Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 10 appraisals
Molecular pharmaceutics
Platelets as Programmable Drug Depots: Toward Predictive and Mechanistic Drug Loading
Efficient drug delivery remains a major challenge in pharmaceutical science, with synthetic nanocarriers often facing limitations in real biological systems. While blood platelets have been explored as biomimetic carriers, their intrinsic capacity for molecular storage and stimulus-triggered release remains largely underutilized. Here, we propose that platelet granules can be viewed as programmable drug depots, whose loading can be rationally controlled using physicochemical principles and endogenous transport mechanisms. In contrast to empirical approaches, we outline a framework based on two complementary pathways: passive accumulation driven by ion trapping of weakly basic compounds in acidic granules, and active uptake mediated by vesicular transporters. Building on recent advances in structural biology and computational modeling, we further argue that drug loading into platelets can be predicted and optimized using molecular descriptors, docking approaches, and machine learning models to estimate transporter compatibility and accumulation efficiency. This perspective introduces a conceptual design loop linking prediction and molecular optimization─i.e., enabling both selection of suitable drug candidates and rational modification of their structures to enhance platelet compatibility. Together, these concepts position platelets not simply as biomimetic carriers but as model-guided and engineerable platforms for drug delivery, bridging biological systems with rational design strategies.
6 July 2026
Read appraisal →Annals of medicine
Association between packed red blood cell transfusion and clinical deterioration in neonatal necrotizing enterocolitis: a systematic review and meta-analysis
BACKGROUND: No systematic review has evaluated the existing evidence regarding the association between packed red blood cell (pRBC) transfusion and clinical worsening of necrotizing enterocolitis (NEC) in neonates. This systematic review and meta-analysis was conducted to address this knowledge gap. MATERIALS AND METHODS: We searched the Cochrane Library, EBSCO, Embase, Web of Science, Google Scholar, and PubMed for studies on pRBC transfusion and NEC published before May 10, 2025. Relevant articles were selected through title, abstract, and full-text screening. English-language case-control studies or cohort studies, or randomized controlled trials involving newborns with NEC that compared pRBC transfusion with no transfusion and reported changes in NEC clinical status were included. Review articles, systematic reviews, case reports, editorials, animal studies, duplicate publications, and studies with incomplete data were excluded. RESULTS: Five studies involving 971 neonates with NEC were included. The pooled analysis demonstrated a potential association between pRBC transfusion and clinical deterioration of NEC in neonates (odds ratio: 6.05, 95% confidence interval: 3.02-12.14). CONCLUSIONS: pRBC transfusion was associated with an exacerbation of NEC in neonates. However, these findings should be interpreted cautiously because of the small number of eligible studies included in this meta-analysis, and future large-scale, well-designed studies are needed to confirm the observed association. Published studies have identified an association between pRBC transfusion and NEC progression; however, whether this association is causal remains unclear.Despite consistent evidence from observational studies suggesting a potential association between pRBC transfusion and NEC progression, the overall quality of the evidence is low because of the lack of randomized controlled trials to determine causality.
25 May 2026
Read appraisal →Biomedical microdevices
Automated blood group classification using a digital microfluidics chip and vision transformer-based image analysis
Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen-antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.
21 May 2026
Read appraisal →JMIR medical informatics
Implementation and Evaluation of an Alternative Electronic Health Record Tool for Ordering Blood Products in Pediatric Oncology and Stem Cell Transplantation: Mixed Methods Analysis
BACKGROUND: Repeated blood product ordering is associated with order entry errors and potential patient harm. Traditional electronic health record order sets require repeated re-entry for recurrent transfusions, creating inefficiencies and opportunities for error, and contributing to physician burnout. Historically, we have used order sets to order blood products, which must be re-entered each time a transfusion is needed. Reusable transfusion therapy plans may address these challenges by standardizing and streamlining transfusion workflows. We conducted a pre-post study at a single pediatric academic center, evaluating the implementation of reusable transfusion therapy plans for packed red blood cells and platelets in oncology patients and those undergoing hematopoietic stem cell transplantation. OBJECTIVE: The primary outcome was to evaluate the proportion of transfusions originating from the transfusion therapy plans during the postimplementation period. Secondary outcomes included evaluating (1) the proportion of eligible patients with applied transfusion therapy plans, (2) changes in transfusion efficiency (time from laboratory result to transfusion release and administration, premedication timing, and estimated overnight pages), and (3) the impact on safety (guideline-concordant dosing, irradiated product ordering, and transfusion thresholds). We also assessed health care practitioner experience using an adaptation of the technology acceptance model survey. METHODS: The prestudy period consisted of the 1-year preimplementation, and the postperiod consisted of the 1-year post implementation. We used our institution's enterprise data warehouse (SickKids Enterprise-Wide Data in Azure Repository) to obtain demographic and transfusion details for all eligible patients. The adapted technology acceptance model survey was administered to eligible oncology clinicians. RESULTS: The preimplementation cohort had 558 unique patients who received a total of 2678 transfusions. The postimplementation cohort had 521 unique patients who received 2777 transfusions. During the postimplementation period, 59% of transfusion orders originated from a therapy plan, increasing to 71% in the final month. Compared with order sets, therapy plan-derived transfusions were released and administered significantly faster following laboratory results (P<.001). Guideline-concordant transfusion volumes increased significantly postimplementation for both packed red blood cells and platelets (P<.001), as did the ordering of irradiated blood products (P<.001). No differences were observed in pretransfusion hemoglobin or platelet thresholds between study periods. Use of therapy plans was associated with an average avoidance of 4 overnight blood product entries per night. Survey responses from nurses and providers demonstrated high perceived usefulness and ease of use, with 95% endorsing continued use. CONCLUSIONS: Reusable transfusion therapy plans improved efficiency, standardized safe ordering practices, and were highly acceptable to clinicians. This longitudinal, noninterruptive electronic health record intervention represents a scalable approach to supporting high-risk transfusion workflows in pediatric oncology.
17 May 2026
Read appraisal →The New England journal of medicine
Base Editing of HBG1 and HBG2 Promoters for Sickle Cell Disease
BACKGROUND: Sickle cell disease is characterized by chronic hemolytic anemia and recurrent severe vaso-occlusive crises. Ristoglogene autogetemcel (risto-cel) includes autologous CD34+ hematopoietic stem and progenitor cells that have been base-edited to target the HBG1 and HBG2 promoters and inhibit BCL11A binding without altering BCL11A expression, yielding a switch in hemoglobin production from sickle hemoglobin (HbS) to antisickling fetal hemoglobin (HbF). METHODS: In this phase 1-2 study, we enrolled patients 12 to 35 years of age with sickle cell disease who had had at least four severe vaso-occlusive crises in the 2 years before enrollment. After myeloablative conditioning with pharmacokinetically guided administration of busulfan, patients received a single infusion of risto-cel (at a dose of ≥3.0×106 viable CD34+ cells per kilogram of body weight). The primary efficacy end point was freedom from severe vaso-occlusive crises for 12 consecutive months, starting later than 60 days after the last red-cell transfusion. This interim analysis was unplanned; here, we describe safety, editing, engraftment, and hemoglobin production and the number of severe vaso-occlusive crises starting later than 60 days after the last red-cell transfusion. RESULTS: A total of 31 patients received risto-cel and were followed for a mean of 6.6 months (range, 0.3 to 20.4). A median of one cycle (range, one to five) was required for stem-cell collection. Neutrophil engraftment occurred at a median of 17.5 days, and platelet engraftment at a median of 19 days. One patient died from idiopathic pneumonia syndrome. All 31 patients had at least one adverse event, 27 (87%) had an adverse event of grade 3 or higher, and 12 (39%) had a serious adverse event. At 6 months, the mean fraction of on-target edited alleles in peripheral blood was 67.4%, the mean HbF as a fraction of total hemoglobin was more than 60%, and the HbS as a fraction of total hemoglobin was less than 40% (among 13 patients); these levels were maintained throughout follow-up. No investigator-reported severe vaso-occlusive crises occurred later than 60 days after the last red-cell transfusion. CONCLUSIONS: Treatment with risto-cel was followed by rapid engraftment and durable expression of HbF and reduction in HbS. These data support further investigation of risto-cel to treat sickle cell disease. (Funded by Beam Therapeutics; BEACON ClinicalTrials.gov number, NCT05456880.).
12 May 2026
Read appraisal →Blood
Long-term efficacy and safety results of betibeglogene autotemcel gene therapy for transfusion-dependent β-thalassemia.
