Effect of deuteration on the single dose pharmacokinetic properties and postoperative analgesic activity of methadone

Although methadone is effective in the management of acute pain, the complexity of its absorption-distribution-metabolism-excretion profile limits its use as an opioid of choice for perioperative analgesia. Because deuteration is known to improve the pharmacokinetic, pharmacodynamic and toxicological properties of some drugs, here we characterized the single dose pharmacokinetic properties and post-operative analgesic efficacy of d₉-methadone.The pharmacokinetic profiles of d₉-methadone and methadone administered intravenously to CD-1 male mice revealed that deuteration leads to a 5.7- and 4.4-fold increase in the area under the time-concentration curve and maximum concentration in plasma, respectively, as well as reduction in clearance (0.9 ± 0.3 L/h/kg vs 4.7 ± 0.8 L/h/kg). The lower brain-to-plasma ratio of d₉-methadone compared to that of methadone (0.35 ± 0.12 vs 2.05 ± 0.62) suggested that deuteration decreases the transfer of the drug across the blood-brain barrier. The estimated LD₅₀ value for a single intravenous dose of d₉-methadone was 2.1-fold higher than that for methadone. Moreover, d₉-methadone outperformed methadone in the efficacy against postoperative pain by primarily activating peripheral opioid receptors. Collectively, these data suggest that the replacement of three hydrogen atoms in three methyl groups of methadone altered its pharmacokinetic properties, improved safety, and enhanced its analgesic efficacy.     

Anomaly segmentation in retinal images with poisson-blending data augmentation

Diabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions is of great importance for the early diagnosis of DR. However, simultaneous segmentation of multi-type DR lesions is technically challenging because of 1) the lack of pixel-level annotations and 2) the large diversity between different types of DR lesions. In this study, first, we propose a novel Poisson-blending data augmentation (PBDA) algorithm to generate synthetic images, which can be easily utilized to expand the existing training data for lesion segmentation. We perform extensive experiments to recognize the important attributes in the PBDA algorithm. We show that position constraints are of great importance and that the synthesis density of one type of lesion has a joint influence on the segmentation of other types of lesions. Second, we propose a convolutional neural network architecture, named DSR-U-Net++ (i.e., DC-SC residual U-Net++), for the simultaneous segmentation of multi-type DR lesions. Ablation studies showed that the mean area under precision recall curve (AUPR) for all four types of lesions increased by >5% with PBDA. The proposed DSR-U-Net++ with PBDA outperformed the state-of-the-art methods by 1.7%-9.9% on the Indian Diabetic Retinopathy Image Dataset (IDRiD) and 67.3% on the e-ophtha dataset with respect to mean AUPR. The developed method would be an efficient tool to generate large-scale task-specific training data for other medical anomaly segmentation tasks.     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.     

Applications of three-dimensional printing in ophthalmology

Three-dimensional (3D) printing is increasingly used to produce customized objects and is a promising alternative to traditional manufacturing methods in diverse fields, such as dentistry and orthopedics. Already in use in other medical specialties, adoption in ophthalmology has been limited to date. This review aims to provide an overview of 3D printing technology with respect to current and potential applications in ophthalmic practice.Medline, Embase, and Internet searches were performed with “3D printing,” “ophthalmology,” “dentistry,” “orthopaedics” and their synonyms used as main search terms. In addition, search terms related to clinical applications such as “surgery” and “implant” were employed.3D printing has multiple applications in ophthalmology, including in diagnosis, surgery, prosthetics, medications, and medical education. Within the past decade, researchers have produced 3D printed models of objects such as implants, prostheses, anatomical models and surgical simulators. Further development is necessary to generate optimal biomaterials for various applications, and the quality and long-term performance of 3D models needs to be validated.     

The epidemiology of infectious diseases in Europe in 2020 versus 2017–2019 and the rise of tick-borne encephalitis (1995–2020)

Health control measures instituted in 2020 to mitigate the COVID-19 pandemic decreased the case numbers of many infectious diseases across Europe. One notable exception was tick-borne encephalitis (TBE). In Austria, Germany, Switzerland, Lithuania, and the Czech Republic, the upturn was significantly higher compared to the average of the three years previously (P<0.05), with increases of 88%, 48%, 51%, 28%, and 18%, respectively. Six countries reported TBE incidences of ≥5 cases/100,000, defined as highly endemic by the World Health Organization (WHO). Possible factors contributing to this surge may include increased participation in outdoor activities in endemic regions and increased tick counts/tick activity. In highly endemic regions, the WHO recommends that vaccination be offered to all age groups, including children.     

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019(COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web inter...     

Physicochemical and Functional Characterization of HS016, a Biosimilar of Adalimumab (Humira)

The characterization of a biosimilar drug HS016, the reference product adalimumab (Humira), and their biosimilarities were determined using physical chemistry and functional similarity tests. The primary and higher order structures, size and charge variants, glycosylation profiles, and in vitro potency of both antibodies were characterized both for unstressed and stability samples. Slight differences were observed in the relative levels of methionine oxidation, low molecular weight components, terminal lysine variant, high mannoses and galactosylated glycans between HS016 and Humira. However, no differences in antigen binding activity, Fc receptor affinity, antibody-dependent cell-mediated cytotoxicity or complemented-dependent cytotoxicity were found. The primary and higher order structures, physicochemical properties, and biological activity of HS016 and adalimumab were similar.     

Quantitative Assessment of the Impact of Crohn's Disease on Protein Abundance of Human Intestinal Drug-Metabolising Enzymes and Transporters

Crohn's disease affects the mucosal layer of the intestine, predominantly ileum and colon segments, with the potential to affect the expression of intestinal enzymes and transporters, and consequently, oral drug bioavailability. We carried out a quantitative proteomic analysis of inflamed and non-inflamed ileum and colon tissues from Crohn's disease patients and healthy donors. Homogenates from samples in each group were pooled and protein abundance determined by liquid chromatography–mass spectrometry (LC-MS). In inflamed Crohn's ileum, CYP3A4, CYP20A1, CYP51A1, ADH1B, ALPI, FOM1, SULT1A2, SULT1B1 and ABCB7 showed ≥10-fold reduction in abundance compared with healthy baseline. By contrast, only MGST1 showed ≥10 fold reduction in inflamed colon. Ileal UGT1A1, MGST1, MGST2, and MAOA levels increased by ≥2 fold in Crohn's patients, while only ALPI showed ≥2 fold increase in the colon. Counter-intuitively, non-inflamed ileum had a higher magnitude of fold change than inflamed tissue when compared with healthy tissue. Marked but non-uniform alterations were observed in the expression of various enzymes and transporters in ileum and colon compared with healthy samples. Modelling will allow improved understanding of the variable effects of Crohn's disease on bioavailability of orally administered drugs.     

Automatic Grading Assessments for Knee MRI Cartilage Defects via Self-ensembling Semi-supervised Learning with Dual-Consistency

Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT.