ZP3 genes. Within the ZP3 gene, we identified a homozygous variant  specifically located in the zona pellucida (ZP) domain. Further analysis, including bioinformatics methods and protein structure modeling, was carried out to investigate the conservation of the ZP3L59H variant across different species. This homozygous variant exhibited a high degree of conservation across various species. Importantly, the homozygous ZP3L59H variant was associated with the occurrence of empty follicle syndrome in affected female patients. The homozygous ZP3L59H variant represents a newly discovered genetic locus implicated in the development of human empty follicle syndrome. Our findings contribute to a deeper understanding of the role of zona pellucida-related genes in infertility and provide valuable insights for the genetic diagnosis of female infertility.Empty follicle syndrome (EFS) is a rare condition in female infertility. It is characterized by the inability to retrieve oocytes from visibly large, normally developing follicles in the ovaries, despite ovarian stimulation. The genetic factors contributing to this syndrome remain unclear. This study focused on patients who underwent three consecutive ovarian stimulation procedures for oocyte retrieval but experienced unsuccessful outcomes, despite the presence of observable large follicles. Ultrasound examinations were conducted to assess follicular development during each procedure. In order to investigate potential genetic causes, we performed whole exome sequencing on peripheral blood samples from the patient. Interestingly, we identified that this patient carries a homozygous mutation in the  Empty follicle syndrome (EFS) refers to a condition where no oocytes can be retrieved from apparently large, normally growing, fluid-filled follicles in the ovaries after stimulation. EFS can be classified into two main types based on circulating HCG levels: the “genuine” form (gEFS) and the “false” form (f-EFS) . AlthougThe zona pellucida (ZP) is an extracellular glycoprotein matrix that accompanies the growth of oocytes and remains intact around the embryo until hatching . Within The human ZP is primarily composed of four proteins . These ZP-related proteins exhibit structural conservation and possess a signal sequence, ZP domain (ZPD), consensus furin cleavage site (CFCS), and transmembrane domain (TMD). The trefoil domain is found only in ZP1 and ZP4 . InteresZP3 gene through whole-exome sequencing. Despite the presence of large follicles, this patient failed to retrieve any oocytes after three rounds of ovarian stimulation. Subsequent bioinformatic analysis provided further confirmation of the impact of this new mutation on the ZP3 protein.In this study, we identified a novel homozygous variant  located at the N-terminus of the ZP domain in the Approval for this study was obtained from the Ethics Committee of Nanjing Drum Tower Hospital (2021-384-01). All embryos analyzed in this study were obtained from the Center for Reproductive Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University. Informed consent was obtained from all participants consenting to the collection of clinical samples relevant to this study.http://varnomen.hgvs.org/). Criteria for grading the pathogenic nature of variants were established based on the standards and guidelines for variation interpretation set forth by the American College of Medical Genetics and Genomics  (Genomics . Notablyhttps://www.uniprot.org/). Schematic representations of both the wild-type (WT) and mutated  ZP3 proteins were generated using SWISS-MODEL software (https://swissmodel.expasy.org). The 3nk3.1.A.pdb template was selected as the control template for modeling.The mutated sites within the ZP3 protein were visually depicted using IBS 2.0. Conservation analysis of ZP3 amino acids across various species, including mouse, rat, sheep, bovine, macaque, and human, was conducted using the Align function available on the UniProt website (http://mutationassessor.org/) server predicts the functional impact of amino-acid substitutions in proteins, such as mutations discovered in disease. The functional impact is assessed based on evolutionary conservation of the affected amino acid in protein homologs. PolyPhen-2  across various species were reanalyzed using the single-cell transcriptome database  one-cell embryos and shows a further increase in expression at the four-cell stage. Subsequently, the expression level of ZP3 gradually decreases. These findings suggest that ZP3 plays a crucial role in the development of 2PN embryos. Interestingly, the expression levels of ZP3 in 2PN embryos were most abundant at various stages of preimplantation embryo development in mice and marmosets  Despite the presence of a large amount of mRNA stored within oocytes, only a fraction of it is translated. Using ribosome profiling (low-input Ribo-seq) methodology, we have further elucidated the dynamic changes in ZP3 protein translation levels. 3) Due to the scarcity of human embryos and ethical constraints, our cross-species analysis has revealed intriguing differences in the expression patterns of the zp3 gene in mice and marmosets. While ZP3 expression gradually decreases from the embryo to the four-cell and eight-cell stages in these species, ZP3 gene expression increases in human four-cell embryos. These results suggest species-specific functional differences of the ZP3 gene, particularly indicating a potentially distinct role for ZP3 in early human embryonic development. Although human embryo samples are limited, this finding is noteworthy and warrants further investigation.The human armosets . This in species . Since o zygotes   in this patient with human empty follicle syndrome. Further validation in the patient’s parents revealed that both of them were carriers, with DNA sequencing showing heterozygous ZP3L59H mutations  domain. The ZP domain is a protein polymerization module of approximately 260 amino acids found at the C-terminus of many secreted eukaryotic glycoproteins that play fundamental roles in development, hearing and immunity   . In compd human) .L59H mutation may have an impact on the protein’s tertiary structure.Oocyte anomalies and female infertility, particularly in the context of Gamete Elastase Factor Syndrome (GEFS), have been associated with various mutations in human ZP genes. In GEFS cases with mutations in ZP1 or ZP3, oocytes were either degenerated or completely absent, while cumulus-oocyte complexes (COCs) could still be obtained . HoweverHuman ZP glycoproteins (ZPGs) consist of various domains, including an N-terminal signal peptide, a ZP domain (ZPD), a C-terminal peptide with a consensus furin cleavage site (CFCS), and a transmembrane domain (TMD) . ZPD, esPrevious studies have mostly focused on the role of ZP3 in oocyte maturation processes , which iThe egg envelope is a fibrous matrix composed of conserved components, characterized by shared protein domains. Among mammals, amphibians, birds, and fish, the composition of the egg envelope varies as it is made up of ZP domain-containing proteins . NeverthL59R mutations to early embryonic development. However, it is important to acknowledge the limitations of our research. Firstly, the sample size employed in this study is relatively small, necessitating the identification of the same mutation site with similar phenotypes across multiple reproductive centers to confirm our findings. Secondly, it is important to consider the inherent limitations of the exome sequencing analysis process, as well as the ongoing updates and improvements to the pathogenic variants database. Consequently, there is a possibility that our study may have missed the discovery of other pathogenic variants in different genes that could have contributed to this phenotype (In this study, we have elucidated the noteworthy contribution of ZP3henotype .In summary, we have reported a novel homozygous variant in ZP3 associated with empty follicle syndrome. This mutation leads to abnormal ZP assembly and follicular development, providing insights into the pathogenicity of the mutation and potential implications for the diagnosis and treatment of EFS.


Teaching Point: Multiple micronodular pneumocyte hyperplasia (MMPH) is a less-known presentation of tuberous sclerosis complex (TSC) associated lung disease that usually requires no treatment or follow-up imaging. Tuberous sclerosis complex (TSC) is a genetic systemic disorder characterized by the formation of widespread hamartomas and other dysplastic lesions in various organs. The typically affected organs are the brain, kidneys, lungs, skin, and heart.Pulmonary involvement is frequent in patients with TSC, most typically lymphangioleiomyomatosis (LAM). LAM is characterized by multiple cystic lung lesions, which can lead to pneumothorax or chylous pleural collection and ultimately can cause progressive pulmonary failure. Multiple micronodular pneumocyte hyperplasia (MMPH) is a second and lesser-known presentation of lung disease associated with TSC. Early diagnosis of MMPH on computed tomography is critical to prevent unnecessary treatment or follow-up, since MMPH is not progressive and is asymptomatic.In this case report, we will discuss a patient with TSC and signs of MMPH on a screening computed tomography scan of the chest.We present a case of a 37-year-old woman with a history of tuberous sclerosis without any respiratory complaints.A computed tomography scan was ordered to examine lung involvement secondary to TSC. Multiple scattered nodules, both solid and ground glass, were noted in a random distribution pattern , 2. The Lymphangioleiomyomatosis (LAM) and multifocal micronodular pneumocyte hyperplasia (MMPH) are both classic manifestations of lung involvement in TSC. The prevalence of LAM is estimated at around 40% of female patients with TSC and increases with age up to around 80% prevalence in patients above 40 years old.LAM presents typically with progressive cystic lesions in the lungs which can cause recurrent pneumothorax, chylous pleural collection and can lead to progressive pulmonary failure. A lesser-known presentation of pulmonary involvement of TSC is multiple micronodular pneumocyte hyperplasia (MMPH). MMPH is characterized by the proliferation of type II pneumocytes along the alveolar septa combined with an increase in alveolar macrophages, fibrotic interstitial thickening, and possible lymphocytic infiltration . MMPH afMMPH typically presents multiple pure ground glass nodules in a random distribution pattern , 2, 3. TMultiple micronodular pneumocyte hyperplasia is a less-known presentation of TSC associated lung disease compared to lymphangioleiomyomatosis. It presents as multiple pure ground glass nodules with a random distribution pattern on computed tomography. Patients with MMPH are typically asymptomatic and lung lesions are not progressive. Treatment or follow-up imaging are therefore unnecessary.


