Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole-Body MRI for the Detection of Recurrence in Melanoma Patients at High Risk of Relapse

Simple Summary Treatment for metastatic melanoma patients has significantly improved in the last decade. Literature suggests a better prognosis for patients with minimal disease burden. No consensus exists on the surveillance of melanoma patients at high risk of recurrence. In this trial we evaluated the use of whole-body MRI for the surveillance of melanoma patients, more specifically in patients with stage IIIb/c disease following surgical resection and in patients obtaining a durable response on systemic therapy. Overall, we can conclude that whole- body MRI is a safe and sensitive technique for the discovery of distant melanoma metastasis. Abstract Introduction: No standard protocol for surveillance for melanoma patients is established. Whole-body magnetic resonance imaging (whole-body MRI) is a safe and sensitive technique that avoids exposure to X-rays and contrast agents. This prospective study explores the use of whole-body MRI for the early detection of recurrences. Material and Methods: Patients with American Joint Committee on Cancer Staging Manual (seventh edition; AJCC-7) stages IIIb/c or -IV melanoma who were disease-free following resection of macrometastases (cohort A), or obtained a durable complete response (CR) or partial response (PR) following systemic therapy (cohort B), were included. All patients underwent whole-body MRI, including T1, Short Tau Inversion Recovery, and diffusion-weighted imaging, every 4 months the first 3 years of follow-up and every 6 months in the following 2 years. A total body skin examination was performed every 6 months. Results: From November 2014 to November 2019, 111 patients were included (four screen failures, cohort A: 68 patients; cohort B: 39 patients). The median follow-up was 32 months. Twenty-six patients were diagnosed with suspected lesions. Of these, 15 patients were diagnosed with a recurrence on MRI. Eleven suspected lesions were considered to be of non-neoplastic origin. In addition, nine patients detected a solitary subcutaneous metastasis during self-examination, and two patients presented in between MRIs with recurrences. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were, respectively, 58%, 98%, 58%, 98%, and 98%. Sensitivity and specificity for the detection of distant metastases was respectively 88% and 98%. No patient experienced a clinically meaningful (>grade 1) adverse event. Conclusions: Whole-body MRI for the surveillance of melanoma patients is a safe and sensitive technique sparing patients′ cumulative exposure to X-rays and contrast media.
Structure of the Human Secretory Immunoglobulin M Core

Immunoglobulins (Ig) A and M are the only human antibodies that form oligomers and undergo transcytosis to mucosal secretions via the polymeric Ig receptor (pIgR). When complexed with the J-chain (JC) and the secretory component (SC) of pIgR, secretory IgA and IgM (sIgA and sIgM) play critical roles in host-pathogen defense. Recently, we determined the structure of sIgA-Fc which elucidated the mechanism of polymeric IgA assembly and revealed an extensive binding interface between IgA-Fc, JC, and SC. Despite low sequence identity shared with IgA-Fc, IgM-Fc also undergoes JC-mediated assembly and binds pIgR. Here, we report the structure of sIgM-Fc and carryout a systematic comparison to sIgA-Fc. Our structural analysis reveals a remarkably conserved mechanism of JC-templated oligomerization and SC recognition of both IgM and IgA through highly a conserved network of interactions. These studies reveal the structurally conserved features of sIgM and sIgA required for function in mucosal immunity.
Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Heterogeneity is a hallmark of cancer, diabetes, cardiovascular diseases, and many other complex diseases. This study has been partly motivated by the unsupervised heterogeneity analysis for complex diseases based on molecular and imaging data, for which, network‐based analysis, by accommodating the interconnections among variables, can be more informative than that limited to mean, variance, and other simple distributional properties. In the literature, there has been very limited research on network‐based heterogeneity analysis, and a common limitation shared by the existing techniques is that the number of subgroups needs to be specified a priori or in an ad hoc manner. In this article, we develop a penalized fusion approach for heterogeneity analysis based on the Gaussian graphical model. It applies penalization to the mean and precision matrix parameters to generate regularized and interpretable estimates. More importantly, a fusion penalty is imposed to “automatedly” determine the number of subgroups and generate more concise, reliable, and interpretable estimation. Consistency properties are rigorously established, and an effective computational algorithm is developed. The heterogeneity analysis of non‐small‐cell lung cancer based on single‐cell gene expression data of the Wnt pathway and that of lung adenocarcinoma based on histopathological imaging data not only demonstrate the practical applicability of the proposed approach but also lead to interesting new findings.
Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.
A CNN-Based Length-Aware Cascade Road Damage Detection Approach