Betibeglogene autotemcel (beti-cel) gene therapy for transfusion-dependent β-thalassemia (TDT) involves autologous transplantation of hematopoietic stem and progenitor cells transduced with a modified β-globin gene to produce functional adult hemoglobin (Hb) containing βA-T87Q-globin (HbAT87Q). Sixty-three participants with TDT (median age, 17 years [range, 4-35]) received beti-cel in phase 1/2 (n = 22) or phase 3 (n = 41) studies and enrolled in the long-term follow-up LTF-303 study (median follow-up, 5.9 years [range, 2.9-10.1]). Manufacturing refinements in phase 3 increased transduction efficiency, resulting in higher drug product vector copy number and HbAT87Q levels, which translated into higher Hb and transfusion independence (TI) rates than in phase 1/2. TI was achieved by 15 of 22 (68.2%) phase 1/2 participants (median weighted average Hb during TI, 10.2 g/dL) and 37 of 41 (90.2%) of phase 3 participants (median, 11.2 g/dL), and was sustained through last follow-up. Treatment efficacy was similar across ages and TDT genotypes. Among participants achieving TI, 38 of 52 (73%) had discontinued iron chelation at last follow-up, with no increase in liver iron concentration. Markers of ineffective erythropoiesis, including serum transferrin receptor and erythropoietin, improved with restoration of iron homeostasis. Health-related quality-of-life assessment scores showed durable improvements. No malignancies, insertional oncogenesis, or vector-derived replication-competent lentivirus were reported. These findings establish beti-cel as a durable, 1-time therapy that achieves TI, restores iron balance, and improves quality of life, offering a potentially curative treatment option for people with TDT. This trial was registered at www.clinicaltrials.gov as NCT02633943.
11 May 2026
Read appraisal →Annals of laboratory medicine
Evaluation of the Performance of Advanced Large Language Models in Laboratory Medicine Using Residency Examinations
BACKGROUND: Recent advancements in large language models (LLMs) have accelerated their integration into clinical domains, including laboratory medicine. The performance of LLMs in answering board-level laboratory medicine questions has not been comprehensively evaluated. Given the importance of diagnostic accuracy in this field, rigorous and objective evaluations of LLM capabilities are essential. METHODS: We assessed 12 LLMs from OpenAI, Anthropic, and Google using 320 Korean Residency Examination questions (2021-2024) spanning six laboratory medicine subspecialties. Standardized prompts were provided via their application programming interfaces under deterministic settings (temperature=0). Questions were administered thrice to assess response reproducibility. Outputs were compared with validated answers and analyzed for accuracy, reasoning quality, and error typology. RESULTS: Google's Gemini 2.0 Pro achieved the highest accuracy (80.0%), followed by OpenAI's GPT-4.5 (77.2%) and Anthropic's Claude 3.7 Sonnet (74.1%). Accuracy decreased as the difficulty of questions increased (78.0% for easy vs. 45.1% for challenging). Subspecialty performance varied. Al models underperformed on questions on transfusion medicine (mean accuracy: 38.8%), primarily because of limitations in domain-specific and regional knowledge representations. Incorrect answers primarily resulted from reasoning errors. Reproducibility exceeded 95% for most models; however, some residual non-determinism appeared even with greedy decoding (temperature=0). CONCLUSIONS: LLMs demonstrated substantial potential for integration into laboratory medicine, particularly in clinical chemistry and immunology. Performance inconsistencies (particularly for high-difficulty questions) and knowledge gaps (notably for transfusion medicine) highlight the necessity for further development-potentially including domain-specific fine-tuning and retrieval-augmented generation integration-and robust expert oversight before clinical application.
6 May 2026
Read appraisal →Platelets
Neural network reveals platelet age from fluorescence microscopy images
Platelets are small, anucleate cells with a primary physiological role in vascular damage repair (hemostasis) and initiation of thrombus formation in response to vascular injury. Platelets circulate approximately 7-10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. As older platelets are associated with poor clinical outcome, no in vitro tests are available to predict platelet age, or to determine the fitness of platelet transfusion products. In this study, we developed a convolutional neural network model that could determine platelets' chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with >97% accuracy. To test our model in vivo, we analyzed a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish in vivo between samples with younger and older platelets during the course of treatment. This study demonstrates the ability to predict platelets' chronological age both in vitro during storage and in vivo, which may impact clinical transfusion medicine and the diagnosis and treatment of patients with platelet disorders. What is the context?Platelets circulate approximately 7–10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. During storage, proinflammatory changes occur in platelets over time and platelet age may be associated with poorer clinical outcomes. Therefore, understanding platelet aging and developing novel tools to interrogate platelet age will be useful. However, no platelet tests are currently available that can predict platelet age accurately based on platelet characteristics without keeping record of the (storage) time.What is new?We developed a convolutional neural network model that could determine platelets’ chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with very high accuracy. Furthermore, our model was in vivo tested in a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish between in vivo samples with younger and older platelets during the course of treatment.What is the impact?Our study demonstrates the utility of a convolutional neural network model using confocal images of platelets to determine storage time-associated platelet phenotype in vitro as well as physiologic platelets’ chronological age in vivo. This model may be useful in several clinical settings, including clinical transfusion medicine, testing the quality of stored platelet concentrates, and diagnosing and treatment of patients with platelet disorders, thrombocytopenia, or thrombosis.