Pemphigus is a rare group of autoimmune mucocutaneous blistering conditions for which the mainstay of treatment is immunosuppression. This is usually achieved with high dose corticosteroids as well as steroid sparing agents. Rituximab is now recommended as a first line treatment for moderate to severe pemphigus vulgaris, the commonest form of pemphigus, alongside corticosteroids. During the early stages of the COVID-19 pandemic the use of rituximab was reduced in our department due to its long term irreversible B-cell suppression. During the COVID-19 pandemic careful pharmacological selection was undertaken for our pemphigus patients to balance the risks of immunosuppression. To demonstrate this, we report three pemphigus patients who required treatment for COVID-19 and assessment throughout the pandemic. To date there has been limited published data regarding the clinical outcomes of pemphigus patients who have developed COVID-19 infections following rituximab infusions, especially in those patients who have received COVID-19 vaccinations. Following careful personalized consideration, all three pemphigus patients presented received rituximab infusions since the start of the COVID-19 pandemic. These patients had also received COVID-19 vaccinations prior to becoming infected with COVID-19. Each patient had a mild COVID-19 infection after receiving rituximab. We advocate for all pemphigus patients to have a full course of COVID-19 vaccinations. Antibody response to COVID-19 vaccinations should ideally be confirmed by measuring pemphigus patient’s SARS-CoV-2 antibodies prior to receiving rituximab. Pemphigus is a rare group of autoimmune mucocutaneous blistering conditions, which are characterized by the production of autoantibodies against desmogleins (DSG) 1 and/or 3, leading to acantholysis (In 2018 the US Food and Drug Administration (FDA) approved rituximab for the management of adults with moderate to severe pemphigus vulgaris (PV). Rituximab is a monoclonal antibody which targets the CD20 antigen on B lymphocytes which results in suppression of CD20 expressing B lymphocytes, which leads to reduced plasma cells and therefore, reduced antibody production. This effect lasts approximately 6 months, with recovery in B lymphocytes expected 9–12 months following treatment. Rituximab is now recommended as a first line treatment for pemphigus in conjunction with high dose oral corticosteroids, Joly el al. . HoweverHighlighted by the three following PV cases, we report on our careful immunosuppressive pharmacologic selection and experience using rituximab during the COVID-19 pandemic. We also report on the outcomes of these PV patients who were subsequently diagnosed with COVID-19 after receiving rituximab infusions . To our A 36 year old female of Asian ethnicity presented in 2019 with a 2 year history of oral blisters, primarily affecting her cutaneous and mucosal buccal region . The diaA 41 year old female of Indian descent presented in February 2021 with a 1 year history of established PV, with lesions primarily affecting her oral mucosa, face and upper back . She hadA 51 year old Caucasian female with a 7 year history of PV, was enrolled in a placebo controlled, phase 3 clinical trial for PV during October 2019 to October 2020 . Due to The management of pemphigus presented many challenges during the COVID-19 pandemic. Pemphigus itself is associated with significant mortality if not managed effectively, however, the use of immunosuppressive treatment poses its own increased infective risk which has been exacerbated during the COVID-19 pandemic. The use of rituximab posed a particular challenge during the COVID-19 pandemic. The prolonged B-cell suppression produced by rituximab leads to a reduction in circulating pemphigus pathogenic autoantibodies, improving disease control. Its use has been shown to reduce the overall mortality in pemphigus and results in less adverse events compared to corticosteroid treatment monotherapy. However, given the prolonged B-cell suppression these patients have been shown to have increased COVID-19 related mortality , 7. It hThese three cases discussed demonstrate the consideration of different management decisions during the COVID-19 pandemic. Australia adopted strict COVID-19 restrictions, including shutting international borders, restricting immigration and international travel and enforcing strict lockdowns. This kept case numbers low in comparison to Europe and the Americas until these restrictions were relaxed in late 2021 . HoweverClinicalTrials.gov, identifier: NCT03762265). The benefit of this treatment is that it works via reversible covalent binding and therefore has a self-limited immunomodulatory effect  for the publication of any potentially identifiable images or data included in this article.BK designed and wrote the first draft of the manuscript. DM supervised the work. All authors contributed to the preparation of the manuscript and approved the submitted version.


Systemic racism perpetuates health disparities and negatively impacts health care delivery and patient outcomes. Racism and bias can affect every aspect of clinical care, including history-taking, physical examination, laboratory interpretation, note-writing, oral presentation, and decision-making. Medical students must learn racism- and bias-mitigation skills early in their professional development to provide high-quality, equitable care.In November 2021, senior medical students and faculty with expertise in promoting health equity and justice in medicine designed and cotaught a Zoom-based, 75-minute, interactive session for second-year medical students. Participants prepared by reading assigned articles. Breakout rooms were used to facilitate small-group discussions. Session topics included use of a structural vulnerability assessment tool, examples of how bias can impact the physical exam, demonstration of how language can transmit bias, and skill practice using neutral instead of stigmatizing language.Forty second-year medical students participated in the session. Thirty-one students (78%) completed Likert-type surveys evaluating reaction and learning. Results showed improvements in students’ perceptions of their abilities to assess for structural factors that influence health, recognize ways bias can impact clinical encounters, and apply skills to minimize bias in clinical care and decision-making.Providing opportunities for health care learners to think critically about how bias impacts patients and communities and equipping them with tools to begin dismantling exclusionary, racist practices in medicine are achievable and crucial to actualizing a just and equitable health system. This educational session can be adapted for training across health care professions and the educational continuum. In response, some medical schools have initiated anti-racism efforts, including curricular innovations that aim to shift medical cultures and systems toward anti-racist change. Students and house staff have frequently been at the forefront of advocacy for anti-racist structural and curricular innovations in medical education.2Systemic racism and bias are pervasive in medicine and cause increased morbidity and mortality for minoritized populations, perpetuating health disparities.3; (b) skill-building in microaggression response, emphasizing speaking up against and encouraging dialogue around microaggressions8; (c) the use of neutral instead of stigmatizing language to reduce bias transmission when communicating about patients11; and (d) structural competency in medical education.12 Learner populations have included medical students, residents, fellows, faculty, and institutional leaders.There is a growing field of scholarship on anti-racism in medical education. A review of publications found that curricular innovations have focused on (a) knowledge-building in the areas of cultural competency, implicit bias, racism, health disparities, and social determinants of healthHowever, there is a lack of educational initiatives designed to impart a cohesive view of multiple concrete clinical skills that combat racism and bias in patient care. Developing anti-racist clinical skills is critical to eliminating health inequities because racism and bias can negatively influence every aspect of clinical care, including history-taking, the physical exam, laboratory interpretation, note-writing, oral presentations, and decision-making. To address this gap, senior medical students and faculty educators developed, piloted, and evaluated a workshop to teach second-year medical students concrete skills promoting equitable care at multiple points during a clinical encounter.Our session is unique in that it applies multiple concepts and skills aimed at mitigating bias in patient care, organizing them around a typical patient encounter. In addition, our session is designed for preclinical medical students at a critical time during training before they begin their clinical rotations. Our session is also uniquely proactive by having participants learn and practice bias-mitigation skills. We chose this action-oriented, case-based approach that focuses on skills development because eliminating racism and inequity in medicine requires training future health care providers to actively apply tools to uproot deeply entrenched racism and bias.To increase knowledge of how racism impacts patient care and teach bias-mitigation skills, we designed a 75-minute workshop entitled Preparing Medical Students for Anti-racism at the Bedside  that wouWe offered the workshop to second-year medical students as part of the preclinical course teaching history-taking and physical exam skills. The session occurred several weeks prior to students’ entry into clinical clerkships and was one of three offered options—anti-racism at the bedside, clinical dermatology, and surgical skills—all of which were designed to be immediately applicable in the clinical setting. All participants had previously completed anti-racism coursework, including an 8-hour prematriculation reading assignment and small-group discussion curriculum focused on texts about historic and current systemic racism, bias, privilege, health equity, genetics, and health disparities.13 facilitated each workshop. E&J fellows were students selected for their exceptional interest and activism regarding anti-racism in medical education. Faculty facilitators, who had heterogeneous health equity knowledge levels, participated in a 1-hour preparatory training with E&J fellows to review the workshop and discuss content-related questions. In addition, student–faculty cofacilitator dyads met separately to coordinate logistics. Facilitators were encouraged to review the facilitation guide  Fellowshipon guide  and all 16 informed small-group discussions. All students read (a) a short article about racial bias in pulse oximetry measurements14 and (b) either an article advocating replacement of estimated glomerular filtration rate (eGFR) race-based equations with equations omitting race and including creatinine and cystatin C15 or an article highlighting racial bias in utilization of interventional cardiac procedures.16 Students summarized the main points of the second paper they read for their peers. Dividing prereadings fostered peer teaching and minimized student preparation workload.Student prereadings17 a clinical tool designed to aid provider identification of structural contributors  to health and disease. Faculty facilitators provided additional suggestions based on their own clinical experience for how to assess for and overcome structural barriers to care.The session agenda content can be found in 18 Additionally, students discussed disparities in detection of hypoxemia on skin of color, centering on the prereading about racial bias in pulse oximetry measurements.14 Facilitators asked students to brainstorm solutions they might utilize in a clinical environment using the information and data presented.After this initial section on history-taking, the next section focused on the physical exam. Students discussed ways in which historical and current systemic biases could impact practitioners’ approach to the physical examination. In particular, the session focused on the dermatological exam and introduced students to an optional resource for diagnosing dermatological findings on skin of color: VisualDx.15 or interventional cardiology disparities.16 Again, groups pondered anti-racist clinical solutions to promote health equity and eliminate health disparities.The next part of the workshop was about clinical decision-making. In breakout groups, students discussed the article they had read on either eGFR19 students considered the use of patient identifiers in presentations and notes. They then focused specifically on language and the transmission of bias using a 2018 study11 that looked at stigmatizing versus neutral language in a clinical vignette about a patient with sickle cell disease. The study found that stigmatizing language was associated with more negative attitudes toward the patient and less aggressive pain management. Students were given the clinical