Implementation of Guideline-Based HBV Reactivation Management in Patients with Chronic HBV Infections of HBsAg or Resolved HBV Infection Undergoing Immunosuppressive Therapy.

INTRODUCTION: Although patients with HBV have a risk of reactivation after immunosuppressive therapy (IST), the status of their risk management is unclear in Japan. This study aims to describe the proportion of patients who received preventive management of HBV reactivation during ISTs in patients with chronic HBV infection of HBsAg or resolved HBV infection. METHOD: A retrospective cohort study was conducted using the JMDC Japanese claims database from April 2011 to June 2021. Patients with HBV infections of HbsAg who received ISTs or patients who had resolved HBV infections who received ISTs were identified from the database and evaluated for appropriate management to prevent HBV reactivation. RESULTS: In total, 6242 eligible patients were identified. The proportions of patients with appropriate HBV reactivation management, stratified by the HBV reactivation risk level of IST, was 43.1% (276/641) for high-risk, 40.2% (223/555) for intermediate-risk and 14.9% (741/4965) for low-risk patients. When the evaluation period for the outcome calculation was shortened from 360 to 180 days, the proportion for high risk increased to 52.7%. The odds ratios of large hospitals for receiving appropriate management were 2.16 (95% CI 1.12-4.44) in the high-risk, 4.63 (95% CI 2.34-10.25) in the intermediate-risk and 3.60 (95% CI 3.07-4.24) in the low-risk patients. CONCLUSION: HBV reactivation management was tailored according to the reactivation risk associated with IST. However, adherence to HBV reactivation management guidelines was sub-optimal, even among high-risk patients. This is especially the case for ensuring smaller-sized medical institutions, highlighting the need for further educational activities.

The study assesses the implementation of guideline-based management of hepatitis B virus reactivation during immunosuppressive therapy in Japan. The appropriate management of hepatitis B virus treatment involves prophylactic nucleos(t)ide analog (NUC) therapy and regular monitoring of hepatitis B virus DNA. This study aims to assess the extent to which these management practices are implemented in a clinical setting in Japan using a retrospective cohort study using the Japanese Medical Claims Database. The analysis identified 6242 eligible patients and identified whether they received appropriate management to prevent hepatitis B virus reactivation based on the level of risk associated with their immunosuppressive therapy. Based on the guidelines, the proportions of patients receiving appropriate reactivation management were 43.1% for high-risk, 40.2% for intermediate-risk and 14.9% for low-risk immunosuppressive therapy patients. Shortening the evaluation period from 360 to 180 days showed an increase in the proportion of high-risk patients to 52.7%, which indicated the potential challenge for continued monitoring after immunosuppressive therapy administration. The study shows that large hospitals present higher odds of patients receiving appropriate management. Overall, adherence to hepatitis B virus reactivation management guidelines was suboptimal, especially in smaller medical institutions, emphasizing the need for additional educational activities.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness.

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones.

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones.

Introduction: We conducted a 3-month, prospective study in a population of patients with Myasthenia Gravis (MG), utilizing a fully decentralized approach for recruitment and monitoring (ClinicalTrials.gov Identifier: NCT04590716). The study objectives were to assess the feasibility of collecting real-world data through a smartphone-based research platform, in order to characterize symptom involvement during MG exacerbations. Methods: Primary data collection included daily electronically recorded patient-reported outcomes (ePROs) on the presence of MG symptoms, the level of symptom severity (using the MG-Activities of Daily Living assessment, MG-ADL), and exacerbation status. Participants were also given the option to contribute data on their physical activity levels from their own wearable devices. Results: The study enrolled and onboarded 113 participants across 37 US states, and 73% (N= 82) completed the study. The mean age of participants was 53.6 years, 60% were female. Participants were representative of a moderate to severe MG phenotype, with frequent exacerbations, high symptom burden and multiple comorbidities. 55% of participants (N=45) reported MG exacerbations during the study, with an average of 6.3 exacerbation days per participant. Median average MG-ADL scores for participants during self-reported exacerbation and non-exacerbation periods were 7 (interquartile range 4-9, range 1-19) and 0.3 (interquartile range 0-0.8, range 0-9), respectively. Analyses examining relationships between patient-reported and patient-generated health data streams and exacerbation status demonstrated concordance between self-reported MG-ADL scores and exacerbation status, and identified features that may be used to understand and predict the onset of MG symptom exacerbations, including: 1.) dynamic changes in day-to-day symptom reporting and severity 2.) daily step counts as a measure of physical activity and 3.) clinical characteristics of the patient, including the amount of time since their initial diagnosis and their active medications related to MG treatment. Finally, application of unsupervised machine learning methods identified unique clusters of exacerbation subtypes, each with their own specific representation of symptoms and symptom severity. Conclusion: While these symptom signatures require further study and validation, our results suggest that digital phenotyping, characterized by increased multidimensionality and frequency of the data collection, holds promise for furthering our understanding of clinically significant exacerbations and reimagining the approach to treating MG as a heterogeneous condition.

Protective human IgE responses are promoted by comparable life-cycle dependent Tegument Allergen-Like expression in Schistosoma haematobium and Schistosoma mansoni infection.