18 Apr 2026
Read appraisal →The New England journal of medicine
Ianalumab plus Eltrombopag in Immune Thrombocytopenia
BACKGROUND: Current second-line treatments for immune thrombocytopenia (ITP) require long-term administration. Ianalumab, a monoclonal antibody targeting B cells, is being assessed as a short-course second-line therapy in ITP. METHODS: In this phase 3, randomized, double-blind trial, we assigned, in a 1:1:1 ratio, adults with primary ITP and an insufficient response or a relapse after first-line glucocorticoid therapy to receive ianalumab at a dose of 9 mg or 3 mg per kilogram of body weight or placebo once monthly for 4 months. Eltrombopag, an oral thrombopoietin-receptor agonist, was administered once daily in each group according to local prescribing information; the dose was tapered until discontinuation by the end of week 24 in eligible patients. The primary end point was freedom from treatment failure, as determined in a time-to-event analysis, with treatment failure defined by a platelet count of less than 30×109 per liter more than 8 weeks after randomization, initiation of rescue therapy more than 8 weeks after randomization, initiation of new ITP therapy, inability to taper or discontinue eltrombopag because of an inadequate platelet count, or death from any cause, whichever occurred first. The key secondary end point was a stable response at 6 months, defined by a platelet count of at least 50×109 per liter in at least 75% of the measurements between weeks 19 and 25 without use of rescue therapy or new ITP therapy. Safety was assessed. RESULTS: A total of 152 patients underwent randomization: 50 to the 9-mg ianalumab group, 51 to the 3-mg ianalumab group, and 51 to the placebo group. The estimated probability of being free from treatment failure at 12 months was 54% (95% confidence interval [CI], 39 to 67) in the 9-mg group, 51% (95% CI, 36 to 64) in the 3-mg group, and 30% (95% CI, 18 to 43) in the placebo group. The time to treatment failure was significantly longer with ianalumab plus eltrombopag than with placebo plus eltrombopag; the estimated hazard ratio for treatment failure (ianalumab vs. placebo) was 0.55 (P = 0.04) in the 9-mg group and 0.58 (P = 0.045) in the 3-mg group. The percentage of patients with a stable response at 6 months was significantly higher in the 9-mg group than in the placebo group (62% vs. 39%; P = 0.045). The overall frequency of adverse events during the treatment period was generally similar in the three groups. The frequency of serious adverse events was 16% in the 9-mg group, 6% in the 3-mg group, and 4% in the placebo group. CONCLUSIONS: Ianalumab plus eltrombopag led to a longer time to treatment failure than placebo plus eltrombopag. (Funded by Novartis; VAYHIT2 ClinicalTrials.gov number, NCT05653219.).
16 Apr 2026
Read appraisal →Hematology (Amsterdam, Netherlands)
Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment
OBJECTIVES: Iron chelation therapy titration is managed by hematologists who monitor iron levels and adjust medication dosages to achieve patient outcomes. This study developed a machine learning (ML) algorithm to predict the liver iron concentration (LIC) and chelation therapy adjustments. METHODS: This retrospective, single-centre cohort study included adult patients who underwent FerriScan MRI to obtain LIC for chelation therapy monitoring. Using an XgBoost-based ML framework, the proximal-time model (PTM) utilised clinical/drug, laboratory and MRI data features from one visit prior to the target visit, whereas the all-time model (ATM) utilised the data from all prior visits. RESULTS: 94 patients with 892 consecutive visits between January 2008 and November 2018 were included in this study. We assessed the prediction capabilities of the PTM and ATM in LIC, changes to chelation drug type and dosage changes. The PTM model was superior to the ATM model in all the experiments. When drug features were excluded, the CLICT model for predicting patient iron overload status improved to an AUROC of 0.83 [95% CI 0.75-0.91] for PTM; compared to an AUROC of 0.73 [95% CI 0.66-0.80] when drug features were included.For predicting changes in chelation type, the CLICT model showed AUROC of 0.83 [95% CIs 0.77-0.89] for PTM. There is high concordance of the agreement of hematologists with ML in not changing the chelation drug type. DISCUSSION/CONCLUSION: The ML model is a step toward creating a clinical decision support system tool for the prediction of LIC and iron chelation therapy adjustment in patients with haemoglobinopathies or hemolytic anemias.
5 Apr 2026
Read appraisal →