vignette with stigmatizing language and wrote a corrected version using neutral language in small groups.The session ended with discussion and skills practice concerning oral presentations and note-writing. Using guidelines from the University of Washington,20 Approval for this study was obtained from the Columbia University Medical Center Institutional Review Board.We created a retrospective pre/post survey utilizing Likert-type questions based on the learning objectives to measure participants’ reaction to and learning from the session . With thp values using the McNemar statistical test with α = .05. We conducted the analyses in R 4.2.2 .We analyzed the survey data using a binary dependent variable of agree or disagree for each survey question and 2 × 2 contingency tables. We calculated Forty second-year medical students participated in the workshop. Thirty-one students (78% of participants) completed the retrospective pre/post survey at the end of the session, including 13 (33%) who completed the optional open-ended questions. The survey consisted of 20 Likert-type questions (10 presession and 10 postsession) and two optional open-ended questions.p < .001), (2) describe an alternative method for calculating eGFR that does not include race (p = .02), (3) explain how a patient's race affects the utilization of cardiac procedures (p = .003), (4) describe steps to promote equity in clinical decision-making and improve access to cardiac procedures (p < .001), and (5) explain best practices in utilizing patient identifiers in presentations and notes  could be used to strengthen assessment of learning objectives. Retrospective pre/post surveys are used to avoid response-shift bias and prevent students from overestimating their knowledge before an event. However, they do introduce recall bias  and response shift bias (desire to demonstrate growth leading to an underestimation of prior level). Those who wish to adapt our workshop could consider doing both a true pretest and a retrospective pretest to better measure and understand growth.Educating the next generation of clinicians to provide inclusive care that mitigates the effects of systemic racism and bias in medicine is critical to reducing health disparities that cause exaggerated death and disability within historically marginalized populations. Our workshop addresses some of the most glaring clinical areas that lead to disparity. This session has been incorporated into our medical school's new longitudinal health equity and justice curricular thread and serves to translate preclerkship foundational knowledge into crucial clinical skills. We believe this session would also be a strong addition to educational efforts at the graduate and continuing medical education levels with tailoring toward clinical specialty. In particular, training in modifying stigmatizing language into neutral language that honors patient stories and recognizes social determinants of health is critical and applicable at every level of training and in every medical specialty. Other future directions include incorporating interdisciplinary team members, such as social work and nursing, in educational efforts and skill development around mitigating racism and bias in patient encounters.Presentation.pptxFacilitation Guide.docxStructural Vulnerability Assessment Tool.docxSurvey Questions.docxAll appendices are peer reviewed as integral parts of the Original Publication.


A standardized pathology management tool for melanocytic skin lesions may improve patient care by simplifying interpretation and categorization of the diverse terminology currently extant.To assess an online educational intervention that teaches dermatopathologists to use the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx), a schema collapsing multiple diagnostic terms into 5 classes ranging from benign to invasive melanoma.N = 149) from 40 US states participated in a 2-year educational intervention study (71% response rate). The intervention involved a brief tutorial followed by practice on 28 melanocytic lesions, with the goal of teaching pathologists how to correctly use the MPATH-Dx schema; competence using the MPATH-Dx tool 12-24 months postintervention was assessed. Participants’ self-reported confidence using the MPATH-Dx tool was assessed preintervention and postintervention.Practicing dermatopathologists (P = .0003). During the intervention, participants used the MPATH-Dx tool correctly for 90% of their interpretations; postintervention, participants used the MPATH-Dx tool correctly for 88% of their interpretations.At preintervention, confidence using the MPATH-Dx tool was already high, despite 68% lacking prior familiarity with it, and confidence increased postintervention (Future research should examine implementing a standardized pathology assessment schema in actual clinical practice.Dermatopathologists can be taught to confidently and competently use the MPATH-Dx schema with a simple educational tutorial followed by practice. Although international standards already exist for melanoma, the use of this tool could facilitate greater consistency and higher-quality research into management approaches for benign melanocytic lesions. In 2014, the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) reporting schema was developed to categorize diagnoses of melanocytic skin lesions by mapping the many diverse diagnostic terms into 5 classes, each with corresponding risk assessments and treatment recommendations.,,The scheme includes melanoma but is expected to be more useful for benign lesions, for which standardized recommendations do not exist, with some limited exceptions.The teachability of the MPATH-Dx schema has not yet been studied in a sample of practicing dermatopathologists. We developed a simple online MPATH-Dx educational tutorial to train pathologists, provided participants with an opportunity to practice what they had learned, and evaluated the effect of this educational intervention on the confidence and correct use of the MPATH-Dx tool. We hypothesized that pathologists would gain confidence and sustained competence using this tool due to this educational intervention.,N = 226) were invited to enroll in the study between July 2018 and July 2019. Data collection continued through May 2021.The educational intervention took place within the larger Reducing Errors in Melanocytic Interpretations study, which has been described in detail elsewhere.All procedures were Health Insurance Portability and Accountability Act (HIPAA) compliant, and approval was obtained from the Institutional Review Boards of the Fred Hutchinson Cancer Research Center in Seattle, WA, and the David Geffen School of Medicine at the University of California Los Angeles. Participants who completed all aspects of the study received up to 25 AMA Category 1 Continuing Medical Education Credits.Cutaneous melanocytic lesions from the shave, punch, and excisional specimens were gathered from a pacific northwest laboratory and then prepared onto hematoxylin and eosin stained glass slides. Cases were selected using stratification based on patient age and medical chart documentation of the original diagnosis.in situ, repeat excision with ≥5mm but <1cm margins), class IV  and class V .The MPATH-Dx schema has been described in full detail elsewhere.The MPATH-Dx Educational Intervention Study  includedParticipating dermatopathologists completed a brief online preintervention survey, which gathered demographic information and queried participants on their familiarity with the MPATH-Dx classification tool. Participants were asked “A 2014 publication in the Journal of the American Academy of Dermatology by Piepkorn et alhttps://data.mendeley.com/datasets/cxkv26n853/1). Participants reviewed a 30-minute online tutorial on all MPATH-Dx classes (class I -benign through class V -invasive melanoma), including instructions on how to map their diagnostic interpretations into these classes, followed by case examples and exercises to ensure understanding.During the one-week window of their choosing, participants were mailed a hard copy of the MPATH-Dx schema to aid in their learning and for reference during the tutorial and slide reviews  at a convenient one-week window of their choosing, participants were mailed a slide set of 28 cases along with another hard copy of the MPATH-Dx tool. Of the 28 cases, 18 were identical to cases interpreted in phase 1, and 10 were new cases. Using identical methods to phase I interpretations, participants documented their interpretations for each case and mapped their diagnoses using the MPATH-Dx schema, as described above. The slide review served to assess retention of the correct use of the MPATH-Dx schema.Following completion of their phase 2 slide review, participants completed an online postintervention survey. Participants were asked “Now that you have completed the Reducing Errors in Melanocytic Interpretations Study and CME, how confident are you in using the MPATH-Dx classification tool?” on a 6-point scale ranging from “Not at all confident” (1) to “Very confident” (6).Of 226 invited dermatopathologists, 160 consented and completed the preintervention survey (response rate = 71%), 151 of those completed the educational intervention, and 143 of those completed all components of the study, including the postintervention assessment . At the phase I slide set interpretations, immediately after viewing the tutorial, participants correctly used the MPATH-Dx tool for 90% of their interpretations overall. At phase II slide set interpretations, 12-24 months after initially completing the educational tutorial, participants still correctly used the MPATH-Dx tool for 88% of their interpretations. Competence using the MPATH-Dx classification tool is shown for both phase I and phase II assessments in At the preintervention survey, 68% of participants reported a lack of familiarity with the MPATH-Dx schema (or were unsure), and yet after reading a summary of the schema, their self-reported perceived confidence in being able to use the MPATH-Dx classification tool was already high In a study of diagnostic terminology used in current practice, it was found that pathologists use a wide range and diversity of terminology when interpreting melanocytic neoplasmsOur prior work documented that dermatopathologists recognize the value of using a standardized lexicon of terminologies for melanocytic skin lesions and express a willingness to embrace such a scheme in their clinical practice.This study had several limitations. With survey research, there is always the possibility of socially -desirable responses. However, confidence ratings are difficult or impossible to ascertain by a mechanism other than self-report. We did not gather data on participants’ time spent practicing using the MPATH-Dx tool in clinical practice between phases I and II, nor did we require such practice. Continued practice during the wash-out period between phases I and II may have increased their mastery of the tool to an even greater degree, but we did not gather the data to address that possibility. We also did not gather data on what classification systems, if any, participants were using in practice before this study. This study was performed outside clinical practice and limited to US dermatopathologists only; future research on the implementation of a standardized taxonomy within the context of actual clinical practice will be necessary, as well as including international practicing dermatopathologists. It is possible that some participants misunderstood the histology form question “Which MPATH-Dx class would you classify this case into,” and mistakenly interpreted the question as MPATH-Dx class selection being their own prerogative despite the fact that we referred them to the hard copy of the MPATH-Dx (diagnostic-treatment mapping) classification tool. Finally, the MPATH-Dx schema was recently revised into an MPATH-Dx 2.0 version, whereby most moderate atypias do not require a re-excision.The most important study strength is the long-term follow-up of participants after they were involved in the educational intervention. Most educational intervention studies in pathology have been described as having poor experimental design, including fewer than 100 study participants, lacking data collection among multiple institutions, and failing to provide statistical significance.We propose that the implementation of a management-related pathology assessment schema in routine practice may provide a robust tool for standardized diagnostic reporting of melanocytic lesions and management of patients. This functions as a supplement to the extant user-generated diagnostic nomenclatures and taxonomy, with the goal of benefiting both health care providers and patients. Such a schema is only useful if dermatopathologists can be taught to use it correctly. The findings herein demonstrate that when the field is poised to implement a standardized management-related system, dermatopathologists can be taught to use the MPATH-Dx reporting schema correctly with a simple educational tutorial followed by practice.None disclosed.