Accurate and robust detection of road damage is essential for public transportation safety. Currently, deep convolutional neural networks (CNNs)-based road damage detection algorithms to localize and classify damage with a bounding box have achieved remarkable progress. However, research in this field fails to take into account two key characteristics of road damage: weak semantic information and abnormal geometric properties, resulting in inappropriate feature representation and suboptimal detection results. To boost the performance, we propose a CNN-based cascaded damage detection network, called CrdNet. The proposed model has three parts: (1) We introduce a novel backbone network, named LrNet, that reuses low-level features and mixes suitable range dependency features to learn high-to-low level feature fusions for road damage weak semantic information representation. (2) We apply multi-scale and multiple aspect ratios anchor mechanism to generate high-quality positive samples regarding the damage with abnormal geometric properties for network training. (3) We designed an adaptive proposal assignment strategy and performed cascade predictions on corresponding branches that can establish different range dependencies. The experiments show that the proposed method achieves mean average precision (mAP) of 90.92% on a collected road damage dataset, demonstrating the good performance and robustness of the model.
Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study

Background Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. Objective This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. Methods The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance. Results The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model. Conclusions Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.
Using Quantitative Passive Thermography and Modified Paris-Law for Probabilistic Calculation of the Fatigue Damage Development in a CFRP-Aluminum Hybrid Joint

Although metal to Carbon-fiber-reinforced-polymer (CFRP) hybrid-joints possess a high lightweight construction potential, their extensive application has to deal with interfacial stress concentrations promoting fatigue damage. Furthermore, the underlying damage processes and their influencing factors are still not completely understood. Besides interfacial property-gradients, generic shapes counteract a precise determination of local stresses or strains, respectively. Hence, new methods are required that combine non-destructive testing and fracture mechanics to account for the fatigue damage. In this work, data of mechanical fatigue testing of an aluminum-CFRP hybrid-structure is presented by means of the dynamic stiffness and the mechanical hysteresis. Additionally, in situ passive thermography allows for capturing the heat development due to delamination growth. Correlating the obtained data implies that faster delamination growth coincides with higher amplitude values of lock-in thermography and higher mechanical hysteresis. Supported by this observation, a model is formulated to calculate the local dissipation per loading cycle. Further integration into a Paris-law like formulation results in a calculation model to account for the mode-I fatigue delamination growth. Additional validation of the model parameters shows good agreement with the experimental data.
Immunogenicity and safety of rapid scheme vaccination against tick-borne encephalitis in HIV-1 infected persons

Abstract Tick-borne encephalitis (TBE) is a vector-borne infection associated with a variety of potentially serious complications and sequelae. Vaccination against TBE is strongly recommended for people living in endemic areas. There are two TBE vaccination schemes – standard and rapid – which differ in the onset of protection. With vaccination in a rapid schedule, protection starts as early as 4 weeks after the first dose and is therefore especially recommended for non-immune individuals travelling to endemic areas. Both schemes work reliably in immunocompetent individuals, but only little is known about how TBE vaccination works in people with HIV infection. Our aim was to assess the immunogenicity and safety of the rapid scheme of TBE vaccination in HIV-1 infected individuals. Concentrations of TBE-specific IgG > 126 VIEU/ml were considered protective. The seroprotection rate was 35.7% on day 28 and 39.3% on day 60. There were no differences between responders and non-responders in baseline and nadir CD4 + T lymphocytes. No serious adverse events were observed after vaccination. The immunogenicity of the TBE vaccination was unsatisfactory in our study and early protection was only achieved in a small proportion of vaccinees. Therefore, TBE vaccination with the rapid scheme cannot be recommended for HIV-1 infected individuals.
Corn Starch/Chitosan Nanoparticles/Thymol Bio-Nanocomposite Films for Potential Food Packaging Applications

