Citations

Abstract Background The use of autonomous mobile robots (AMRs) to move materials/supplies is long-standing in manufacturing/warehouse settings and is expanding into hospitals and clinical laboratories. The recently launched Proxie robot (Collaborative Robotics i.e. Cobot) is an AMR that can autonomously move existing carts between defined locations. However, AMRs pose new challenges compared to traditional lab automation. The aims of this study were to: i) perform functional validation to verify Proxie*s manufacturer performance specifications within the laboratory, ii) assess the risk of using Proxie to deliver laboratory specimens, and iii) pilot the use of the Proxie robot to autonomously deliver loaded carts from the Pathology case intake area to the sorting station at Mayo Clinic Laboratories. Methods Functional validation of Proxie consisted of verifying manufacturer’s performance specifications for the following robot parameters (5 test runs): maximum speed, emergency-stop, and ability to maintain localization, travel autonomously, stop and/or avoid moving/fixed objects, and park/unpark. Risk assessment for the robotic use case was performed by a multi-disciplinary team utilizing an AMR risk assessment tool developed by the institutional robotics group. Hazards were categorized as mechanical, electrical, noise, ergonomic, environmental, material/substance or combination risks. Injury severity, exposure(frequency), and avoidance scores were assigned to each hazard to determine negligible-, low-, medium-, high- or very high-risk levels. Proxie’s workstream was evaluated weekly over four weeks by measuring number of cart deliveries, total time/miles traveled, % autonomous deliveries(goal >80%), % on-time deliveries, average±standard deviation(SD) time (min:sec±min:sec) for delivery/return trips, and dwell times at intake/sorting stations. Results Manufacturer’s performance specifications were verified in 100% of test runs. Proxie traveled at human walking speed, stopped within 1 second of emergency stop being enabled, maintained localization, and traveled autonomously for =5 consecutive workflow cycles. Proxie navigated through spaces =4’ wide along fixed pathways and stopped safely when encountering obstacles. Proxie navigated around people and carts in the main 6’ hallway if they were blocking <2’ hallway width. For objects blocking >2’, Proxie stopped and resumed motion after the object moved. After task completion, Proxie side-shifted into the AMR parking space stopping no closer than 7.8” from any object. Twenty-four potential hazards were identified through the risk assessment tool with all risk levels scored as negligible or low. Proxie completed 304 cart deliveries/returns operating 17:35 hours and traveling 11.5 miles during the pilot. The % autonomous deliveries for week 1-4 was 82%,88%,91% and 88%, respectively with =99% on-time deliveries. Delivery from pathology case intake to sorting station averaged 5:43±0:25 min and return trips averaged 3:32±0:34. Proxie dwell times were 13:10±0:33 and 37:34±0:23 min at the pathology intake and sorting stations, respectively. Conclusions The Proxie AMR was capable of successfully and autonomously moving carts with laboratory specimens between the pathology intake and sorting stations of the laboratory during the pilot study. Adding Proxie to this workstream translated to eliminating 5,800 steps and 4 hours 24 min of human cart-pushing per week. Incorporating Proxie into laboratory operations as a cart-moving AMR would increase efficiency and allow laboratory staff to focus on technical, non-repetitive work.

Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Error analysis is a critical step in evaluating and improving clinical concept extraction models, the most common clinical natural language processing (NLP) task. Unlike corpus annotation, which follows standardized protocols for creating gold-standard datasets, error analysis requires nuanced judgment grounded in both clinical expertise and NLP knowledge. This is especially important given the heterogeneity of clinical text, where variations in documentation style, note structure, and terminology can substantially influence model behavior. Despite its importance, there is currently no standardized, user-level framework to support systematic error analysis in clinical concept extraction task. In this study, we developed and validated MedError, a machine-assisted, human-in-the-loop framework designed to standardize and enhance error analysis for clinical concept extraction tasks. We collected and manually curated a corpus of 1,187 unique errors from a total of 4,237 notes across three different distinct hospitals. The error categories were defined using our previously validated error taxonomy and included 480 false negatives and 707 false positives across 25 error types and 48 clinical concept categories. We evaluated the performance of three proprietary and three open-source large language models (LLMs) in automatically classifying these errors into 26 and 15 predefined categories. We further developed a machine-assisted framework, MedError, which integrates best practices in error analysis, LLM-assisted classification and reasoning, and a user-friendly interface to enable more efficient, reproducible, and context-aware error analysis. The framework supports both single-site and federated multisite error analysis, facilitating the effective translation of clinical NLP systems into real-world settings.

Abstract Frailty is highly prevalent in cardiovascular disease and associates with poor clinical outcomes. Several cardiovascular conditions benefit from invasive interventions, but the impact of these procedures in frail patients remains unclear. We thus performed a systematic review and meta-analysis to summarize evidence and quantify the pooled effect of clinical outcomes of invasive cardiovascular interventions among frail vs. non-frail patients. Systematic search of two electronic databases from January 2015 to April 2025 was performed including all randomized controlled trials (RCTs) and prospective cohort studies examining outcomes of invasive cardiovascular interventions stratified by frailty. Outcomes included all-cause mortality, hospitalizations, and other adverse outcomes. Risk ratios (RRs) and their 95% confidence intervals (CI) were assessed using random-effects models. Thirty-one studies (10 RCTs, 21 prospective, 3 retrospective studies) with a total of 27 534 participants were included. Compared with non-frail patients, frail patients after cardiovascular procedures were at higher risks of all-cause mortality (RR 3.14, 95% CI: 2.29-4.30), and hospitalizations (RR 1.88, 95% CI: 1.29-2.73). Frail patients undergoing transcatheter aortic-valve implantation and percutaneous mitral-valve interventions did not have significant increased mortality risks compared with non-frail patients (P > 0.05). Pre-frailty predicted lower mortality risks amongst patients undergoing transcatheter vs. surgical aortic valve replacement (HR 0.78, 95% CI: 0.67-0.92). Frail patients with non-ST elevation myocardial infarction derived similar mortality benefits from an invasive vs. conservative approach (HR 1.13, 95% CI: 0.64-2.01). In conclusion, frailty prognosticates adverse outcomes following invasive cardiovascular interventions and influences treatment efficacy, highlighting its significance in treatment selection

Background: The impact of multiple substance use on the risk of pancreatitis remains underexplored. Objective: To systematically review peer-reviewed observational studies assessing the association of multiple substance use with the risk of acute pancreatitis (AP) or chronic pancreatitis (CP) in adults. Design: We conducted a systematic review informed by the Preferred Reporting Items for Systematic Review and Meta-Analyses guideline. Data sources and methods: EMBASE, MEDLINE, and PsycINFO were searched up to March 2024. Reference lists of included studies were reviewed. From 5205 records identified, 181 relevant records were evaluated in full text. Studies evaluating the association of ⩾2 substances, including tobacco, alcohol, cannabis, and illicit substances, with AP or CP were included. Data were extracted by one reviewer, with quality control by a second reviewer. Quality assessments were independently conducted by two reviewers, with differences resolved by a third. Results: Of 11 included studies, 6 investigated AP as the outcome and 5 examined CP. Among AP studies, 5 comparing smoking and alcohol to alcohol-only use showed high heterogeneity ( I 2 = 90.9%), with relative risks (RRs) from 1.40 to 11.40. One study examining cannabis and alcohol versus alcohol found a lower risk of AP in cannabis users. Among CP studies, four comparing smoking and alcohol to alcohol-only use were heterogeneous ( I 2 = 81%) with odds ratios 1.21–31.50. Where examined, smoking increases the risk of AP and CP in a dose-dependent fashion. Heavy alcohol users demonstrated a significant increase in CP risk across all smoking categories in one study. Conclusion: Combined alcohol and tobacco use increases pancreatitis risk compared to single substance use, despite heterogeneity in RRs and exposure definitions. Evidence suggests a dose-dependent impact of smoking on pancreatitis risk when added to alcohol. Studies on the impact of a combination of other substance use on pancreatitis risk are needed. Trial registration (PROSPERO): CRD42024503677.

Abstract Introduction Alice in Wonderland Syndrome (AIWS) is an uncommon condition manifesting visual and somatosensory disturbances in which patients may perceive objects, including their own bodies, as larger or smaller than they are. In adults, AIWS typically is associated with migraine and epilepsy but has been reported as an adverse effect of topiramate, which is used to treat both illnesses, making it difficult to ascertain whether illness or medication causes altered perceptions. Reported is a case of topiramate-associated AIWS, which adds to the limited reports published on this unique phenomenon. Case Report A 40-year-old woman admitted to a 3-week pain rehabilitation program to treat chronic migraine reported macrosomatognosia that she retrospectively recalled beginning during titration of topiramate. The symptoms of AIWS resolved after the topiramate was discontinued. An assessment with the Naranjo ADR Probability Scale indicated a possible association between topiramate and macrosomatognosia. Discussion In this patient with migraine, the onset of AIWS coincided with titration of topiramate, providing a clue that its emergence may have been an adverse drug reaction rather than the underlying illness. Consistent with previous reports, AIWS resolved shortly after topiramate was discontinued, lending credence to this conclusion. However, the mechanism of AIWS is unknown and may be multifactorial, leaving the possibility that migraine predisposed the patient to develop AIWS from topiramate. Conclusion The long association of AIWS with migraine and epilepsy makes it possible that cases caused by medications used to treat these illnesses, like topiramate, are underreported. Awareness of the temporal relationships between the course of illness and the timing of drug therapy can help guide the recognition of AIWS as a potential adverse event.

Acquired angioedema due to C1-inhibitor deficiency (AAE-C1INH) is a rare condition characterized by the localized swelling of the deeper skin layers and mucous membranes, especially the face, lips, tongue, throat, and gastrointestinal tract. AAE-C1INH is strongly associated with lymphoproliferative disorders, although it can also be linked to autoimmune conditions, solid tumors, infections, or even occur without an identifiable cause. We present the case of a 45-year-old female patient with complaints of recurrent abdominal pain, bloating, and joint swelling. Laboratory testing showed decreased C1q and C4 complement levels, and C1 esterase inhibitor levels, indicative of AAE-C1INH. Further work up confirmed a diagnosis of extranodal marginal zone lymphoma with involvement of the bone marrow and spleen. Treatment with rituximab led to resolution of angioedema symptoms and almost complete remission of underlying lymphoma. This case underscores the importance of evaluating an underlying lymphoproliferative disorder in AAE-C1INH. Therefore, the early participation of a multidisciplinary team including specialists in immunology, hematology, and oncology is necessary for appropriate management.