Olfactory groove meningiomas (OGM) are a skull base neoplasm that represents between 8 and 13% of all intracranial meningiomas. Approach selection focuses on achieving frontal lobe decompression, gross total resection and vision preservation. Recently, there has been a focus on olfaction and considering its preservation as a quality-of-life outcome measure. An electronic search of the databases Medline, Scopus, Embase, Web of Science and Cochrane library databases was performed and data extracted according 2020 Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) statement. Six articles were selected for inclusion mainly based due to reporting quantitative outcomes for olfaction assessed by a smell identification test (e.g. sniffin’ sticks). Objective olfaction preservation can be achieved with a variety of surgical approaches. More research which includes objective assessment of olfactory function and ideally as well QoL outcome measures is needed to further optimize the treatment pathways in OGM patients. Olfactory groove meningiomas (OGM) are a skull base neoplasm that arises from the lamina cribrosa and frontoethmoidal suture. They represent between 8 and 13% of all intracranial meningiomas . When syThe goal of this article is to systematically review the available literature examining olfaction outcomes in patients with olfactory groove meningiomas and recommend a strategy to approach these tumours.A search strategy was devised according to the 2020 Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) statement. An electronic search of the databases Medline, Scopus, Embase, Web of Science and Cochrane library databases was performed from January 1996 until 26th of May 2023. To identify articles investigating olfactory outcomes in olfactory groove meningioma resection, the following search terms were applied: (((olfaction OR smell) AND (olfactory groove OR anterior skull base)) AND (meningioma)) AND (outcome) with prior checking in the MeSH database to include synonyms.The database search was further supplemented by a search of the reference lists of included studies as well as checking the related article function provided by each database. Titles and abstracts were screened to identify potentially relevant studies. All potentially relevant articles, or articles where it was unclear based on the abstract, were assessed by reviews of the full-text articles.Articles were eligible if they (1) included only anterior skull base meningiomas, (2) reported outcomes for olfaction assessed by a smell identification test (e.g. sniffin’ sticks), (3) presented original data on patients and (4) included patients who had surgery as primary management. Studies were excluded when (1) results did not specifically detail the surgical outcomes for olfactory groove meningioma resection and (2) did not demonstrate systematic assessment of olfaction pre-operatively and post-operatively.All data was reviewed independently by 2 authors (NC and DH), and discrepancies were cross-checked in a consensus meeting.The following data was obtained from the included studies: mean age, gender, number of patients, tumour size, peritumoural oedema, pre-operative olfaction assessment, visual assessment, type of approach, degree of resection, post-operative olfaction in patients with normal olfaction pre-operatively and visual outcome in patients with abnormal vision pre-operatively.We used a modified quality assessment tool incorporating the Cochrane Collaboration tool to assess the methodological quality of the included articles . The quaFrom the literature search, 444 articles were identified through searching Medline, Scopus, Embase, Web of Science and Cochrane library databases  as primary management for OGMs is an alternative management to surgical resection in appropriately selected patients. An international, multicentre study  reportedUltimately, more focused research into olfactory outcomes for the different management modalities needs to be undertaken. Routine objective assessment of patient’s olfaction will then allow a better understanding about how changes to olfaction may affect a patient’s quality of life.The main limitations of the included articles are related to the variable reporting of outcome variables as well as variation in the way the outcome is reported. This makes direct comparisons between articles difficult.We have demonstrated that objective olfaction preservation can be achieved between a variety of surgical approaches. More research which includes objective assessment of olfactory function and ideally as well QoL outcome measures is needed to further optimize the treatment pathways in OGM patients.


Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards. Nevertheless, a standard approach to triaging patients with positive test results to optimize treatment delivery has not been established. This is in part due to the wide variation in symptom severity among patients and the paucity of recent clinical data on patients available at the time of diagnosis. Therefore, it is crucial to predict severe patient outcomes once a positive test result has been obtained. In addition, it has been demonstrated that COVID-19 healthcare outcomes are disproportionately more devastating in traditionally underserved populations4. Understanding how data-driven solutions to healthcare delivery optimization affects these populations is vital to the equitable delivery of potentially life saving care.COVID-19 testing is now commonplace, and has become a requirement for many public activities. It has been demonstrated that early treatment of COVID-19 positive decreases risk of serious adverse events5. In addition, we generate a robust rank of clinical feature importance to provide clinical insights into the factors most influential to catastrophic outcomes. Specifically, we use explainable AI techniques to rank features by their importance across seven different conventional machine learning models and four variants of a deep learning model. A final feature ranking was generated using an average weighted rank of each feature across all models weighted by model performance, measured by area under the receiver operating curve (AUROC).To address this ongoing challenge, we generated an interpretable AI workflow to predict individualized risk for death and ventilator use using data available at the time of COVID-19 diagnosis. To support our predictions, we visualize the importance of patient-level features using SHapley Additive exPlanations (SHAP)To further elucidate the factors affecting patient outcomes, we sought to identify the best unsupervised clustering approach to separate patients with severe outcomes from those without. Specifically, we clustered patients using three conventional approaches  and a novel deep learning-based clustering technique. For each conventional clustering method, we generated 2, 3, 5, and 10 clusters with five different feature sets . To compare clustering approaches, we used normalized mutual information (NMI) to measure the ability of each approach to separate patients with severe outcomes from non-severe outcomes.6. Our major contributions in this work include:We developed an interpretable clinical decision support system for patient risk assessment.We demonstrated equitable model performance across communities most severely affected by COVID-19.We generated meaningful clinical insights using robust feature importance rankings and clustering approaches to identify novel biomarkers for patient outcomes.We facilitated clinical deployment of our optimized model via a user-friendly web application.Finally, we establish the equity of our model across race and sex by comparing differences in performance of our optimized classifier within patient populations in our holdout testing dataset, as shown in Fig. 7. Recent studies have leveraged machine learning to derive and validate risk prediction algorithms via electronic health records (EHRs) in order to estimate the risk of COVID-19-related adverse events, such as ICU readmission and mortality.Timely risk assessment of COVID-19 patients can significantly improve the quality of patient care and in-hospital resource allocation8 proposed conventional machine learning tools to predict 10-day mortality based on the blood sample data of 485 infected patients in order to support decision-making and logistical planning in healthcare systems during pandemics. The machine learning model achieved greater than 90% accuracy, and several positive results from external validation11 demonstrated the effectiveness of the proposed model in early and accurate risk assessment for COVID-19 patients. Kuanr et al.12 used a patient similarity-based approach to develop a patient recommendation system. Fu et al.13 proposed a risk prediction model using logistic regression based on laboratory findings for the early identification of high-risk hospital admission patients and achieved an AUROC of 84% during external validation. In addition, Barda et al.14 proposed a hybrid methodology to develop a baseline severe respiratory infection risk prediction model and a post-processing algorithm to calibrate the predictions to reported COVID-19 mortality risk using population-level data. Similarly, several recent studies30 also took advantage of conventional machine learning methods  to develop a machine learning based early warning system enabling mortality or hospital admission risk prediction for COVID-19 patients.Yan et al.32; however, there are still several limitations preventing the widespread adoption of risk prediction models in clinical practice. First, compared to conventional machine learning algorithms, fewer studies33 have been conducted on COVID-19 risk prediction based on deep learning methods, which have the potential to achieve better performance due to their enhanced capability to perform representation learning. Second, model transparency is one of the most significant obstacles preventing clinicians from comprehending and implementing black box models  in clinical practice34. Few existing studies tried to interpret prediction models by identifying potential important clinical features using tree-based32 or SHAP feature importance33. In addition, none of the aforementioned studies have considered the issue of model fairness to achieve equitable model performance across the most severely affected communities. Finally, without integrating a prediction model into a functional web application with a user interface, it is extremely difficult to promote the adoption of the technology among non-technical users. Few existing studies36 have taken user interface into consideration to provide real-time progression prediction and risk calculation for policy decision-making and intervention guidance.Existing studies have demonstrated that early warning risk prediction models play a significant role in the allocation of scarce hospital resources37.During the COVID-19 pandemic, AI-enabled clinical decision support became an integral part of the diagnosis, triage, and treatment processes. Gender, racial, and other ethnic disparities in healthcare have been well-documented in terms of COVID-19 infection, hospital admissions, and in-hospital outcomes38. Existing studies have also highlighted racial disparities through the non-representative morbidity rate, hospitalization risk, and mortality rates among Black and Latinx populations41. In addition, African Americans are at a greater risk for COVID-19 due to the higher prevalence of comorbidities, such as diabetes and hypertension39. Furthermore, Black people and other minority groups are more