This work aims to develop corn starch/chitosan nanoparticles/thymol (CS/CNP/Thy) bio-nanocomposite films as potential food packaging materials that can enhance the shelf life of food. CS/CNP/Thy bio-nanocomposite films were prepared by the addition of different concentrations of thymol (0, 1.5, 3.0, 4.5 w/w%) using a solvent casting method. The resulting films were characterized in terms of optical, mechanical, and water vapor permeability (WVP) properties. The addition of thymol was found to reduce the tensile strength (TS), elongation at break (EAB), and Young’s modulus (YM) of the films. Generally, the increment in the concentration of thymol did not significantly affect the TS, EAB, and YM values. The addition of 1.5 w/w% thymol increased the WVP of the films but the WVP reduced with the increase in thymol concentrations. CS/CNP/Thy-3% bio-nanocomposite films demonstrated the potential to lengthen the shelf life of cherry tomatoes packed with the films, whereby the cherry tomatoes exhibited no significant changes in firmness and the lowest weight loss. In addition, no mold growth was observed on the sliced cherry tomatoes that were in direct contact with the films during 7 days of storage, proving the promising application of the films as active food packaging materials.
Development of Korean CARcinogen EXposure: An Initiative of the Occupational Carcinogen Surveillance System in Korea

Abstract Objective To prevent occupational cancers, carcinogen exposure surveillance systems have been developed in many countries. This study aimed to develop a carcinogen exposure database specific to South Korea. Methods Twenty known human carcinogens were selected for this study. The International Standard Classification of Industry was used for a classification scheme of industries. Three nationwide occupational exposure databases, the Work Environment Measurement Database, the Special Health Examination Database, and the Work Environment Condition Survey, were used to calculate reference exposure prevalence estimates by carcinogen and industry. Then, 37 professional industrial hygienists with at least 19 years of field experience provided their own exposure prevalence estimates, after reviewing the abovementioned reference estimates derived from three data sources. The median value of the experts’ estimates was used as the final exposure prevalence. Finally, the number of exposed workers was computed by multiplying the final exposure prevalence by the number of workers extracted from the 2010 national census data by carcinogen and industry. Results The exposure prevalence and the number of exposed workers were calculated according to 20 carcinogen and 228 minor industrial groups, assuming year 2010 circumstances. The largest population was exposed to welding fumes (326 822 workers), followed by ultraviolet radiation (238 937 workers), ionizing radiation (168 712 workers), and mineral oil mist (146 798 workers). Conclusions Our results provide critical data on carcinogen exposure for the prevention of occupational cancers.
CD22-Directed CAR T-Cell Therapy Induces Complete Remissions in CD19-Directed CAR-Refractory Large B-Cell Lymphoma.

The prognosis for patients with large B-cell lymphoma (LBCL) progressing after treatment with chimeric antigen receptor (CAR) T-cell therapy targeting CD19 (CAR19) is poor. We report on the first three consecutive patients with autologous CAR19-refractory LBCL treated with a single infusion of autologous 1×106 CAR+ T-cells/kg targeting CD22 (CAR22) as part of a phase I dose escalation study. CAR22 therapy was relatively well tolerated, without any observed non-hematologic adverse events higher than grade 2. Following infusion, all three patients achieved complete remission, with all responses ongoing at the time of last follow up (mean 7.8 months, range 6-9.3). Circulating CAR22 cells demonstrated robust expansion (peak range 85.4-350 cells/µL), and persisted beyond three months in all patients with continued radiographic responses and corresponding decreases in circulating tumor DNA (ctDNA) beyond six months post-infusion. Further accrual at a higher dose level in this phase 1 dose-escalation study is ongoing and will explore the role of this therapy in patients who have failed prior CAR T-cell therapies. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT04088890).
Pro-inflammatory adiponectin in pediatric-onset multiple sclerosis

