Knowledge assessment of Tunisian junior doctors in transfusion medicine

BACKGROUND

Although transfusion medicine (TM) is included in medical curricula, junior doctors still have gaps in practical knowledge. These gaps with limited training increase patient risks, as well as health care costs. The aim of this study is to assess TM knowledge among juniors' doctors.

METHODS

This is a monocentric study, conducted across nine clinical departments. A structured questionnaire was used to assess four TM concepts. Knowledge scores were analyzed according to experience, prior training, and department.

RESULTS

Of the 102 doctors surveyed, 82 (80%) responded. The median age of participants was 27 years old. The percentage of residents was 82%. Among them, 41% had prior and only 10% had more than three years of experience. The average knowledge score was 32.4%, with 41.5% scoring above the mean. Most interns and residents scored in the poor or moderate knowledge range, while the good knowledge group consisted exclusively of residents. Scores were highest for pre-transfusion initiation and monitoring, and lowest for basic knowledge and risk management. Residents generally outperformed interns, with knowledge correlating positively with clinical experience.

CONCLUSION

Experience alone does not ensure competency. Enhanced curricula with standardized and innovative tools are recommended to reduce transfusion errors and improve patient safety.

Quality Improvement Initiative to Implement Anxiety Screening for Children and Teens With Headache and Epilepsy

Background and Objectives

We conducted a quality improvement initiative to implement standardized screening for anxiety among adolescents with headache and/or epilepsy receiving outpatient neurology care at a quaternary health care system, consistent with recommendations from the American Academy of Neurology. Our SMART (Specific, Measurable, Achievable, Relevant, and Time-Based) aim was to screen ≥90% of established patients aged 12 years or older seen by a participating health care professional using a standardized anxiety screener by February 2024.

Methods

This initiative was conducted in patients seen for follow-up by 17 participating neurology health care professionals. Health care professional opinions were assessed before and after implementation of the Generalized Anxiety Disorder-7 (GAD-7), administered as a previsit questionnaire distributed using the electronic health record. The integrated workflow included a best practice advisory (BPA) alert that permitted easy access to interventions and automatic population of education materials into the after-visit summary. After 12 months of use (March 2023 to February 2024), we assessed demographic and diagnostic information, GAD-7 completion rates, anxiety symptom severity, BPA utilization, and health care professional acceptance of the intervention.

Results

The GAD-7 was completed for 64% of 3,671 encounters and by 71% of 2031 unique patients. The GAD-7 was more often completed for encounters if the patient was female, younger, or White or had a headache diagnosis. Among unique patients, anxiety symptoms were minimal in 50%, mild in 24%, moderate in 17%, and severe in 10%. Severe anxiety symptoms were more often present in female patients or those with a headache diagnosis. Among patients with severe anxiety symptoms, 66% had established behavioral health care plans and, for remaining patients, referrals were made to community behavioral health care professionals (11%), or pediatric psychologists (4%) or social workers (3%) within neurology. Clinicians indicated that the approach was easy to use and improved the quality of patient care.

Discussion

We implemented standardized EHR-based screening for anxiety symptoms for pediatric neurology patients, most of whom had headache or epilepsy. Screening was feasible, and approximately one-quarter of patients had moderate or severe anxiety symptoms. Future work will focus on improving completion rates of previsit questionnaires including the GAD-7 and optimizing clinician actions based on the screening data.

Burkitt's lymphoma presenting as acute dacryocystitis in a 14-year-Old male with ataxia telangiectasia: A case report and review of the literature

Purpose

This report describes an atypical presentation of a case of Burkitt lymphoma in a pediatric patient with ataxia telangiectasia.

Main observations

A 14-year-old boy with a history of AT was referred to our hospital with lower eyelid swelling and medial canthus abscess. On physical examination, movement of the left eye was limited in downgaze and adduction. Two mm proptosis was observed on the left side. Snellen's Visual acuity in the left eye was 8/20. The rest of the examination was normal. Magnetic resonance imaging (MRI) was performed and showed an infiltrative mass in the inferior of the left orbit, left ethmoidal sinus, and maxillary sinus. The patient subsequently underwent an incisional biopsy. The biopsy specimen was sent for histopathologic evaluation. Histopathology was significant for atypical monotonous cell infiltrations in the fibroconnective tissue and the presence of pleomorphic, irregularly shaped nuclei with multiple mitoses. Immunohistochemistry (IHC) findings were consistent with Burkitt's lymphoma, and the patient was referred to the oncology department for chemotherapy and appropriate treatment.

Conclusion

and SignificanceOrbital and lacrimal duct involvement is a rare presentation of Burkitt lymphoma especially in the pediatric population. In the new-onset nasolacrimal duct obstruction (NLDO) in a child or teenager, the differential diagnosis should include malignancies, especially leukemia/lymphoma infiltrations.

A Typology of US Public Health Work-Education Programs

OBJECTIVE

A pipeline is required to build a qualified and diverse public health workforce. Work-education programs offer public health students experiential learning, training, and a pathway to public health professions. However, there is a gap in the literature to guide public health practice on the types of programs, their components, and their potential impact. By defining and differentiating work-education programs, we aim to provide public health practitioners a useful tool to advance public health careers.

DESIGN

We conducted a scoping review of US-based public health work-education programs and performed a content analysis to extract, analyze, and synthesize data to develop a typology of programs in the US. We searched CINAHL, ERIC, PsycInfo, PubMed and Web of Science databases for papers published between January 1, 2012, and December 31, 2022.

ELIGIBILITY CRITERIA

Inclusion criteria included programs with a description, intervention design, student characteristics, setting characteristics, and program outcomes if available. Exclusion criteria included programs with no description, that focused on the expansion of a specific clinical profession (ie, dentistry), that were implemented outside of the US, and that targeted exclusively high school students.

STUDY SELECTION

The search resulted in 650 references, which yielded 35 articles from 33 interventions.

MAIN OUTCOME MEASURES

The main outcome measures were qualitative and included program design, features, and implementation.

RESULTS

We identified 3 types of programs: those that (1) expose students to public health (n = 6), (2) guide students to specific public health professions (n = 12), and (3) connect students with organizations for public health practice (n = 15). Program types were influenced by student participants, setting, and program components.

CONCLUSIONS

This typology illustrates the spectrum of work-education programs and their variability in design and implementation. The typology has utility for practitioners to identify programmatic aspects that may be feasible and desirable in the context of their practice.

Insights from autopsy-initiated pathological studies of the pathogenesis and clinical manifestations of atherosclerosis and ischemic heart disease: Part II. Ischemic heart disease

CONTEXT

Ischemic heart disease (IHD) due to coronary atherosclerosis constitutes the leading cause of morbidity and mortality worldwide. This review was undertaken to retrospectively analyze the lines of research that generated the evidence for our contemporary understanding of atherosclerosis-based coronary artery disease and to provide a rationale for continued support for autopsy-based research in order to make further progress in reduction of the morbidity and mortaility from IHD.

OBJECTIVES

To analyze the contributions of the autopsy to complement and validate other lines of investigation in determining the complex interactions between coronary artery alterations linked to the major manifestations of coronary atherosclerosis, namely, coronary thrombosis, acute myocardial infarction, and sudden cardiac death.

DATA SOURCES

Systematic search on PubMed to gather relevant studies concerning autopsy studies and reviews of the pathology and pathogenesis of atherosclerosis, ischemic heart disease, coronary atherosclerosis, coronary thrombosis, myocardial infarction and sudden cardiac death.

CONCLUSIONS

An extensive search of the published literature has confirmed the continuing importance of the autopsy as a powerful tool to understand the pathogenesis, clinical features, and therapeutic options for the treatment of atherosclerosis and its major manifestation, ischemic heart disease. This has been described in the Part I companion of the present review. Autopsy-initiated studies have documented the prevalence and clinicopathological significance of atherosclerosis in different human populations and its relationship to risk factors. It has been shown that the clinically silent phase of ischemic heart disease (IHD) begins in the first decades of life. Pathological studies have clarified the complex relationship between coronary atherosclerosis, coronary thrombosis, and myocardial ischemic events. These studies also have elucidated the pathological basis of sudden cardiac death. Insights from these studies also have been important in developing and evaluating strategies for continued progress in reducing the morbidity and mortality attributed to atherosclerosis and IHD.

Can ChatGPT Generate Acceptable Case-Based Multiple-Choice Questions for Medical School Anatomy Exams? A Pilot Study on Item Difficulty and Discrimination

Developing high-quality multiple-choice questions (MCQs) for medical school exams is effortful and time-consuming. In this study, we investigated the ability of ChatGPT to generate case-based anatomy MCQs with acceptable levels of item difficulty and discrimination for medical school exams. We used ChatGPT to generate case-based anatomy MCQs for an endocrine and urogenital system exam based on a framework for artificial intelligence (AI)-assisted item generation. The questions were evaluated by experts, approved by the department, and administered to 502 second-year medical students (372 Turkish-language, 130 English-language). The items were analyzed to determine the discrimination and difficulty indices. The item discrimination indices ranged from 0.29 to 0.54, indicating acceptable differentiation between high- and low-performing students. All items in Turkish (six out of six) and five out of six in English met the higher discrimination threshold (≥ 0.30) required for large-scale standardized tests. The item difficulty indices ranged from 0.41 to 0.89, most items falling within the moderate difficulty range (0.20-0.80). Therefore, it was concluded that ChatGPT can generate case-based anatomy MCQs with acceptable psychometric properties, offering a promising tool for medical educators. However, human expertise remains crucial for reviewing and refining AI-generated assessment items. Future research should explore AI-generated MCQs across various anatomy topics and investigate different AI models for question generation.

Insights from autopsy-initiated pathological studies of the pathogenesis and clinical manifestations of atherosclerosis and ischemic heart disease: Part I. Atherosclerosis

CONTEXT

Ischemic heart disease (IHD) due to coronary atherosclerosis constitutes the leading cause of morbidity and mortality worldwide. This review was undertaken to document the historical basis for our contemporary understanding of atherosclerosis-based disease and to provide a rationale for continued support for autopsy-based research to make further progress in reducing the morbidity and mortality from atherosclerosis-related disease.

OBJECTIVES

To analyze the contributions of the autopsy-initiated pathological studies to complement and validate other lines of investigation in determining the pathology and pathogenesis of the leading worldwide cause of morbidity and mortality, namely, atherosclerosis and its major complications of coronary atherosclerosis, ischemic heart disease, coronary thrombosis, acute myocardial infarction, and sudden cardiac death.

DATA SOURCES

Systematic search on PubMed to gather relevant studies concerning autopsy studies and reviews of the pathology and pathogenesis of atherosclerosis, ischemic heart disease, coronary atherosclerosis, coronary thrombosis, myocardial infarction, and sudden cardiac death CONCLUSIONS: Extensive published reports have confirmed the continuing importance of the autopsy as a powerful tool to understand the pathogenesis, clinical features, and therapeutic options for major diseases. This specifically has been shown by the analysis of atherosclerosis and its major manifestation of ischemic heart disease, as presented in this (Part I) and its companion (Part II) review. Autopsy-initiated pathological studies have documented the prevalence and natural history of atherosclerosis in different human populations in relationship to the prevalence of risk factors and established that the clinically silent phase of the disease begins in the first decades of life. Insights from these studies have been essential in developing and evaluating strategies for continued progress in preventing and controlling the disability and death associated with atherosclerotic heart disease.

Gender and Pathways to Leadership in Academic Anesthesiology: A Qualitative Content Analysis of US Chairpersons' Curricula Vitae

BACKGROUND

Women are underrepresented in medicine and academic anesthesiology, and especially in leadership positions. We sought to characterize career achievement milestones of female versus male academic anesthesiology chairs to understand possible gender-related differences in pathways to leadership.

METHODS

We conducted a retrospective observational cross-sectional analysis. In November 2019, curricula vitae (CVs) were requested from then-current members of the US Association of Academic Anesthesiology Chairs. Data reflecting accomplishments up to the time of chair appointment were systematically extracted from CVs and analyzed using a mixed methods approach with qualitative content analysis supplemented by descriptive statistics and bivariate statistical testing. Missing data were not imputed.

RESULTS

Seventy-two CVs were received from eligible individuals (response rate 67.3%). The respondent sample was 12.5% women (n = 9), 87.5% men (n = 63), and no transgender or nonbinary people; this is similar to the known gender balance in anesthesiology chairs in the United States. No statistically significant differences in objective markers of academic achievement at the time of chair appointment were evident for female versus male chairs, including time elapsed between the first faculty appointment and assumption of the chair role (median 25 vs 18 years, P = .06), number of publications at the time the chair was assumed (101 vs 69, P = .28), or proportion who had ever held a National Institutes of Health (NIH) grant as principal investigator (44.4% vs 25.4%, 0.25). Four phenotypes of career paths were discernible in the data: the clinician-administrator, the educator, the investigator, and the well-rounded scholar; these did not differ by gender.

CONCLUSIONS

Female chairpersons who were members of the Association of Academic Anesthesiology Chairs in the United States demonstrated similar patterns of academic achievement as compared to male chairpersons at the time the position of chair was assumed, suggesting that they were equally qualified for the role as compared to men. Four patterns of career achievements were evident in the chairperson group, suggesting multiple viable pathways to this leadership position.

Professionalism skills education in medical physics residency: Current state and perceived importance

PURPOSE

The purpose of this study was to collect data on current practices for teaching and assessing professionalism in CAMPEP-accredited residency programs.

METHODS

A survey of 21 questions was sent to 160 program directors (PDs) of CAMPEP-accredited residency programs. A list of professionalism skills was compiled from the AAPM MPLA curriculum: (a) Personal and interpersonal, (b) professional and developmental, and (c) executive and administrative. The survey collected information on: (1) residency program respondent demographics, (2) essential professionalism skills and training methods, (3) confidence and satisfaction in teaching professionalism, (4) assessment of professionalism, (5) barriers and desired resources, (6) training of PD and staff in professionalism, and (7) free response. Descriptive statistics and thematic analyses were used to evaluate the collected data.

RESULTS

A total of 97 respondents completed the survey (therapy = 75, diagnostic = 22) with a 61% response rate. 16 out of 24 professionalism skills were deemed essential for trainees to develop during residency training. While 92% teach professionalism, only 51% reported confidence in teaching these skills. The ABR/ACR/RSNA/AAPM/ASTRO/ARR/ARS online modules are used by 87% but only 31% indicated that the modules are sufficient. Only 10% use a structured method for assessment. The majority (59%) assess professionalism in an ad-hoc manner and 22% only assess when problems arise. 44% reported facing barriers to implementing a professionalism curriculum. The main barriers for developing a professionalism curriculum included: lack of time (39%), resources (32%), or expertise (26%). 79% reported that case studies were the most desired resource. 47% of respondents indicated receiving formal professionalism training.

CONCLUSIONS

There is a strong need and desire for structured professionalism training in residency programs. This study presents a consensus understanding of the professionalism skills that are deemed essential and teachable during residency and has identified areas for improvement in teaching, assessing, and developing curricula.

Lessons in leadership: Developing a longitudinal evidence-based teaching curriculum on professionalism in healthcare

Given the responsibility of healthcare organizations to promote positive workplace cultures, the development of appropriate teaching material focused on professionalism is of relevance. A longitudinal evidence-based educational curriculum was thus constructed to equip participants with tools to enhance team-based care and to create an inclusive, respectful environment. Core themes on which to center the curriculum were identified based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement. A MEDLINE search was undertaken to identify original peer-reviewed works using terms associated with professionalism in healthcare with the goal of building a foundational basis. Articles published from January 2014 to January 2024 and restricted to the English language were included. Based on the search results, a 12-month curriculum designed to promote workforce engagement and discussion was established. The 537 peer-reviewed publications selected to develop this thematic framework were broadly categorized as follows: ethics/accountability (N = 131); conflict resolution (N = 120); collaboration (N = 107); interpersonal communication (N = 70); empathy (N = 57); and wellness (N = 52). Between November 2023 and November 2024, a total of 12 sessions were scheduled. The feasibility of developing a standardized, evidence-based curriculum on workplace professionalism was demonstrated. The practical implications are discussed.

Modifying phrases in veterinary pathology diagnostic reports: The veterinary professionals' perception on the diagnostic confidence

Pathologists use modifying phrases (MPs) to express diagnostic confidence (DC) in the diagnosis when essential features of a lesion are lacking. Although commonly used, a standardized application and shared definitions of MPs are not recognized, resulting in a discretional use in veterinary pathology reports. Through an anonymous online survey, this study explored how different groups (veterinary pathologists, general practitioners, specialists of other specialties, residents, and students) perceive selected MPs used in veterinary pathology reports, evaluated their impact on the clinical decision, and provided a possible indication on their use. Participants (n = 753) responded from various continents. The highest DC was generally perceived for the term "diagnostic of," in all groups. "Compatible with," "indicative of," and "consistent with" were also generally perceived with high DC. The DC was lower for the other MPs ("suggestive of," "suspicious for," and "cannot rule out"). MPs perceived as having the highest DC were more often interpreted as definitive diagnoses, while those with lower DC were more likely perceived as suggestions to perform additional diagnostic tests. Pathology reports may benefit from using the comment section to further clarify the level of certainty of the diagnosis. Limitations in the data's representativeness arise from the non-probabilistic sampling and a predominance of a single nation's participants. Despite this, our study provides valuable insights into the perception and clinical impact of MPs, providing a foundation for discussions aimed in standardizing their use in veterinary pathology reports. Adopting consistent and uniform MPs could improve communication between pathologists and clinicians and patient care.

A breakdown of how diagnostic radiology residency became increasingly competitive for US doctors of osteopathic medicine (DOs) and international medical graduates (IMGs)

Diagnostic Radiology has emerged as an increasingly competitive specialty posing a significant challenge for aspirants, particularly for Doctors of Osteopathic Medicine (DOs) and International Medical Graduates (IMGs). This could be attributed to the field's dynamic nature, flexibility of career paths, and high job demand. This article delves into a decade's worth of matching trends in diagnostic radiology, underscoring the unique obstacles faced by DOs and IMGs including possible implicit biases, logistical hurdles, and the implications of the USMLE Step 1's transition to pass/fail scoring. It offers practical solutions to level the playing field, such as expanding clinical and research opportunities for applicants, encouraging residency programs to address implicit biases, increasing curriculum adaptability in osteopathic and foreign medical schools, and exploring accreditation reforms. Together, these recommendations aim to create a more equitable selection process and mitigate the systemic barriers DOs and IMGs face in securing highly sought-after radiology residency spots.

Accre 8 emerging point of care CLIA system for vitamin B12 assessment compared with three established assays

Accurate Vitamin B12 (Vit B12) quantification is essential for diagnosing deficiencies linked to neurological and hematological disorders. The Accre 8 Point-of-Care (POC) Chemiluminescent Immunoassay (CLIA) system offers a compact design, rapid single-step operation, and minimal calibration requirements. This study evaluates Accre 8's performance against established CLIA immunoassays (Abbott and Roche) and LC-MS/MS, the gold standard for Vit B12 quantification. A total of 297 serum samples, spanning deficient to sufficient Vit B12 levels, were analyzed. Accre 8 demonstrated a strong correlation with LC-MS/MS (r = 0.94, p < 0.001), with median Vit B12 levels closely aligning with LC-MS/MS (256.0 pmol/L). Accre 8 exhibited high sensitivity (96.9%) and specificity (86.7%), with Cohen's Kappa agreement (0.76). Bland-Altman analysis showed a mean bias of - 18.5%, while Passing-Bablok regression indicated proportional bias at higher concentrations (slope = 1.44). ROC analysis confirmed excellent diagnostic accuracy (AUC = 0.98). Accre 8's strong diagnostic performance, minimal calibration needs, and low sample volume requirements position it as a practical alternative to conventional CLIA systems for Vit B12 assessment, particularly in clinical and resource-limited settings. These findings support its potential integration into routine diagnostic workflows for Vit B12 deficiency screening and monitoring.

A multi-institutional multi-methods analysis of jeopardy systems in academic hospital medicine

BACKGROUND

Hospital medicine programs use backup ("jeopardy") systems to cover unexpected staffing gaps, but little is known about their structures or optimal practices.

OBJECTIVES

To describe jeopardy structures, assess clinician perceptions, and identify potential approaches across a broad sample of hospital medicine groups.

METHODS

This multi-methods study, conducted within a national hospitalist consortium, used virtual focus groups and an email survey to (1) describe the presence and structure of jeopardy systems and (2) explore features perceived as fair, equitable, and tolerable. Rapid qualitative analysis identified major themes, while descriptive methods analyzed survey data.

RESULTS

Twenty-five individuals participated in focus groups, and 26 completed the survey, representing 31 unique institutions. Participants were primarily physicians in academic hospital medicine groups. Three themes emerged: (1) jeopardy systems are widely used but vary in structure, activation criteria, and compensation, leading to inconsistencies in clinician experiences; (2) many clinicians report stress and dissatisfaction due to unpredictability, perceived inequities in assignment, and concerns about inappropriate use; and (3) strategies to improve fairness, equity and tolerability include structured scheduling, support for sick days, and compensation for the burden of jeopardy coverage. Survey data confirmed high variability in jeopardy systems across institutions. Common practices included jeopardy activation over redistributing patients and compensating clinicians for covered shifts.

CONCLUSIONS

Jeopardy systems are essential for hospital medicine staffing but contribute to clinician dissatisfaction due to unpredictability and perceived inequities in coverage. Implementing deliberate scheduling, formalized support for absences, and equitable compensation models may reduce dissatisfaction and improve jeopardy system sustainability.

Adrenal Gland Pathology Reporting Among Genitourinary Pathologists: An Orphan Field Handled by Foster Pathologists?

Due to their association with the kidney, adrenal glands are frequently resected by urologists and evaluated by genitourinary (GU) pathologists. However, given the growing complexity of adrenal pathology and advent of a dedicated "endocrine pathology" subspecialty, herein we sought to assess the sentiment regarding adrenal pathology among GU pathologists. One hundred twenty-eight pathologists who handle GU specimens participated in a survey including both junior (40% < 10 years in practice) and experienced pathologists (60% > 11 years in practice), who work in academic (75%) or private practice settings (25%). Participants reported "on the job" adrenal pathology training (61%) and/or formal training during GU fellowship (36%). While participants felt mainly "comfortable" (36%) or "neutral" (29%) reporting adrenal specimens, some felt "uncomfortable" (15%) or "very uncomfortable" (5%). Most reported that adrenal specimens are handled by GU pathology (56%) versus general surgical pathology (26%) or endocrine pathology (22%; although only 30% reported having formal endocrine pathologists). However, when the participants were asked who they felt should be handling adrenal specimens, participants most strongly endorsed either endocrine pathology (74%) or GU pathology (58%). For workplaces that didn't have a dedicated endocrine pathologist, the main limitations were insufficient number of endocrine pathology specimens for the position (53%; 81% reporting an average of ≤10 per month) or insufficient number of qualified endocrine pathologists (46%). Although adrenal specimens are typically received from urology colleagues, many GU pathologists feel it may be prudent to consider them under the rubric of endocrine pathology services as they become more readily available.

A bioinformatics-driven CURE extension increases student self-efficacy and interest in biomedical research

The biology workforce has a need for undergraduate students trained in bioinformatics. Although bioinformatics is a critical sub-discipline of biology, it is not required in all biology degree programs. In parallel, there is a need to increase student access to research experiences. To address these needs, we offer a one-credit bioinformatics-focused and computational biology course-based undergraduate research experience (CURE), here called the CB-CURE. Preliminary data suggest the CB-CURE increased student interest, knowledge, and self-efficacy, but also reveal a shortage of access to undergraduate research experiences (UREs) in faculty labs at our large institution. To provide a more URE-like experience for a class setting, we developed a one-semester extension to the CB-CURE, called CURE+. In CURE+, students execute individual bioinformatics-driven research projects and obtain additional career development and mentoring. To evaluate CURE+, we measured students' bioinformatics and research self-efficacy, interest in bioinformatics and research, and emotions toward their project. Additionally, we evaluated student mastery of the CURE+ learning outcomes to determine if the experience successfully enabled students to develop their research skills. Our data show significant increases in (i) student self-efficacy in various bioinformatics and research skills and (ii) student interest in bioinformatics-related activities and in biomedical research. Students had positive emotions toward their research project, and a majority of students mastered the CURE+ learning outcomes. Our data suggest that an intensive CURE extension can provide a potentially transformative research experience that helps fill a void in access to research at institutions with a high student-to-faculty ratio.

From classroom to clinic: evaluating a clinical pathology course to strengthen pathology report literacy of medical interns

BACKGROUND

Pathology reports serve as the primary communication tool between pathologists and clinicians, directly influencing clinical decision-making and treatment strategies. Despite their critical role, medical students may struggle with interpreting these reports, which can lead to miscommunication and potential diagnostic errors. This study investigated the impact of incorporating a clinical pathology course into the routine medical curriculum to enhance medical students' understanding of pathology reports and their satisfaction with the course.

METHODS

This cross-sectional study involved 92 medical students in their internship phase, who were divided into two groups: those who had completed the clinical pathology course and those who had not. The participants were provided with two pathology reports (covering malignant and benign gastrointestinal diseases) and a self-administered questionnaire consisting of 24 items. The data were analyzed via the chi-square test to assess significant differences between groups.

RESULTS

Medical interns who completed the course demonstrated significantly higher rates of moderate interpretation scores (80.43% vs. 63.04%) and lower rates of weak scores (6.53% vs. 32.61%) compared to those who did not participate (p = 0.001). Interns who passed the clinical pathology course had a significantly greater mean number of correct answers for interpreting malignant cases reports (p = 0.04), although no significant difference was found for benign cases reports (p = 0.93). Most interns who completed the course reported that it helped improve their interpretation skills, although some felt that the perceived benefits were limited. Additionally, the study identified key challenges students still faced when interpreting pathology reports including difficulties with pathology report terminology, understanding cancer staging abbreviations, and applying basic pathology concepts in clinical context.

CONCLUSIONS

Our findings suggest that clinical pathology courses can improve medical students' understanding of pathology reports, particularly in cases of cancer, but improvements in course content and teaching methods are needed. This research offers valuable insights into improving medical education.

Prediction of nitrate concentration and the impact of land use types on groundwater in the Nansi Lake Basin

Groundwater faces a pervasive threat from anthropogenic nitrate contamination worldwide, particularly in regions characterized by intensive agricultural practices. This study examines groundwater quality in the Nansi Lake Basin (NSLB), emphasizing nitrate (NO3--N) contamination. Utilizing 422 groundwater samples, it investigates hydrochemical dynamics and the impact of land use on groundwater composition. Key methods include hydrogeochemical analysis, PCA, and the Duncan comparison method. The innovative aspect lies in using Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) to predict NO3--N contamination. The results showed that NO3--N levels ranged from 0.004 to 177.72 mg/L, with approximately 43.6 % of the samples exceeding the safe drinking water limit of 10 mg/L (WHO 2022). Substantial spatial variability in the concentrations of major ions within aquifers, with NO3--N exhibiting the most significant fluctuations. The factors responsible for the hydrochemical composition of groundwater include recharge sources, water-rock interaction, prevailing groundwater environment, land use patterns, and related anthropogenic activities. Notably, land use types, primarily farmland and rural areas, exhibited a strong association with NO3--N. The MLP-ANNs achieved high prediction accuracy for NO3--N, with an AUC of 0.85. The MLP-ANN model identified heightened susceptibility to nitrate contamination in the central and southeastern regions, characterized by dense shallow wells (<60 m). Key factors include nitrogen-based fertilizer overuse, agricultural runoff, domestic wastewater discharge, and septic system leakage. The vulnerability is exacerbated by highly permeable loose rock pore water systems underlying intensively cultivated agricultural lands. This study elucidates the complex interrelation between natural processes and anthropogenic activities that influence groundwater quality, providing valuable perspectives that could guide the formulation of policies and practices aimed at promoting sustainable groundwater utilization and environmental conservation.

Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG

Background

Template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. Medical education, a field that relies heavily on both formative and summative assessments with multiple choice questions, is in dire need of AI-based support for the efficient automatic generation of items.

Objective

We aimed to propose a hybrid AIG to demonstrate whether it is possible to generate item templates using AI in the field of medical education.

Methods

This is a mixed-methods methodological study with proof-of-concept elements. We propose the hybrid AIG method as a structured series of interactions between a human subject matter expert and AI, designed as a collaborative authoring effort. The method leverages AI to generate item models (templates) and cognitive models to combine the advantages of the two AIG approaches. To demonstrate how to create item models using hybrid AIG, we used 2 medical multiple-choice questions: one on respiratory infections in adults and another on acute allergic reactions in the pediatric population.

Results

The hybrid AIG method we propose consists of 7 steps. The first 5 steps are performed by an expert in a customized AI environment. These involve providing a parent item, identifying elements for manipulation, selecting options and assigning values to elements, and generating the cognitive model. After a final expert review (Step 6), the content in the template can be used for item generation through a traditional (non-AI) software (Step 7). We showed that AI is capable of generating item templates for AIG under the control of a human expert in only 10 minutes. Leveraging AI in template development made it less challenging.

Conclusions

The hybrid AIG method transcends the traditional template-based approach by marrying the "art" that comes from AI as a "black box" with the "science" of algorithmic generation under the oversight of expert as a "marriage registrar". It does not only capitalize on the strengths of both approaches but also mitigates their weaknesses, offering a human-AI collaboration to increase efficiency in medical education.

Next-generation sequencing: A powerful multi-purpose tool in cell line development for biologics production

Within the biopharmaceutical industry, the cell line development (CLD) process generates recombinant mammalian cell lines for the expression of therapeutic proteins. Analytical methods for the extensive characterisation of the protein product are well established; however, over recent years, next-generation sequencing (NGS) technologies have rapidly become an integral part of the CLD workflow. NGS can be used for different applications to characterise the genome, epigenome and transcriptome of cell lines. The resulting extensive datasets, especially when integrated with systems biology models, can give comprehensive insights that can be applied to optimize cell lines, media, and fermentation processes. NGS also provides comprehensive methods to monitor genetic variability during CLD. High coverage NGS experiments can indeed be used to ensure the integrity of plasmids, identify integration sites, and verify monoclonality of the cell lines. This review summarises the role of NGS in advancing biopharmaceutical production to ensure safety and efficacy of therapeutic proteins.

Another gap on the rota: a scoping review of attrition from specialty training in secondary care

BACKGROUND

The healthcare workforce is in crisis. Despite a competitive selection process, a substantial number of doctors leave specialty training (ST) programmes prematurely. This attrition causes increased costs for the National Health Service, exacerbates workforce shortages and threatens quality and safety of care. It also increases pressure on trainees who remain, further compounding the risk of attrition. There is an urgent need to understand why doctors leave ST in order to find ways to maintain the training pipeline from ST to consultant.

OBJECTIVES

We aimed to understand what is known about why doctors choose to leave ST programmes in secondary care, to map current knowledge and identify avenues for future research.

ELIGIBILITY CRITERIA

All studies which investigated why doctors leave ST programmes in secondary care were included.

SOURCES OF EVIDENCE

Ovid Medline, Web of Science, SCOPUS and EMBASE were searched until January 2024. Descriptive codes were assigned to the findings of each study. These descriptive codes were reviewed and grouped together in broader categories.

CHARTING METHODS

Data was extracted and charted, and a qualitative content approach was used to synthesise data.

RESULTS

A total of 6079 potentially relevant abstracts were retrieved, of which 23 were included in the final analysis. This included the experience of 1896 doctors who have left training and 454 programme directors. Doctors chose to leave training programmes because (1) they felt unsupported and underappreciated, (2) training was associated with unacceptable personal costs and (3) career prospects were unattractive.

CONCLUSION

There is a mismatch between trainees' expectations of ST and the reality of being a trainee in ST. Understanding the issues which drive attrition and developing evidence-based solutions, has the potential to both reduce attrition, and improve the training experience for doctors in training more widely.

Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics

This manuscript serves as an introduction to a comprehensive 7-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary. The article also provides a broad overview of the main domains in the AI-ML field, encompassing both generative and nongenerative (traditional) AI, thereby serving as a primer to the other 6 review articles in this series that describe the details about statistics, regulations, bias, ethical dilemmas, and ML-Ops in AI-ML. The intent of these review articles is to better equip individuals who are or will be working in an AI-enabled health care system.

PARAFFIN: A software tool for Pathology Report Automated Feedback for Improved Education of anatomic pathology trainees

Background

Feedback on the diagnosis and reporting of pathology findings is essential to the training of residents and fellows, but time constraints and other factors can make it difficult to ensure learners are made aware of the outcome of all cases in which they participated. Many trainees attempt to keep track of their cases and later look up final pathology reports in the laboratory information system (LIS); however, this manual and time-consuming process is prone to error and may prevent them from spending time reviewing and learning from these reports.

Methods

To address this, we developed a software solution, (Pathology Report Automated Feedback for Improved Education; "PARAFFIN"), which provides pathology trainees with a weekly email digest containing an attached case log with the date, accession sequence, attending pathologist initials, and final diagnosis text for each case in which they participated. PARAFFIN is implemented as two R scripts running on a Posit Connect server: a data extraction script, which accesses an interactive report from our enterprise analytics SQL server, and a reporting script, which performs recipient-specific filtering and emails the trainee with their personalized case log attached as .txt and .csv files. After implementation, pathology trainees were surveyed about PARAFFIN's impact on report collection and case feedback.

Results

Of the total 51 pathology trainees who were receiving PARAFFIN digests at the long-term follow-up timepoint, 20 responded to our survey. 90% (18 of 20) of respondents report that PARAFFIN allows them to spend more time reviewing the content of final anatomic pathology reports, rather than collecting reports. Trainees report utilizing PARAFFIN for feedback on multiple aspects of pathology reporting, with final diagnosis, wording/style of final diagnostic line, and diagnostic comment being most frequently used.

Conclusions

Our automated case feedback solution provides trainees with a record of final pathology reports for cases in which they participated, which allows trainees to spend more time reviewing reports for feedback rather than manually collecting them from the LIS.

Assessment of Approaches Promoting Virtual Radiation Oncology Educational Content to Medical Students

Purpose

Virtual learning in radiation oncology (RO) has potential to reach medical students who otherwise lack access to RO exposure or mentorship at their school. This study characterized the relative effectiveness of different methods of promoting virtual education content, to inform future efforts to expand access to RO education.

Methods and Materials

A 4-part "Oncology Virtual Series for Medical Students" was developed to emulate an oncology interest group (OIG). All academic RO department chairs and residency program directors were asked to engage their respective Dean's office or OIG to promote to all students, especially groups with primarily underrepresented in medicine membership. Promotional emails were also sent to the Diversity, Equity, and Inclusion office of all allopathic United States (US) medical schools, and Student National Medical Association (SNMA) and Latino Medical Student Association (LMSA) regional directors. The American Society for Radiation Oncology (ASTRO) promoted via ASTROgram, social media, and ROhub. Descriptive statistics are reported.

Results

A total of 660 students preregistered, and 140 attended, at least 1 session. Attendees represented 53 allopathic and 2 osteopathic US medical schools, and 18 international schools. One hundred six attendees (87%) were from schools with an affiliated RO department, and 79 (65%) with an affiliated RO residency. Fifteen schools had at least 3 students attend, with the highest number of attendees from the principal investigator's home institution (n = 10). These 15 schools accounted for 52% of all attendees, of which 10 had an affiliated RO residency. Two hundred eighty of six hundred sixty preregistered students (42%) described how they heard about the series: 87 (31%) medical school faculty, 75 (27%) social media post or email, 53 (19%) OIG, 16 (6%) SNMA or LMSA, and 15 (5%) ASTRO.

Conclusions

Disproportionately high attendance was from a few schools, suggesting that radiation oncologists' individual efforts and pre-existing relationships with students, Dean's offices, and student groups are most effective for promoting nationwide virtual RO education.

Combining physical therapy and anatomy education in a high school anatomy outreach program in central Arkansas

Student participation in interactive outreach programs focusing on anatomy has been shown to have a positive impact on learning and healthcare career aspirations. This article describes the Anatomy Outreach Program held at the Physical Therapy Center and Gross Anatomy Lab on the campus of the University of Central Arkansas. Physical therapy (PT) students demonstrated PT skills and led hands-on activities in the gross anatomy lab using donor specimens and models. Hands-on physical therapy-based activities involved wheelchairs and cushions, assistive devices, goniometry and reflex testing, grip strength testing, and lines and tubes in the acute care setting. Before anatomy-based activities, learners were educated about the significance of donors' choice for donating their tissues and respect shown to donors. Learners participated in anatomy stations consisting of hands-on activities with anatomical specimens and models of heart and lungs, brain and spinal cord anatomy, bones and radiographs, and upper and lower extremity anatomy. Content analysis and descriptive statistics were performed on post-tour survey data. After the program, 59% (32/54) of high school learners rate their interest in pursuing a career in healthcare as 10/10. The three biggest takeaways participants took from the program were developing an appreciation for the gross anatomy lab and anatomy education, learning and appreciating the field of PT, and affirming interests in healthcare. High school learners' favorite experiences were in the gross anatomy lab, talking with PT students about the profession, and the goniometer and grip strength activities. One hundred percent (54/54) of survey participants agreed that the program provided them with valuable information.

Evidence-Based Potential of Generative Artificial Intelligence Large Language Models on Dental Avulsion: ChatGPT Versus Gemini

BACKGROUND

In this study, the accuracy and comprehensiveness of the answers given to questions about dental avulsion by two artificial intelligence-based language models, ChatGPT and Gemini, were comparatively evaluated.

MATERIALS AND METHODS

Based on the guidelines of the International Society of Dental Traumatology, a total of 33 questions were prepared, including multiple-choice questions, binary questions, and open-ended questions as technical questions and patient questions about dental avulsion. They were directed to ChatGPT and Gemini. Responses were recorded and scored by four pediatric dentists. Statistical analyses, including ICC analysis, were performed to determine the agreement and accuracy of the responses. The significance level was set as p < 0.050.

RESULTS

The mean score of the Gemini model was statistically significantly higher than the ChatGPT (p = 0.001). ChatGPT gave more correct answers to open-ended questions and T/F questions on dental avulsion; it showed the lowest accuracy in the MCQ section. There was no significant difference between the responses of the Gemini model to different types of questions on dental avulsion and the median scores (p = 0.088). ChatGPT and Gemini were analyzed with the Mann-Whitney U test without making a distinction between question types, and Gemini answers were found to be statistically significantly more accurate (p = 0.004).

CONCLUSIONS

The Gemini and ChatGPT language models based on the IADT guideline for dental avulsion undoubtedly show promise. To guarantee the successful incorporation of LLMs into practice, it is imperative to conduct additional research, clinical validation, and improvements to the models.

Ghost Publications and Research Misrepresentation in the 2023-2024 Plastic Surgery Common Application

PURPOSE

United States Medical Licensing Examination (USMLE) Step 1 Pass/Fail grading has increased the emphasis of research productivity as a quantitative metric in residency applications. Quantifying discrepancies between self-reported and verified research publications can elucidate the extent of research misrepresentation in submitted residency applications.

METHODS

A retrospective review of 339 residency applications to our institution's integrated plastic surgery residency program in the 2023-2024 application cycle was performed. The number of self-reported publications reported between applicants' plastic surgery common application (PSCA) and curriculum vitae (CV) were recorded. Self-reported publications were denoted as verified if a publication record was identified with an independent internet search.

RESULTS

The mean number of self-reported and verified total peer-reviewed publications was 10.8 and 7.4, respectively. In 9.1% (31/339) of applicants, unverified manuscripts or discrepancies between self-reported and verified author lists were identified. Unpublished manuscripts and publication records of nonpeer-reviewed abstract presentation records were self-reported under the PSCA total number of proportion of unmatched applicants (62%) versus matched applicants (48%) misrepresented unpublished manuscripts or nonpeer-reviewed abstract presentation records as peer-reviewed publications (P = 0.011).

CONCLUSIONS

Several types of discrepancies between self-reported and verified research publications were identified in PSCA applications. Discrepancies may result from PSCA prompt misinterpretation, inadvertent misclassification, or applicant misrepresentation or falsification. Efforts to standardize the reporting and verification of research in residency applications are needed.

Leveraging large language models to improve patient education on dry eye disease

BACKGROUND/OBJECTIVES

Dry eye disease (DED) is an exceedingly common diagnosis in patients, yet recent analyses have demonstrated patient education materials (PEMs) on DED to be of low quality and readability. Our study evaluated the utility and performance of three large language models (LLMs) in enhancing and generating new patient education materials (PEMs) on dry eye disease (DED).

SUBJECTS/METHODS

We evaluated PEMs generated by ChatGPT-3.5, ChatGPT-4, Gemini Advanced, using three separate prompts. Prompts A and B requested they generate PEMs on DED, with Prompt B specifying a 6th-grade reading level, using the SMOG (Simple Measure of Gobbledygook) readability formula. Prompt C asked for a rewrite of existing PEMs at a 6th-grade reading level. Each PEM was assessed on readability (SMOG, FKGL: Flesch-Kincaid Grade Level), quality (PEMAT: Patient Education Materials Assessment Tool, DISCERN), and accuracy (Likert Misinformation scale).

RESULTS

All LLM-generated PEMs in response to Prompt A and B were of high quality (median DISCERN = 4), understandable (PEMAT understandability ≥70%) and accurate (Likert Score=1). LLM-generated PEMs were not actionable (PEMAT Actionability <70%). ChatGPT-4 and Gemini Advanced rewrote existing PEMs (Prompt C) from a baseline readability level (FKGL: 8.0 ± 2.4, SMOG: 7.9 ± 1.7) to targeted 6th-grade reading level; rewrites contained little to no misinformation (median Likert misinformation=1 (range: 1-2)). However, only ChatGPT-4 rewrote PEMs while maintaining high quality and reliability (median DISCERN = 4).

CONCLUSION

LLMs (notably ChatGPT-4) were able to generate and rewrite PEMs on DED that were readable, accurate, and high quality. Our study underscores the value of leveraging LLMs as supplementary tools to improving PEMs.

Evaluation of whole-slide imaging for diagnosing frozen sections

A promising application of digital pathology is the use of Whole slide imaging (WSI) for rapid and remote intraoperative consultations. Based on recommendations from the College of American Pathologists, we compared diagnostic accuracy and technical analysis of WSI with optical microscopy (OM) for reporting frozen sections (FS). A series of 105 consecutive FS cases were included in our study and were categorized as primary diagnosis, assessment of margin status, and lymph node status. A surgical pathologist reviewed all WSI digital slides of FS cases online and their corresponding glass slides using OM after a 2-week washout period. Technical and diagnostic parameters for remote reporting of frozen sections using WSI were compared to routine OM. Diagnostic agreement between WSI and OM in the FS cases was 100 %. In comparison with the reference standard (original sign-out diagnosis), the overall diagnostic accuracy of WSI and OM was 99.04 %. Scan time per slide averaged 103.89 s. Mean diagnostic assessment time for OM was 17.48 s, while it was 26.62 s for WSI, with a mean difference of 9.14 s (P < .001). The overall mean turnaround time was 3.8 min for reporting a single slide using WSI based digital pathology system. The diagnostic accuracy of WSI is comparable to that of conventional OM. Therefore, we conclude that WSI based digital pathology systems can be safely implemented and integrated into a laboratory workflow as an alternative to conventional OM.

Myeloid sarcoma of the heart-A clinicopathological correlation

A 63-year-old woman with a history of acute myeloid leukemia followed by stem cell transplantation presented with acute heart failure. Transthoracic echocardiography revealed a preserved left ventricular ejection fraction with severe ventricular hypertrophy and signs of elevated filling pressures indicating infiltrative cardiomyopathy. She died from cardiac arrest due to cardiogenic shock. The autopsy revealed an enlarged heart with a fish-flesh appearance. Here, we describe a rare case of a myeloid sarcoma of the heart.

General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing

CONTEXT.—

The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development.

OBJECTIVE.—

To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing.

DESIGN.—

CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review.

RESULTS.—

Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images.

CONCLUSIONS.—

Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.

Making housestaff feel at home: impact of workspace interventions on anatomic pathology trainee wellness

To improve trainee well-being in a healthcare setting, we implemented changes shown to significantly improve employee well-being in corporate settings in an anatomic pathology (AP) trainee workspace at one large academic program and measured changes in trainee stress and well-being. An IRB-approved survey was distributed to trainees before and after implementation of workspace modifications, which included deep cleaning of the physical space, making storage space for personal items, improving access to perishable and nonperishable foods, arranging equipment to facilitate ergonomic use, and providing real and artificial visuals of nature. The survey incorporated evidence-based scales including the Ambient Belonging Scale (ABS), the five-item WHO-5 Well-Being Index, the five-item modified Spielberger State and Trait Anxiety Scales, and the Growth Mindset Scale. Pre-intervention (n = 21) and post-intervention (n = 18) participants had scores consistent with a growth mindset, no significant anxiety state or trait, and above average sense of well-being. Compared with pre-intervention survey results, post-intervention AP residents who actively worked in the space had a significantly increased sense of belonging. Free-text feedback indicated that our efforts to improve the environment and to increase access to food positively impacted their well-being as AP trainees. We show that workspace interventions implemented at our institution significantly increased a sense of belonging for our trainees independent of their growth mindset, anxiety state or trait, and sense of well-being, which was high pre- and post-intervention. These simple and cost-effective workspace interventions can be implemented broadly to create a more supportive, inclusive environment for pathology trainees.

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to identify patient populations likely to respond to ICI treatments, particularly in lung cancer treatment. Currently, the tumor proportion score (TPS), a crucial biomarker for patient selection, relies on manual interpretation by pathologists, which often shows substantial variability and inconsistency. To address these challenges, we innovatively developed multi-instance learning for TPS (MiLT), an innovative artificial intelligence (AI)-powered tool that predicts TPS from whole slide images. Our approach leverages multiple instance learning (MIL), which significantly reduces the need for labor-intensive cell-level annotations while maintaining high accuracy. In comprehensive validation studies, MiLT demonstrated remarkable consistency with pathologist assessments (intraclass correlation coefficient = 0.960, 95% confidence interval = 0.950-0.971) and robust performance across both internal and external cohorts. This tool not only standardizes TPS evaluation but also adapts to various clinical standards and provides time-efficient predictions, potentially transforming routine pathological practice. By offering a reliable, AI-assisted solution, MiLT could significantly improve patient selection for immunotherapy and reduce inter-observer variability among pathologists. These promising results warrant further exploration in prospective clinical trials and suggest new possibilities for integrating advanced AI in pathological diagnostics. MiLT represents a significant step toward more precise and efficient cancer immunotherapy decision-making.

Influence of hemolysis, lipemia and bilirubin on biobank sample quality- origin and interference in the use for extracellular vesicle (EV) and MiRNA analyses

PURPOSE

Pre-analytic interferences can influence the laboratory downstream measurements. We recognized hemolysis, lipemia and bilirubin in some of the serum/plasma samples of the NTF-Biobank from polytraumatized patients. Aim of the present study was to detect interferences, find reasons and describe the influence on downstream analyses.

METHODS

The study included serum samples of n = 88 polytraumatized patients admitted to a Level 1 Trauma Center in Germany at the ER & up to 10 days after trauma. Optical absorption spectra of UV-VIS (350-660 nm) were measured to detect hemolysis, lipemia and bilirubin. To find reasons for the interferences, clinical parameters like triglycerides (TAGs), nutrition, anaesthesia or transfusions were collected from patients' record. Extracellular vesicles (EVs) were isolated by SEC from controls, lipidemic and hemolytic samples and analysed via NTA.

RESULTS

Within 10 days after trauma 31.8% of polytraumatized patients' samples showed hemolysis, 12.5% showed increased bilirubin and 15.9% lipemia. Hemolysis occurred in samples mostly at the ER (18%) and was not associated with the number of red blood cell transfusions or the ISS. Both contaminants, hemolysis and lipemia interfered with EV/EV-miRNA measurements. EV miR-16-5p was significantly increased in patients with hemolysis. The presence of lipids further influenced the EV particle size distribution and concentration.

CONCLUSION

The optical absorption spectra measurement is an easy tool for a robust pre-analytic sample controlling for the presence of interferences. Nutrition and anaesthesia were found to be related with lipemia in samples. Hemolysis and lipemia interfered with EV/EV-miRNA analysis. Therefore, the optical absorption spectra pre-analyses should be incorporated in the EV-biobank sampling.

Leveraging innovative diagnostics as a tool to contain superbugs

The evolutionary adaptation of pathogens to biological materials has led to an upsurge in drug-resistant superbugs that significantly threaten public health. Treating most infections is an uphill task, especially those associated with multi-drug-resistant pathogens, biofilm formation, persister cells, and pathogens that have acquired robust colonization and immune evasion mechanisms. Innovative diagnostic solutions are crucial for identifying and understanding these pathogens, initiating efficient treatment regimens, and curtailing their spread. While next-generation sequencing has proven invaluable in diagnosis over the years, the most glaring drawbacks must be addressed quickly. Many promising pathogen-associated and host biomarkers hold promise, but their sensitivity and specificity remain questionable. The integration of CRISPR-Cas9 enrichment with nanopore sequencing shows promise in rapid bacterial diagnosis from blood samples. Moreover, machine learning and artificial intelligence are proving indispensable in diagnosing pathogens. However, despite renewed efforts from all quarters to improve diagnosis, accelerated bacterial diagnosis, especially in Africa, remains a mystery to this day. In this review, we discuss current and emerging diagnostic approaches, pinpointing the limitations and challenges associated with each technique and their potential to help address drug-resistant bacterial threats. We further critically delve into the need for accelerated diagnosis in low- and middle-income countries, which harbor more infectious disease threats. Overall, this review provides an up-to-date overview of the diagnostic approaches needed for a prompt response to imminent or possible bacterial infectious disease outbreaks.

Primary cutaneous adenoid cystic carcinoma arising in the scalp: a diagnostic challenge

Primary cutaneous adenoid cystic carcinoma (PCACC) is a rare adnexal tumour of the skin, predominantly affecting individuals over 50 years of age, with no established gender predilection. It can arise on various anatomical sites without a clear preference. To date, fewer than 200 cases have been documented in the literature.We present a case of a young, otherwise healthy female who developed a slow-growing painless, skin-coloured scalp nodule. Histopathological examination of an excisional biopsy revealed a dermal tumour composed of basaloid cells arranged in a characteristic cribriform pattern. A thorough diagnostic workup, including sonographic imaging, immunohistochemical analysis and multidisciplinary team discussions, ultimately confirmed the diagnosis of PCACC. This case underscores the diagnostic and therapeutic challenges associated with PCACC.

A Primer on Bluesky (and PathSky) for Pathologists, Trainees, and Medical Students

Social media (SoMe) has become an integral tool in modern pathology, facilitating education, research, mentorship, and professional networking. However, the evolving landscape of SoMe platforms presents both opportunities and challenges for pathologists. Bluesky, a decentralized platform launched publically in 2024 has gained significant traction among pathologists as an alternative to "traditional," or more widely-used, platforms like Twitter/X. This narrative review explores the role of SoMe in pathology, introduces Bluesky and its pathology-focused community PathSky, and compares it with other platforms. In addition, practical guidance on joining Bluesky and engaging with PathSky is provided. By embracing innovative platforms like Bluesky, pathologists can enhance collaboration, education, and professional growth in the digital age.

Big data analytics in food industry: a state-of-the-art literature review

The food industry has experienced rapid growth over the past two decades, driven by technological advancements that have generated vast quantities of complex data. However, the industry's ability to effectively analyze and leverage this data remains limited due to the lack of control over diverse variables. This review addresses a critical gap by exploring how AI-ML-based approaches can be applied to solve key challenges in the food sector.

Guidance for sharing computational models of neural stimulation: from project planning to publication

Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project's stages.Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models.Main results. We first describe the '6 R' characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation.Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models-from project planning through publication-as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

Frozen sections in gastrointestinal, pancreatobiliary and hepatic pathology: A review

In the digestive system, intraoperative frozen sections are commonly requested to assess surgical margins, obtain diagnostic material, and evaluate incidental lesions. Frozen section results may alter surgical planning or lead to the discontinuation of the procedure. As a practicing pathologist, understanding the indication for frozen section and its impact on patient management would improve communication with surgeons. Likewise, understanding what to look for and focus on, what to relay to the requester and common diagnostic pitfalls would improve the quality of service one provides and patients' outcome. Herein we provide an overview of common frozen sections encountered during variable abdominal procedures to include pancreaticoduodenectomy, gastrectomy, appendectomy, colorectal resection and Hirschsprung pull-through along with ample microscopic images.

Artificial intelligence and whole slide imaging, a new tool for the microsatellite instability prediction in colorectal cancer: Friend or foe?

Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heterogeneous and the response to therapy varies significantly, limiting personalized treatment options. Certain molecular biomarkers, including microsatellite instability (MSI), are critical in planning personalized treatment, although only a subset of patients may benefit. Currently, the primary methods for assessing MSI status include immunohistochemistry (IHC) for DNA mismatch repair proteins (MMRs), polymerase chain reaction (PCR)-based molecular testing, or next-generation sequencing (NGS). However, these techniques have limitations, are expensive and time-consuming, and often result in inter-method inconsistencies. Deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H) are critical predictive biomarkers of response to immune checkpoint inhibitor (ICI) therapy and MSI testing is recommended to identify patients who may benefit. There is a pressing need for a more robust, reliable, and cost-effective approach that accurately assesses MSI status. Recent advances in computational pathology, in particular the development of technologies that digitally scan whole slide images (WSI) at high resolution, as well as new approaches to artificial intelligence (AI) in medicine, are increasingly gaining ground. This review aims to provide an overview of the latest findings on WSI and advances in AI methods for predicting MSI status, summarize their applications in CRC, and discuss their strengths and limitations in daily clinical practice.

Evaluation of a pathology resident wellness initiative: Initial establishment and subsequent expansion through a time of high stress, the COVID-19 pandemic

OBJECTIVES

Recent studies have shown that the pathology workforce is at risk of decreased workplace well-being, which may lead to decreased job satisfaction, increased attrition, burnout, depression, anxiety, and suicidality, but there has been relatively little research on well-being initiatives designed for pathologists, pathology trainees, and laboratory professionals. Some studies have suggested that well-being initiatives may decrease burnout and increase workplace satisfaction and engagement.

METHODS

Here we describe the creation of a Pathology Wellness Committee in a large residency program. Interventions included emotional, social, and physical well-being interventions as well as system-based improvements. Additional initiatives were introduced in response to the increased stress, isolation, and social distancing guidelines during the height of the COVID-19 pandemic. The program's impact was measured by an annual House Staff Council Resident Wellness Survey over 4 years.

RESULTS

The annual surveys showed improvements in workplace and residency program satisfaction and emotional well-being following system-based improvements and well-being initiatives. Physical and social well-being showed slight but not statistically significant decreases over the 4-year period. Results from the annual Accreditation Council for Graduate Medical Education Survey were also evaluated.

CONCLUSIONS

We found that dedicated well-being initiatives in conjunction with system-based interventions may help improve overall well-being in pathology residents.

Targeted delivery of TAPI-1 via biomimetic nanoparticles ameliorates post-infarct left ventricle function and remodeling

AIMS

The potential of nanoparticles as effective drug delivery tools for treating failing hearts in heart failure remains a challenge. Leveraging the rapid infiltration of neutrophils into infarcted hearts after myocardial infarction (MI), we developed a nanoparticle platform engineered with neutrophil-membrane proteins for the targeted delivery of TAPI-1, a TACE/ADAM17 inhibitor, to the inflamed myocardium, aiming to treat cardiac dysfunction and remodeling in rats with MI.

METHODS AND RESULTS

Neutrophil-mimic liposomal nanoparticles (Neu-LNPs) were constructed by integrating synthesized liposomal nanoparticles with LPS-stimulated neutrophil membrane fragments and then loaded with TAPI-1. MI rats were treated with TAPI-1 delivered via Neu-LNPs for 4 weeks. Left ventricular function was assessed by echocardiography and cardiac fibrosis was evaluated post-treatment. The novel Neu-LNPs maintained typical nanoparticle features, but with increased biocompatibility. Neu-LNPs demonstrated improved targeting ability and cellular internalization, facilitated by LFA1/Mac1/ICAM-1 interaction. Neu-LNPs displayed higher accumulation and cellular uptake by macrophages and cardiomyocytes in infarcted hearts post-MI, with a sustained duration. Treatments with TAPI-1-Neu-LNPs demonstrated greater protection against myocardial injury and cardiac dysfunction in MI rats compared to untargeted TAPI-1, along with reduced cardiac collagen deposition and expression of fibrosis biomarkers as well as altered immune cell compositions within the hearts.

CONCLUSIONS

Targeted treatment with TACE/ADAM17 inhibitor delivered via biomimetic nanoparticles exhibited pronounced advantages in improving left ventricle function, mitigating cardiac remodeling, and reducing inflammatory responses within the infarcted hearts. This study underscores the effectiveness of Neu-LNPs as a drug delivery strategy to enhance therapeutic efficacy in clinical settings.

Prediction of time averaged wall shear stress distribution in coronary arteries' bifurcation varying in morphological features via deep learning

Introduction

Understanding the hemodynamics of blood circulation is crucial to reveal the processes contributing to stenosis and atherosclerosis development.

Method

Computational fluid dynamics (CFD) facilitates this understanding by simulating blood flow patterns in coronary arteries. Nevertheless, applying CFD in fast-response scenarios presents challenge due to the high computational costs. To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries' bifurcation.

Results

To prepare the dataset, 1800 idealized models with varying morphological parameters are created. Afterward, we design a CNN-based U-net architecture to predict TAWSS by the point cloud of the geometries. Moreover, this architecture is implemented using TensorFlow 2.3.0. Our results indicate that the proposed algorithms can generate results in less than one second, showcasing their suitability for applications in terms of computational efficiency.

Discussion

Furthermore, the DL-based predictions demonstrate strong agreement with results from CFD simulations, with a normalized mean absolute error of only 2.53% across various cases.

Electric Trends of Laboratory Medicine: Five Years of Growth, Visibility, and Opportunity

BACKGROUND

Laboratory medicine has and continues to undergo significant transformation. This paper reviews top trends associated with laboratory medicine using insights, evidence, and outcomes derived from the UNIVANTS of Healthcare ExcellenceTM award program.

METHODS

Seventy-two judge-approved best practices of measurably better healthcare were assessed for trends and insights related to outcomes and opportunities for highlighting the value of laboratory medicine.

RESULTS

Ten industry-relevant and insightful takeaways are identified that span stakeholders and key performance indicators.

CONCLUSION

With evidence that spans 5 years, the findings not only substantiate the critical value of laboratory medicine, but reveal trends associated with award-winning teams, proven integrated clinical care initiatives, and the measurement of their associated outcomes.

Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning

The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such nongenerative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (ie, classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based nongenerative foundation models. Unsupervised learning techniques, including clustering, dimensionality reduction, and anomaly detection, are also discussed for their roles in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of nongenerative AI algorithms for analyzing whole slide images are also highlighted. The performance, explainability, and reliability of nongenerative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.