susceptible to both the acute and long-term effects of COVID-19 due to a lack of access to adequate healthcare services42. As a result, identifying gender- and race-specific clinical bio-markers is important for the development of risk prediction tools to combat systemic healthcare disparities for racial and ethnic minorities43.According to COVID-19 inequity and disparities studies, different gender groups have different levels of risk associated with COVID-19 infection and mortality, with males having a higher COVID-19 death rate across all age groups44 introduced an adversarial training framework for rapid COVID-19 diagnosis to mitigate demographic biases that acquired from data collection and magnified during model development. In risk prediction for other diseases, Do et al.45 proposed a joint fairness model based on logistic regression models for binary outcomes that estimates group-specific classifiers with a joint modeling objective function integrated fairness criteria. In addition, Pfohl et al.46 established a counterfactual fairness reasoning model that extends the group fairness criterion of equalized odds to provide a trade-off between maintaining fairness and performance.Consistent model performance across demographic populations is important for optimal and fair patient care decision making. However, it is challenging to achieve robust model performance in underrepresented populations due to the large data requirements of cutting-edge models. To solve this problem, Yang et al.During the data collection phase, our patient cohort contains a robust representation of patients from the Atlanta metropolitan area in order to promote model equity and reduce disparities in COVID-19 risk prediction. During the stage of model development, we examine the impact of significant characteristics on the output of our models for potential bio-maker exploration, including those associated with sex and race, as well as clinical characteristics of particular importance. In addition, we analyze biases in additional features that may have contributed to the effect of these significant features in order to provide a fair machine learning model for COVID-19 risk prediction. Our research on individual equalized risk prediction could also be extended to future disease risk prediction beyond COVID-19 in order to achieve a robust and consistent clinical decision support.Our dataset consisted of de-identified data from 17,806 COVID-19-positive patients containing 71 clinical features from across the Emory University Hospital System sites in Atlanta, Georgia, USA  of Emory University, Atlanta, Georgia, USA granted the ethics approval in 2020 (Protocol STUDY00001408). All experiments were performed in accordance with relevant guidelines and regulations; informed consent was obtained from all participants. All research data are de-identified and securely stored. Data access is limited to approved study personnel.47, Gaussian Naive Bayes, Support Vector Classifier (SVC)48, Random Forest49, and XGBoost50. Specifically, we performed hyperparameter tuning independently for each model using grid-search and five-fold cross-validation on the training dataset. Throughout this process, we developed a total of 216 variations of the aforementioned conventional models. The best hyperparameter set for each model was chosen as the set producing the highest mean AUROC score across the five-folds. Hyperparameter tuned models were used for all subsequent analyses.Risk scores within the range  indicating a person’s susceptibility to catastrophic outcomes from COVID-19 infection were first calculated for all patients using seven conventional learners: Logistic Regression, K-Nearest Neighbors (KNN), AdaBoost6. Thus, we used the developed common core architecture in our deep learning classifier models. Expanding upon this work, we developed and tested 44 different variations of this deep learning model framework to further optimize two hyperparameters using the training dataset: the activation function for the penultimate layer and the depth of the penultimate layer. Specifically, activation functions for the penultimate layer included ReLU, Sigmoid, Softmax, and Gumbel-Softmax; tested sizes for the depth of the penultimate layer included 2, 3, 4, 5, 8, 10, 15, 20, 30, 50, and 100 neurons. Optimal values for the core architecture, such as overall model depth and sizes for layers other than the penultimate layer, were determined by analyzing the model performance (AUROC)6. Early stopping with patience of 100 was used to stop model training when testing AUROC failed to improve after 100 epochs. Class imbalance effects were minimized by balancing each training epoch to ensure the model would be exposed to the same amount of data from each class. Specifically, during each epoch, we trained the models on all data points from the minority class (catastrophic outcome) and a random sample of observations from the majority class (no catastrophic outcome). The size of this random sample was equal to the size of the minority class. We utilized this technique to prevent our models from developing a bias in favor of the majority class.Deep learning models were trained on the training dataset. Giuste et al. demonstrated a process of optimizing a neural network architecture including the number of fully connected layers, the depth of each layer, and the intensity of each dropout layer5. Average SHAP values across training and validation patients were used to rank the features by importance for each conventional and deep learning model variant. We obtained the feature rankings for each conventional model. These feature rankings were weighted by model performance and averaged across models to obtain a list of the top ten most important features across conventional models and deep model variants.SHAP feature importance values were calculated for each conventional learner to identify the clinical features that were most influential in generating risk score predictionsThe original feature rankings of all high performing conventional learners and deep learners were also weighted by model performance and averaged to generate a third table demonstrating the top ten most important features across conventional and deep learning models.After training, the features generated in the penultimate layer of each deep learning model were obtained and used for clustering. The generated deep features were of special interest due to their potential to separate patients into groups of different (high vs. low) risk scores more distinctly than existing clinical features. We therefore compared the distribution of patients qualitatively and quantitatively using clinical and deep feature spaces to identify meaningful patient clusters to generate clinical insights.51, which provides a lower-dimensional projection of high-dimensional feature spaces for visualization of observation clusters. We first generated a baseline t-SNE plot of points using only the preprocessed clinical feature space, followed by several additional t-SNE visualizations of the deep feature spaces for each deep learner. Points in each plot were colored according to whether their corresponding patients had a low or high risk of catastrophic COVID-19-related outcome based on ground-truth labels. This approach allows the qualitative analysis of potential clusters and their relationships with our primary outcome of interest .We first sought to qualitatively analyze how patients were distributed in the clinical feature space as compared to the deep feature space. We did so using T-distributed Stochastic Neighbor Embedding (t-SNE) plotsFollowing t-SNE plot generation, we sought to quantitatively rank the quality of deep feature clusters in relation to clinical feature clusters. To generate and compare sets of clinical feature clusters, we used three clustering algorithms including K-Means, Agglomerative, and Spectral clustering with number of clusters (K) equal to 2, 3, 5, and 10. To generate deep feature clusters, we used the same three clustering algorithms on the feature spaces corresponding to three of our four deep learning variants . Our Gumbel-Softmax approach automatically assigns each patient to a single cluster, where each cluster is represented as a dimension of the deep learning feature space. Therefore, this approach does not require independent feature generation and clustering approaches. Altogether, 37 sets of deep feature clusters were generated in total.52 of each set of clusters with the known feature indicating catastrophic outcome. Higher NMI scores for a clustering approach corresponds to better separation of patients based on this outcome of interest. A high NMI of clusters with our primary outcome suggests effective separation of patients by meaningful clinical endpoints, lending credibility to our unsupervised approach. We also compared NMI scores of the deep feature clusters to MI scores of the clinical feature clusters to test the hypothesis that the utilization of our novel deep learning feature spaces may better separate low- and high-risk patients compared with clinical features.To quantify clustering performance, we calculated the normalized mutual information (NMI)Having ranked all cluster sets based on NMI score, we then viewed the five best-performing clustering results in greater detail by generating bar plots that illustrated how low- and high-risk patients were segregated within the clusters. We compared our observations to similar bar plots illustrating patient distributions within the five best-performing clinical feature clusters, visually comparing the compositions of cluster sets to make observations concerning the quality of deep and clinical feature clusters.We sought to confirm that our models achieved similar levels of performance across different sexual and racial groups. We first analyzed our XGBoost classifier trained on the entire feature set. To analyze the differences between sex groups, we calculated the performance of the model using the AUROC score for males and females. To analyze the differences between race groups, we calculated the performance of the model using the AUROC score for African Americans, Asians, Caucasians, Hispanics, and unlabeled patients. We performed this same process for the XGBoost classifier trained on just the top ten features determined previously. We also performed this same process for the optimized deep learner (ReLU variant) trained on the full feature set and the optimized deep learner trained on just the top ten features. This approach allowed us to determine if decreasing the number of input features during model training would affect the bias of model performance.Finally, we tested our ReLU model variant and XGBoost model on our holdout (e.i. test) dataset (20% of the preprocessed dataset). We also compared the test-set performance of our trained models on the full feature set as well as those trained on just the top ten feature set. The feature subsets were determined previously using training data only. The degree of importance attributed to the top features was then investigated. We created eleven subsets of the original processed dataset. The first feature subset included only the top ten most important features. Each of the other ten subsets included the top-10 features, iteratively excluding one of the top ten features , for a total of 9 features each. The conventional and deep learners were retrained using the previously outlined procedures with each feature subset. AUROC values were collected and compared to the original AUROC values. A Kruskal–Wallis H-test test was used to determine whether each feature subset performed significantly differently from the original processed dataset using results obtained from all models (each observation was the test-set AUROC of a single trained model).30. Our 4 deep learning model variants, which differ by their penultimate activation function, performed similarly to conventional models, with the exception of the Gumbel Softmax variant. This was not unexpected as the Gumbel Softmax activation layer one-hot encodes the data, which greatly decreases the information available for the final classification layer. Despite this significant reduction in granularity, the Gumbel Softmax variant still achieved AUROC of 81.91% while automatically assigning each patient to a mutually exclusive cluster. To the best of our knowledge, this is the first work using Gumbel Softmax to enable deep-learning clustering and classification within the same model.Out of our best 7 conventional conventional learning models tested on the testing dataset, XGBoost, Random Forest, AdaBoost, and SVC all obtained AUROC scores over 87% Fig.  and weigXGBoost and our ReLU neural network were chosen as representative conventional and deep learning models (respectively) for testing on our holdout dataset Fig. . In addi55. Estiri et al.53 reported a mean AUROC score of 0.898 using generalized linear model with gradient boosting on mortality prediction with electronic health records of 16,709 COVID-19 patients. Sottile et al.54 presented a stacked generalization model for mortality prediction on COVID-19 patients using EHR data, achieving 0.94 AUROC score, outperforming the baseline models based on Charlson Comorbidity Index (0.72 AUROC score) and sequential organ failure assessment (SOFA) (0.90 AUROC score). Therefore, our best model achieves state-of-the-art results for risk analysis.We examine the sensitivity and specificity of our deep learning (ReLU) model trained on only the top 10 clinical features across model risk prediction probabilities to allow end-users to customize threshold for their specific clinical setting . The sensitivity and specificity of our model are identical (both 81.93%) when predicted risk for catastrophic outcome is equal to 64.2%. If additional sensitivity is required, as may be the case when treatment is cheap and effective, a risk threshold of 28.8% may be used to achieve a sensitivity of 90.34% and specificity of 71.14%. We achieved similar AUROC score to those reported in peer-reviewed literature56. The presence of these features likely demonstrate a patient profile which is already suffering from chronic disease, which results in an increased COVID-19 burden on their health. Along with CHF, dementia, and kidney disease are also top important features within our deep learners. It may be interesting to note that our explainable AI approach identified count of prior admissions as an important feature within deep learning models, but not in our conventional learners. Count of prior admits may be increased in patients with chronic disease, which would be consistent with the chronic diseases identified by both models.The presence of hypertension, age, congestive heart failure (CHF), diabetes, and dementia are highly ranked after calculating their weighted average rank across conventional models. It has been well established that there exists a strong relationship between kidney disease and CHF57. Results of our statistical analysis of models trained on feature subsets indicate that using top-10 features does not significantly decrease model performance (p = 0.1228), yet removing additional top-10 features, including CHF, dementia, count of prior admits, and MI, demonstrate significantly reduced performance . These results supports the use of a much reduced input feature space to facilitate clinical integration by reducing the burden of searching for features within patients’ EHRs and manual data entry (e.g. finding and entering 10 features is much more convenient than 56 features spread across EHR locations and patient visit dates). Many of the features included within the top-10 rankings do not require additional blood tests or imaging. Glucose measurements can be quickly achieved using finger pricks, thus avoiding any delay in patient triage and treatment due to obtaining additional data.We were determined to understand the clinical biomarkers most important for determining catastrophic outcome risk. To achieve this goal, we used SHAP to examine the rank of each clinical feature within each model, and across model subsets . As each model generates similar, but different, feature ranks, we calculated the weighted average rank of each feature, using model performance (measured by AUROC) as the weight Table . We show53 used odds ratios with interquartile ranges to estimate the feature importance relative to mortality prediction. Age, several respiratory diseases, and cardiovascular diseases were identified as the top features associated with patient outcome. Similarly, Clark-Boucher et al.58 used Firth bias-corrected odds ratios in logistic regression model to report patient demographic and disease-related survey variables as top ranking features. Many of our top-ranking features are reinforced in these literature including patient demographics (e.g. age), cardiovascular disease , and respiratory disease (e.g. COPD).State-of-the-art works used different approaches to identify the top-ranking features in risk prediction. Estiri et al.Early differentiation of patients into subphenotypes based on their likelihood of suffering catastrophic outcomes may provide insights into the clinical spectrum of disease presentation. Visual inspection of patient similarities using the original clinical features available at the time of diagnosis results in poorly differentiated sub-populations using t-SNE visual inspection Fig. A. There Quantitative comparison of clustering approaches demonstrates that deep features produce better separation of patients by our outcome of interest compared with clinical features alone Fig. . This reThis significant enrichment of patients with catastrophic outcomes within a single cluster demonstrates the potential use of these clusters for patient triage. In addition, the three top-5 clustering approaches using Softmax variant features assigned patients into much smaller clusters with much lower prevalence of catastrophic outcomes compared with the original population. Patients within these catastrophic outcome-enriched clusters may be placed higher on the priority list for aggressive early treatment to maximize patient outcomes. In addition, patients within clusters with significantly lower percentage of catastrophic outcomes may be treated less aggressively to minimize patient discomfort and healthcare costs. It is interesting to note that our Gumbel-Softmax model variant assigned all but 14 patients to only two clusters, despite having the potential to separate patients into 5 clusters. This feature of the model may be valuable if the number of true clusters within the dataset are unknown. In this case, an upper-bound may be provided to allow the model to chose the appropriate number of final clusters. Future works to establish the robustness of this approach may be conducted prior to clinical integration.Consistent model performance across demographic populations is vital to ensure optimal decision making during patient care. Unfortunately, obtaining robust model performance within underrepresented populations is difficult due to the large data requirements of state-of-art models. Fortunately, our dataset contains a robust representation of patients within the Atlanta metropolitan area. Our top models perform consistently across racial categories, as encoded within the electronic healthcare record system Fig. . In addi59. Once a healthcare provider logs in securely and selects a patient within the EHR system, our application uses ICD-10  and SNOMED-CT  codes to automatically fill in the patient’s clinical features including: dementia, CHF, kidney disease, and COPD status; and whether or not the patient has had a prior myocardial infarction or stroke. ICD-10 and SNOMED-CT are both widely used standards in healthcare for classifying and coding conditions, diagnoses, symptoms, etc., in a systematic manner. The number of prior hospital visits and gender are also automatically extracted. This approach leverages standardized data resources further minimizing data entry time requirements. If any value is unable to be retrieved from the appropriate FHIR resource, the provider can perform manual selection. Once the information is populated, the data is automatically standardized by the application and submitted to our best model  which returns a risk assessment score  standard and complimentary SMART-on-FHIR technology, an open-source framework that provides secure access to EHRs. FHIR enables quick and efficient exchange of healthcare data based on modern web information exchange standardsore Fig. .Figure 7In this work, we demonstrate an automated approach to conducting patient risk assessment using AI which generates data-driven clinical insights consistent with published clinical and epidemiological research. Specifically, we developed a clinical decision support system which achieves state-of-art performance of 89.38% AUROC early patient risk assessment. In addition, we demonstrated equitable model performance across demographic populations. To improve model interpretation, we generated feature importance rankings and patient clustering to identify robust biomarkers for risk assessment. Lastly, we encouraged clinical testing of our work by reducing model feature requirements and facilitating electronic healthcare record system integration of a user-friendly web application. This work demonstrates the ability of AI to generate clinical insights early in the course of disease for optimizing patient treatment while encouraging usage and trust in clinical end-users.Our finding of dementia as a significant independent biomarker for severe COVID-19 clinical outcomes demonstrates the utility of our workflow in identifying data-driven patterns within clinical outcomes predictions tasks not previously demonstrated in similar prior work. We demonstrate that this significant effect is due to the presence of dementia, and not due to the association of dementia with age using both SHAP and ablation studies to support this finding. In addition, we elucidate demographic-specific feature contributions to patient-level risks to support the use of our approach where establishing trust in model performance across demographics is vital. Known limitations of our work include the use of data from a single metropolitan region. Although our results demonstrate consistent performance across demographic groups within our cohort, further testing of our approach within individual hospital systems is necessary prior to integration within clinical workflows with significantly different demographic representation.Future work will focus on expanding our test dataset to include data from additional hospital systems. We will seek to better understand if our approach to generating clinical biomarkers identifies similar patterns across sites and additional demographic groups, or if additional insight may be gained on the relationship of clinical features and disease outcomes. We will also test our approach within other clinical tasks requiring reliable risk prediction early in the disease course . We believe this work may establish a consistent approach to clinical decision support tool generation by maximizing interpretability and clinical utility.


Vascular complications associated with extracorporeal membrane oxygenation (ECMO) increase the in-hospital mortality. Perforation of the inferior vena cava (IVC) during venovenous extracorporeal membrane oxygenation (V-V ECMO) cannulation and subsequent emergency management prior to vascular surgery has rarely been reported.A 72-year-old female was diagnosed with IVC perforation caused by venovenous extracorporeal membrane oxygenation cannulation.Abdominal computed tomography venography with 3D reconstruction confirmed that the cannula tip had perforated the abdominal cavity from the conjunction of the iliac vein and IVC. As a result, the patient was diagnosed with inferior vena cava perforation.Attempts to reposition the dislocated cannula using digital subtraction angiography were unsuccessful. However, we found that ECMO could maintain a stable blood flow; therefore, we decided to keep ECMO running, and to minimize blood loss from the puncture site, we ensured adequate blood transfusion while operating V-V ECMO. Subsequently, emergency laparotomy was performed to fix the vascular lesion, and we established a new V-V ECMO circuit through cannulation of the bilateral internal jugular veins.In the case of confirmed V-V ECMO-related vascular perforation of the IVC, it is crucial to continue ECMO device operation to maintain negative pressure in the IVC and position the dislocated catheter to block the perforation site, effectively controlling bleeding. Therefore, emergency laparotomy should be promptly performed for vascular repair. Fortunately, the patient recovered successfully and was subsequently discharged.This case highlights several important lessons: When advancing a cannula, in this case, it is essential to first identify the guidewire placement to ensure proper guidance; In the event of a confirmed V-V ECMO-related vascular perforation of the IVC, maintaining negative pressure in the IVC through continued ECMO device operation and positioning the dislocated catheter to block the perforation site are crucial steps to control bleeding prior to emergency open vascular repair; After undergoing vascular repair, if ECMO support is still necessary, it is advisable to opt for a catheterization strategy that avoids previously repaired blood vessels. However, ECMO cannulation, the process of inserting tubes for ECMO, is a high-risk procedure with potential complications, including vascular complications. Common complications are thrombosis and obstruction,5 of the inferior vena cava (IVC), which is the largest vein in the body. Although there have been reports of ECMO cannulation-related IVC perforation, there is limited information on the treatment of this complication. In a case report by Yeung Ng P et al, a patient with IVC anomaly experienced complications during ECMO cannulation, resulting in IVC perforation. Unfortunately, the report did not provide details on the treatment of the perforation, and the patient ultimately died of nosocomial pneumonia and septic shock. In this report, we present a case of IVC perforation caused by V-V ECMO cannulation and discuss the challenges faced during treatment. Our goal was to identify the potential causes of perforation and explore secure and efficient management strategies for similar incidents in the future.Extracorporeal membrane oxygenation (ECMO) is a medical technique used to provide cardiopulmonary support to critically ill patients. Venovenous extracorporeal membrane oxygenation (V-V ECMO) has been proven to be effective in treating severe respiratory failure caused by conditions such as severe acute respiratory distress syndrome, making it a widely used treatment for severe cases of influenza A2 46.4 mm Hg, Positive end-expiratory pressure 12 cm H2O, Fraction of inspiration O2 100%). V-V ECMO was initiated using point-of-care ultrasound-guided cannulation with the Seldinger technique. The left femoral vein  and the right internal jugular vein  were selected for catheterization. Following vascular puncture of the left femoral vein and placement of the guidewire, the drainage cannula was advanced along the guidewire until the patient suddenly presented with frequent ventricular premature beats. The operator quickly withdrew the guidewire and retained > 30 cm in the vascular lumen. The cannula continued to advance after the ventricular premature beats disappeared. When the insertion depth reached 28 cm, the operator felt slightly increased resistance and rotated the cannula mildly, eventually advancing the cannula smoothly to an insertion depth of 40 cm. After the ECMO circuit was connected, blood flow was initiated at 2.7 L/minutes and 3000 RPM. However, after 30 minutes, the blood flow dropped to 1.6 L/minute, accompanied by a decrease in the arterial blood pressure from 137/69 mm Hg to 110/58 mm Hg. Ultrasonography revealed that the cannula tip in the left femoral vein was not properly positioned at the entrance of the IVC in the right atrium. Instead, it was located close to the outer wall of the vessel (Fig. A 72-year-old female diagnosed with severe Chlamydia psittaci pneumonia-induced respiratory distress syndrome was admitted to our department. The patient had no relevant medical or family history. Despite lung-protective ventilation and prone position ventilation, the patient’s hypoxemia did not improve and worsened on day 7. Arterial blood gas analysis revealed hypoxygen (PaO However, V-V ECMO can lead to various complications, including circuit-related mechanical issues, renal impairment, cardiovascular compromise, and hemorrhage, while cannula-related complications account for 6.6% of complications. Vascular complications can result in a 24% increase in the median hospital charge and a 34% increase in in-hospital mortality.,9 Common cardiovascular perforations due to ECMO cannulation include cardiac tamponade and superior vena cava perforation, with a high incidence in neonates and children.,12 However, IVC perforation in adults and its specific treatment are rarely documented.V-V ECMO can provide respiratory support for severe respiratory failure, making the use of V-V ECMO dramatically increasing over the years, especially after the outbreak of coronavirus disease 2019.In the present case, vascular perforation occurred during cannulation and was an iatrogenic complication. We did not reconfirm the position of the guidewire with ultrasound before advancing the cannula again, because we believed that the length of the guidewire in the vascular lumen was up to 50 cm. However, the length of the guidewire was shorter than we thought, as the drainage cannula penetrated from the conjunction of the iliac vein and inferior vena cava. We speculate that during arrhythmia, the tip of the guidewire was retracted into the cannula, and when the cannula was advanced again without sufficient guidewire guidance, the cannula tip penetrated the IVC.After confirming vascular perforation, our initial attempt to replace the dislocated cannula through digital substraction angiography failed and worsened the situation. The complex treatment process highlights the importance of continuously operating the ECMO device and immobilizing the dislocated catheter before performing emergency laparotomy. These measures create negative pressure in the IVC and minimize bleeding from the vascular perforation. Additionally, cannulation in the bilateral internal jugular veins may affect the patency of cerebral venous flow and lead to catastrophic cerebral events. In our case, the patient’s bilateral femoral vein vessels were not suitable for catheterization, and a dual-lumen bicaval cannula was unavailable; therefore, we chose the bilateral internal jugular vein to establish a V-V ECMO circuit and obtain a good therapeutic effect. When choosing the bilateral internal jugular cannula insertion sites, we must pay close attention to preventing thrombosis and recirculation and monitoring brain function during management.It is generally not recommended to establish a V-V ECMO circuit via the bilateral internal jugular veins. Inserting a drainage cannula into the right internal jugular vein can reverse the blood flow path.V-V ECMO is a valuable treatment option for severe respiratory failure. However, it is important to be aware of the potential risks associated with cannulation, as vascular complications can be life threatening and pose a challenge in terms of management. We report an iatrogenic IVC perforation arising from V-V ECMO cannulation, which highlights several important lessons: When inserting a cannula, it is crucial to ensure the proper placement of the guidewire to accurately guide the procedure. This step helps minimize the risk of complications; In the event of a confirmed V-V ECMO-related vascular perforation of the IVC, maintaining negative pressure in the IVC through continued ECMO device operation and stabilizing the dislocated catheter to block the perforation site are crucial steps to control bleeding prior to emergency open vascular repair; After undergoing vascular repair, if ECMO support is still necessary, it is advisable to opt for a catheterization strategy that avoids the previously repaired blood vessels. Establishing a V-V ECMO circuit via cannulaton in bilateral internal jugular veins requires comprehensive cannulation plan, and precise monitoring during ECMO run. This approach reduces the risk of reinjury, and ensures safer and more effective in ECMO support. By implementing these lessons, healthcare providers can enhance patient safety and outcomes when utilizing V-V ECMO for respiratory support.Writing – original draft: Xiangying Cen.Writing – review & editing: Yanzhu Chen, Yi Chen.


Nonlinear optical (NLO) imaging has emerged as a promising plant cell imaging technique due to its large optical penetration, inherent 3D spatial resolution, and reduced photodamage; exogenous nanoprobes are usually needed for nonsignal target cell analysis. Here, we report in vivo, simultaneous 3D labeling and imaging of potato cell structures using plasmonic nanoprobe-assisted multimodal NLO microscopy. Experimental results show that the complete cell structure can be imaged via the combination of second-harmonic generation (SHG) and two-photon luminescence (TPL) when noble metal silver or gold ions are added. In contrast, without the noble metal ion solution, no NLO signals from the cell wall were acquired. The mechanism can be attributed to noble metal nanoprobes with strong nonlinear optical responses formed along the cell walls via a femtosecond laser scan. During the SHG-TPL imaging process, noble metal ions that crossed the cell wall were rapidly reduced to plasmonic nanoparticles with the fs laser and selectively anchored onto both sides of the cell wall, thereby leading to simultaneous 3D labeling and imaging of the potato cells. Compared with the traditional labeling technique that needs in vitro nanoprobe fabrication and cell labeling, our approach allows for one-step, in vivo labeling of plant cells, thus providing a rapid, cost-effective method for cellular structure construction and imaging. Nonlinear optics (NLO), including multiphoton luminescence, second-harmonic generation (SHG), third-harmonic generation (THG), four-wave mixing (FWM) and coherent anti-stokes Raman scattering (CARS), have been widely applied in the biological fields since their inception ,6,7,8,9 On the other hand, although many ordered structures in cells and tissues can produce inherent NLO signals, exogenous probes are frequently used to increase the contrast or to selectively image nonsignal targets. For example, exogenous SHG probes, including SHG dye or SHG nanoprobes, can be employed to extend their application to nonsignal targets such as the lipid bilayer in cells . AlthougCompared with normal dye probes, noble metal nanoprobes are photostable, non-photobleaching, and biocompatible. Considerable research has proved that noble metal nanostructures such as silver and gold nanoparticles (NPs) exhibit various NLO properties, including two-photon luminescence (TPL) , SHG 2121,22, THAs we know, plant cell walls are mostly composed of cellulose, which are invisible to conventional NLO imaging techniques. In this work, we report for the first time an innovative approach for in vivo and simultaneous 3D labeling and imaging of a complete potato cell structure using plasmonic nanoprobe-assisted multimodal NLO microscopy. Instead of fabricating noble metal NPs outside the cell and then transporting exogenous NPs into the cells, we added a noble metal ion solution to the cells directly during the femtosecond (fs) laser imaging process. The results show that noble metal ions can cross the cell membrane easily through osmotic pressure and were rapidly reduced to plasmonic NPs in situ via a fs laser scan. The as-grown noble metal NPs were found to exhibit strong second-order  and third-order (FWM) NLO responses. More interestingly, these noble metal nanoprobes were selectively anchored onto the cell walls. With this approach, the whole structure of potato cells can be simultaneously labeled and imaged. Compared with traditional labeling techniques, our approach offers a simple and facile way for in vivo construction and the 3D imaging of plant cells.Reagents and materials. Silver nitrate (purity > 99%), gold (III) chloride trihydrate  were purchased from Sigma-Aldrich  and used without further purification. Deionized water with an electrical resistivity of 18 MΩ-cm was used for preparing the silver ion and gold ion solutions with a concentration of 0.3 mM and 3.7 mM, respectively. Fresh potatoes were cut into thin slices and fixed onto glass slides.Multimodal nonlinear optical imaging of potato cells. The nonlinear optical properties of as-prepared and noble metal NPs-decorated potato cell samples were investigated using a home-made multiphoton nonlinear optical microscopy system. A commercial Ti:Sapphire fs laser with a center wavelength of 800 nm was used as the laser source. Combined with a supercontinuum generator, two incident laser beams with tunable powers were generated. One was called a pump laser beam, which was formed via introducing an 800 nm laser beam through an attenuator and a delay line. The other was called a probe laser, which was formed via a 500 mW laser beam to generate the supercontinuum which was then filtered through a long-pass filter. The pump-probe laser beams were then collinearly focused onto the sample surfaces. A spectrometer was used for the nonlinear optical spectra measurement. The nonlinear optical imaging signals were collected using PMTs. With the application of a 390/40 nm emission filter, a 495–540 nm band-pass filter, and a 647/57 nm band-pass filter before PMTs, three different nonlinear optical images were simultaneously acquired.Fs laser-induced formation of noble metal nanoparticles. First, silver (gold) ion solutions were prepared through dissolving silver nitrate (gold chloride trihydrate) in deionized water. Second, the samples such as the fused silica substrates were immersed into a noble metal ion solution. Third, an 800 nm fs laser beam was focused onto the sample surface for irradiation and/or scanning. After fs laser irradiation of a single point, noble metal nanoparticles were formed in a short time  and nanoparticle aggregates were formed with a long-time irradiation ; after fs laser scanning once in a particular area, a uniform noble metal nanoparticle film was formed .Other characterizations. Raman analyses were performed at room temperature in a micro-Raman spectrometer . Raman spectra and mapping were collected through a 50× objective lens with an exposure time of 10 s and 1 s, respectively, at each position. Morphological characterization of the potato cells and the noble metal particles was carried out using field emission scanning electron microscopy .Potato cells mainly contain starch granules and cell walls, which makes it a simple plant cell structure for our study. Furthermore, starch granules exist in most plant cells and serve as the carbon storage; they are important starting materials for various industrial applications including biotechnology and biofuel production. The plant cell wall, which is composed of polysaccharides cellulose, hemicellulose, and pectin, acts as a semi-permeable layer that precisely permits the entry of smaller particles and inhibits larger ones. To examine the complete structural information of potato cells, common imaging characterizations such as scanning electron microscopy (SEM) and Raman spectroscopy were performed. To acquire the SEM imaging, potato samples needed to be fully dehydrated via chemical fixation due to the high vacuum chamber. Thus, the natural structure of the potato cells may be altered and structure artifact may be caused. −1. However, no obvious Raman peaks for the cell wall were observed in the same range of interest. −1, further confirming that the structure of the cell wall cannot be imaged and that only starch granules were visible. In addition, only 2D imaging can be provided via the Raman measurement; it also takes a long time (~2 h) for the mapping data acquisition. In comparison, the home-made multimodal nonlinear microscopy was capable of providing in vivo 3D structure information of biological samples with a scan depth of up to one millimeter in the period of a few minutes.Raman spectroscopy, as one of the noninvasive techniques, yields highly compound specific information for chemical analysis and has great potential for direct imaging. To reveal the intrinsic structure of potato cells, we carried out the Raman measurement on a fresh potato. To investigate the feasibility of imaging potato cell structures using multimodal nonlinear optical microscopy, we first measured the nonlinear optical spectra of potato cells. As shown in  reports . “Peak Bp−ωprobe . The relNext, we used nonlinear optical microscopy to perform simultaneous SHG-TPL-FWM optical imaging of potato cells. From the above analyses, it can be concluded that in order to realize the imaging of complete potato cells using multimodal nonlinear optical microscopy, an effective labeling approach for cell walls must be developed. In the next section, we investigated the possibility of fs laser-assisted growth of noble metal nanoparticles (NPs) as nanoprobes for cell structure labeling and imaging.+), can be reduced to silver NPs via fs laser scan due to the multi-photon absorption or thermal effect [Previous studies have shown that noble metal ions, such as silver ions  solution. It was considered that, during the nonlinear optical imaging process, noble metal ions that crossed the cell wall (middle panel) can be rapidly reduced to NPs via fs laser and selectively anchored onto both sides of the cell wall (lower panel), thus leading to the simultaneous 3D labeling and imaging of complete potato cell structures. To test this idea, two drops (~5 mL) of silver ion solution were added to a thin slice of a freshly cut potato sample (x-y plane) nonlinear optical imaging of the potato cells with a scan depth (z) of 150 μm was simultaneously acquired. The details of the revolution of the cell wall structure construction with and without adding silver ion solution was also recorded (z direction (o sample b. Then, recorded . After tirection c–e.For comparison, we first collected multimodal 3D nonlinear optical images of potato cells without adding silver ion solution c; all thTo investigate the influence of fs laser scan time on the cell wall structure labeling, the same potato sample in Compared to conventional labeling and imaging methods, there are several advantages of our proposed approach. First, we did not use traditional nonlinear dyes or nanoprobes for labeling, which suffer from photobleaching. Instead, noble metal NPs with no photobleaching effect and with strong NLO properties were used. Second, differing to the conventional cell structure modification and imaging based on noble metal NPs, where noble metal NPs were first fabricated outside the cell and then transported into the cells, we here realize the modification of the cell structure with noble metal NPs directly via fs laser scanning during imaging. Overall, our 3D labeling and imaging approach is one-step, simple, and cost-effective through imaging the cells in the noble metal ion solution without any other treatments.In summary, we have developed a simple, one-step approach to 3D plant cell structure labeling and imaging through using noble metal ion-assisted multimodal nonlinear optical microscopy. Instead of fabricating noble metal nanoprobes outside and then transporting them into the cell for labeling/imaging, we realized fs laser-assisted noble metal NP growth and in vivo labeling during the 3D imaging process in a noble metal ion solution. We found that fs laser-induced silver NPs exhibit various excellent nonlinear optical responses including TPL, SHG and FWM. In addition, these silver NPs can be selectively anchored onto the cell walls, thus enabling the simultaneous 3D labeling and nonlinear optical imaging of complete potato cell structures. Similar results have also been observed when substituting silver ions for gold ions. Thus, our method offers a new avenue for facile and in vivo 3D labeling/imaging of plant cells and other cell structures, which is very useful for future biological applications.