-cohort had recovered versus 59.0% in the non-remdesivir-cohort (adjusted odds ratio [aOR] 2.03: 95% confidence interval [CI]: 1.34-3.08, P < .001). At day 14, 7.6% of patients in the remdesivir-cohort had died versus 12.5% in the non-remdesivir-cohort (aOR 0.38, 95% CI: .22-.68, P = .001). CONCLUSIONS: In this comparative analysis, by day 14, remdesivir was associated with significantly greater recovery and 62% reduced odds of death versus standard-of-care treatment in patients with severe COVID-19. CLINICAL TRIALS REGISTRATION: NCT04292899 and EUPAS34303.

Compassionate Use of Remdesivir for Patients with Severe Covid-19.

BACKGROUND: Remdesivir, a nucleotide analogue prodrug that inhibits viral RNA polymerases, has shown in vitro activity against SARS-CoV-2. METHODS: We provided remdesivir on a compassionate-use basis to patients hospitalized with Covid-19, the illness caused by infection with SARS-CoV-2. Patients were those with confirmed SARS-CoV-2 infection who had an oxygen saturation of 94% or less while they were breathing ambient air or who were receiving oxygen support. Patients received a 10-day course of remdesivir, consisting of 200 mg administered intravenously on day 1, followed by 100 mg daily for the remaining 9 days of treatment. This report is based on data from patients who received remdesivir during the period from January 25, 2020, through March 7, 2020, and have clinical data for at least 1 subsequent day. RESULTS: Of the 61 patients who received at least one dose of remdesivir, data from 8 could not be analyzed (including 7 patients with no post-treatment data and 1 with a dosing error). Of the 53 patients whose data were analyzed, 22 were in the United States, 22 in Europe or Canada, and 9 in Japan. At baseline, 30 patients (57%) were receiving mechanical ventilation and 4 (8%) were receiving extracorporeal membrane oxygenation. During a median follow-up of 18 days, 36 patients (68%) had an improvement in oxygen-support class, including 17 of 30 patients (57%) receiving mechanical ventilation who were extubated. A total of 25 patients (47%) were discharged, and 7 patients (13%) died; mortality was 18% (6 of 34) among patients receiving invasive ventilation and 5% (1 of 19) among those not receiving invasive ventilation. CONCLUSIONS: In this cohort of patients hospitalized for severe Covid-19 who were treated with compassionate-use remdesivir, clinical improvement was observed in 36 of 53 patients (68%). Measurement of efficacy will require ongoing randomized, placebo-controlled trials of remdesivir therapy. (Funded by Gilead Sciences.).

Pharmacokinetics of Tenofovir Alafenamide When Coadministered With Other HIV Antiretrovirals.

BACKGROUND: Tenofovir alafenamide (TAF), a prodrug of the nucleotide analogue tenofovir (TFV), is an antiretroviral (ARV) agent approved either as a complete regimen [elvitegravir/cobicistat/emtricitabine (F)/TAF, rilpivirine/F/TAF, bictegravir/F/TAF], or for use with other ARVs (F/TAF), for treatment of HIV. TAF is a substrate of P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) transporters. Disposition of TAF may be altered by comedications that can inhibit or induce P-gp or BCRP transporters. The effects of ARVs on the pharmacokinetics of TAF were evaluated in 3 studies. METHODS: Healthy participants received TAF administered alone or with rilpivirine in study 1, with dolutegravir, ritonavir-boosted atazanavir (ATV + RTV), lopinavir (LPV/RTV), or darunavir (DRV + RTV) in study 2, and with the pharmacokinetic enhancer cobicistat or efavirenz in study 3. RESULTS: Across the 3 studies, 98 participants received treatment with TAF and a coadministered agent (n = 10-34/cohort). All study treatments were well tolerated. TAF and TFV exposures were unaffected after co-administration with rilpivirine and dolutegravir. Coadministration with P-gp/BCRP inhibitors such as cobicistat or PI-based regimens (ATV + RTV, LPV/r, or DRV + RTV) resulted in a range of 6%-183% increases in TAF and 105%-316% increases in TFV exposure, whereas coadministration with a P-gp inducer, efavirenz, resulted in a 15%-24% decrease in TAF and TFV exposure. CONCLUSIONS: Evaluation of the drug interaction between TAF and other commonly prescribed boosted and unboosted ARVs provides characterization of the susceptibility of TAF and/or TFV pharmacokinetics to inhibitors or inducers of P-gp/BCRP transporters.

Electrochemical probing into the active sites of graphitic-layer encapsulated iron oxygen reduction reaction electrocatalysts.

The graphitic-layer encapsulated iron-containing nanoparticles (G@Fe) have been proposed as a potential type of active and stable non-precious metal electrocatalysts (NPMCs) for the oxygen reduction reaction (ORR). However, the contribution of the encapsulated components to the ORR activity is still unclear compared with the well-recognized surface coordinated FeNx/C structure. Using the strong complexing effect of the iron component with anions, cyanide (CN-) in alkaline and thiocyanate (SCN-) in acidic media, the metal containing active sites are electrochemically probed. Three representative catalysts are chosen for a comparison including the as-prepared encapsulated G@Fe, commercial Fe/N/C catalyst with iron-nitrogen coordinated surface functionalities and molecular iron phthalocyanine (FePc) containing well-defined structures and compositions. It was found that all samples showed significant shifts of half-wave potentials indicating that surface Fe coordinated sites in all cases. The G@Fe catalyst showed the weakest poisoning effect (the lowest shifts of half-wave potential) compared to the Fe/N/C and FePc catalysts in both electrolytes. These results could be explained that the encapsulated iron components influence the FeNx/C and/or NxC surface functionality.

Spontaneous and fast growth of large-area graphene nanofilms facilitated by oil/water interfaces.

An efficient wet-chemical method based on soft interfacial self-assembly is developed for spontaneous, fast growth of large-area graphene nanofilms on various substrates. The graphene nanofilms produced show tunable optical properties and a highly reversible optoelectronic response. Complementary to chemical vapor deposition, this method could offer a fast, simple, and low-cost chemical strategy to produce graphene nanofilms.