Background: Being obese is associated with both increased risk of developing multiple sclerosis (MS) and greater MS disease activity. Objectives: The objective of this study is to investigate levels and potential pathophysiologic contribution of serum adipose-hormones (adipokines) in pediatric-onset MS. Methods: Following a Luminex adipokine screen, adiponectin (APN) and its isoforms were quantified by enzyme-linked immunosorbent assay (ELISA) in 169 children with incident acquired demyelinating syndromes (ADS), prospectively ascertained as having either MS or other forms of inflammatory central nervous system (CNS) demyelination. The effect of recombinant APN and APN-containing sera was assessed on functional responses of normal human peripheral blood myeloid and T cells and on human CNS-derived microglia. Results: Compared to other cohorts, children with MS harbored higher serum APN levels, principally driven by higher levels of the low-molecular-weight isoform. Recombinant APN and pediatric MS serum-induced APN-dependent pro-inflammatory activation of CD14+ monocytes and of activated CD4+ and CD8+ T cells (both directly and indirectly through myeloid cells). APN induced human microglia activation while inhibiting their expression of molecules associated with quiescence. Conclusions: Elevated APN levels in children with MS may contribute to enhanced pro-inflammatory states of innate and adaptive peripheral immune responses and breach CNS-resident microglia quiescence, providing a plausible and potentially targetable mechanism by which APN contributes to MS disease activity.
Plasma-Conditioned Liquids as Anticancer Therapies In Vivo: Current State and Future Directions

Simple Summary During the last decade, cold atmospheric plasmas (CAP) have been broadly investigated for their therapeutic effect against cancer. CAP sources can be used to treat liquid media, thereby generating plasma-conditioned liquids (PCL). PCL represent a very interesting alternative to direct CAP treatment, because they may allow treatment of malignant tumors located in inner organs of the body by means of an injection, thus avoiding multiple surgeries. Although research on this therapy is still in its early stage, PCL have already demonstrated their potential anticancer effect in different types of cancer in vivo. This review gathers the existing literature involving PCL treatments in vivo, highlighting the differences between the approaches undertaken and the need for establishing standardized protocols in order to better understand the effects of PCL-based therapies in vivo. Abstract Plasma-conditioned liquids (PCL) are gaining increasing attention in the medical field, especially in oncology, and translation to the clinics is advancing on a good path. This emerging technology involving cold plasmas has great potential as a therapeutic approach in cancer diseases, as PCL have been shown to selectively kill cancer cells by triggering apoptotic mechanisms without damaging healthy cells. In this context, PCL can be injected near the tumor or intratumorally, thereby allowing the treatment of malignant tumors located in internal organs that are not accessible for direct cold atmospheric plasma (CAP) treatment. Therefore, PCL constitutes a very interesting and minimally invasive alternative to direct CAP treatment in cancer therapy, avoiding surgeries and allowing multiple local administrations. As the field advances, it is progressively moving to the evaluation of the therapeutic effects of PCL in in vivo scenarios. Exciting developments are pushing forward the clinical translation of this novel therapy. However, there is still room for research, as the quantification and identification of reactive oxygen and nitrogen species (RONS) in in vivo conditions is not yet clarified, dosage regimens are highly variable among studies, and other more relevant in vivo models could be used. In this context, this work aims to present a critical review of the state of the field of PCL as anticancer agents applied in in vivo studies.
Population Pharmacokinetics of Tofacitinib in Patients With Moderate to Severe Ulcerative Colitis

Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). We characterized tofacitinib pharmacokinetics in patients with moderate to severe UC, and the effects of covariates on variability in pharmacokinetic parameter estimates. Data were pooled from 1 8‐week phase 2 and 2 8‐week phase 3 induction studies, and a 52‐week phase 3 maintenance study (N = 1096). Population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modeling. Potential predictors of apparent oral clearance (CL/F) and volume of distribution (V/F) were evaluated. The PK was described by a 1‐compartment model parameterized in terms of CL/F (26.3 L/hour [h]) and V/F (115.8 L), with first‐order absorption (Ka; 9.85 h−1) and lag time (0.236 h). The derived elimination half‐life was approximately 3.05 h. In the final model, baseline creatinine clearance, sex, and race (Asian vs non‐Asian) were significant covariates for CL/F; significant covariates for V/F were age, sex, and body weight; baseline albumin and baseline Mayo score were not significant covariates. CL/F between‐patient variability was estimated at 22%. Tofacitinib exposure did not change significantly over the duration of induction/maintenance treatment in patients with UC. Although statistically significant covariate effects on CL/F and V/F were observed, the magnitude of the effects are not clinically significant. Therefore, dose adjustment/restrictions for age, body weight, sex, race, or baseline disease severity are not required during tofacitinib treatment. ClinicalTrials.gov numbers: NCT00787202, NCT01465763, NCT01458951, NCT01458574.