Schistosoma haematobium is the most prevalent of the human-infecting schistosome species, causing significant morbidity in endemically exposed populations. Despite this, it has been relatively understudied compared to its fellow species, S. mansoni. Here we provide the first comprehensive characterization of the S. haematobium Tegument Allergen-Like protein family, a key protein family directly linked to protective immunity in S. mansoni infection. Comparable with observations for S. mansoni, parasite phylogenetic analysis and relative gene expression combined with host serological analysis support a cross-reactive relationship between S. haematobium TAL proteins, exposed to the host immune system as adult worms die, and closely related proteins, exposed during penetration by the infecting cercarial and early schistosomulae stages. Specifically, our results strengthen the evidence for host immunity driven by cross-reactivity between family members TAL3 and TAL5, establishing it for the first time for S. haematobium infection. Furthermore, we build upon this relationship to include the involvement of an additional member of the TAL protein family, TAL11 for both schistosome species. Finally, we show a close association between experience of infection and intensity of transmission and the development of protective IgE responses to these antigens, thus improving our knowledge of the mechanisms by which protective host immune responses develop. This knowledge will be critical in understanding how control efforts such as mass drug administration campaigns influence the development of host immunity and subsequent patterns of infection and disease within endemic populations.

Progressive cross-reactivity in IgE responses: an explanation for the slow development of human immunity to schistosomiasis?

People in regions of Schistosoma mansoni endemicity slowly acquire immunity, but why this takes years to develop is still not clear. It has been associated with increases in parasite-specific IgE, induced, some investigators propose, to antigens exposed during the death of adult worms. These antigens include members of the tegumental-allergen-like protein family (TAL1 to TAL13). Previously, in a group of S. mansoni-infected Ugandan males, we showed that IgE responses to three TALs expressed in worms (TAL1, -3, and -5) became more prevalent with age. Now, in a subcohort we examined associations of these responses with resistance to reinfection and use the data to propose a mechanism for the slow development of immunity. IgE was measured 9 weeks posttreatment and at reinfection at 2 years (n = 144). An anti-TAL5 IgE (herein referred to as TAL5 IgE) response was associated with reduced reinfection even after adjusting for age using regression analysis (geometric mean odds ratio, 0.24; P = 0.016). TAL5 IgE responders were a subset of TAL3 IgE responders, themselves a subset of TAL1 responders. TAL3 IgE and TAL5 IgE were highly cross-reactive, with TAL3 the immunizing antigen and TAL5 the cross-reactive antigen. Transcriptional and translational studies show that TAL3 is most abundant in adult worms and that TAL5 is most abundant in infectious larvae. We propose that in chronic schistosomiasis, older individuals have repeatedly experienced IgE antigens exposed when adult worms die (e.g., TAL3) and that this leads to increasing cross-reactivity with antigens of invading larvae (e.g., TAL5). Progressive accumulation of worm/larvae cross-reactivity could explain the age-dependent immunity observed in areas of endemicity.

Local renal autoantibody production in lupus nephritis.

Autoantibodies are central to the pathogenesis of several autoimmune diseases including systemic lupus erythematosus. Plasma cells secrete these autoantibodies, but the anatomical sites of these cells are not well defined. Here, we found that although dsDNA-specific plasma cells in NZB/W mice were present in spleen and bone marrow, a large number were in the kidneys and their number correlated with the serum dsDNA-IgG titer. We observed renal plasma cells only in mice with nephritis, where they located mainly to the tubulointerstitium of the cortex and outer medulla. These cells had the phenotypic characteristics of fully differentiated plasma cells and, similar to long-lived bone marrow plasma cells, they were not in cell cycle. In patients with lupus nephritis, plasma cells were often present in the medulla in those with the most severe disease, especially combined proliferative and membranous lupus nephritis. The identification of the kidney as a major site of autoreactive plasma cells has implications for our understanding of the pathogenesis of lupus nephritis and for strategies to deplete autoreactive plasma cells, a long-standing therapeutic aim.

Trypanosome IFT mutants provide insight into the motor location for mobility of the flagella connector and flagellar membrane formation.

The flagella connector (FC) of procyclic trypanosomes is a mobile, transmembrane junction important in providing cytotactic morphogenetic information to the daughter cell. Quantitative analyses of FC positioning along the old flagellum, involving direct observations and use of the MPM2 anti-phosphoprotein monoclonal reveals a ;stop point' is reached on the old flagellum which correlates well with the initiation of basal body migration and kinetoplast segregation. This demonstrates further complexities of the FC and its movement in morphogenetic events in trypanosomes than have hitherto been described. We used intraflagellar transport RNAi mutants to ablate the formation of a new flagellum. Intriguingly the FC could still move, indicating that a motor function beyond the new flagellum is sufficient to move it. When such a FC moves, it drags a sleeve of new flagellar membrane out of the flagellar pocket. This axoneme-less flagellar membrane maintains appropriate developmental relationships to the cell body including following the correct helical path and being connected to the internal cytoskeleton by macula adherens junctions. Movement of the FC in the apparent absence of intraflagellar transport raises the possibility of a new form of motility within a eukaryotic flagellum.