Developing approaches to control SARS-CoV-2 in a public hospital

The Territorial Public Health Care Company (in Italian, ASST) of the Saints Paolo e Carlo of Milano includes two large public hospitals, and several outpatients and territorial healthcare services. It employs 5642 workers. The outbreak of novel coronavirus disease 2019 (COVID-19) reached our ASST in the last week of February when a doctor in the Intensive Care Unit of the San Paolo Hospital was diagnosed with COVID-19. Our Occupational Health Unit immediately introduced measures to control the epidemic. Our approach was based on contact tracing and isolation of asymptomatic infected workers. A \u2018close contact\u2019 was defined as a person who had face-to-face contact or spent at least 15 min in an indoor environment with a positive subject (patient, colleague or relative) without any protective equipment (surgical mask). From 27 February to 23 April we tested 2907 workers (51% of the total workforce) with nasopharyngeal swabs (NPS) using rtPCR for SARS-CoV-2 detection [1,2], with positive results in 152 hospital and 33 territorial workers (3% of the total workforce). All the infected workers were asked to fill in a daily electronic data collection form for the duration of the infection. About 50% remained substantially asymptomatic for the quarantine period, which ended when the workers underwent two NPS on two consecutive days with a negative result. The time to recovery took from 12\u201347 days, with a median duration of about 30 days, which is longer than normally expected. Symptomatic workers showed only very mild symptoms; mainly loss/change of smell and taste. Four were hospitalized but none had severe or life-threatening infection. The data suggest that the \u2018active search approach\u2019 is more effective in closed communities such as groups of healthcare workers than generalized testing. We have started a retrospective survey of 100 positive workers studying symptoms, source of exposure and co-morbidities using a modified version of the \u2018WHO novel coronavirus acute respiratory infection clinical characterization data tool\u2019, administered by telephone interview. Finally, in order to prepare for future outbreaks, we are testing a novel telemedicine approach enabling us to follow quarantined workers with a digital platform with a mobile phone app that provides remote video examinations and online symptoms and health parameter checking (body temperature, oxygen saturation, etc.). The platform facilitates rapid intervention. Using this approach, we can follow a large cohort of workers with continuous monitoring. The tool may also be able to reduce the rate of patients\u2019 hospitalization. We are also comparing those with positive and negative swabs using a rapid immunochromatographic assay for the detection of IgG and IgM antibodies to SARS-CoV-2 virus in whole blood to assess potential immunity. Preliminary results are promising for IgG, even though the protective capacity of this immunoglobulin is still unknown

Nature, Data, And Power: How Hegemonies Shaped This Special Section

Systems of oppression—racism, colonialism, misogyny, cissexism, ableism, heteronormativity, and more—have long shaped the content and practice of science. But opportunities to reckon with these influences are rarely found within academic science, even though such critiques are well developed in the social sciences and humanities. In this special section, we attempt to bring cross-disciplinary conversations among ecology, evolution, behavior, and genetics on the one hand and critical perspectives from the social sciences and humanities on the other into the pages—and in front of the readers—of a scientific journal. In this introduction to the special section, we recount and reflect on the process of running this cross-disciplinary experiment to confront harms done in the name of science and envision alternatives

Trends in Precision Medicine and Pharmacogenetics as an Adjuvant in Establishing a Correct Immunosuppressive Therapy for Kidney Transplant: An Up-to-Date Historical Overview

Kidney transplantation is currently the treatment of choice for patients with end-stage kidney diseases. Although significant advancements in kidney transplantation have been achieved over the past decades, the host’s immune response remains the primary challenge, often leading to potential graft rejection. Effective management of the immune response is essential to ensure the long-term success of kidney transplantation. To address this issue, immunosuppressives have been developed and are now fully integrated into the clinical management of transplant recipients. However, the considerable inter- and intra-patient variability in pharmacokinetics (PK) and pharmacodynamics (PD) of these drugs represents the primary cause of graft rejection. This variability is primarily attributed to the polymorphic nature (genetic heterogeneity) of genes encoding xenobiotic-metabolizing enzymes, transport proteins, and, in some cases, drug targets. These genetic differences can influence drug metabolism and distribution, leading to either toxicity or reduced efficacy. The main objective of the present review is to report an historical overview of the pharmacogenetics of immunosuppressants, shedding light on the most recent findings and also suggesting how relevant is the research and investment in developing validated NGS-based commercial panels for pharmacogenetic profiling in kidney transplant recipients. These advancements will enable the implementation of precision medicine, optimizing immunosuppressive therapies to improve graft survival and kidney transplanted patient outcomes

Evaluating competitiveness using fuzzy analytic hierarchy process - A case study of Chinese airlines

[[abstract]]With the development of a national market economy, the Chinese aviation industry is now confronted with international competition. Therefore, it is necessary to research the competitive status of Chinese national aviation, as well as advice on how to enhance the competitiveness of the Chinese aviation industry. The main objective of this paper is to propose FAHP as an effective solution for resolving the uncertainty and imprecision in the evaluation of airlines' competitiveness. In this paper, we review the research of industrial international aviation competitiveness at both home and abroad, discuss a theoretical framework for the study of aviation competitiveness, establish an index system with five first-order indicators and 17 second-order indicators, set up a Chinese aviation competitiveness model based on simple fuzzy numbers from the fuzzy analytic hierarchy process, and evaluate the competitiveness of five major Chinese airlines. The results showed that this model and these indicators are scientific and practical, with a wide range of application prospects for the purpose of improving and increasing Chinese airline competitiveness in the international market. The effective approach presented in this paper is especially applicable when subjective judgments on performance ratings and attribute weights are not accessible or reliable, or when suitable decision makers are not available.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子版[[countrycodes]]US

Standards of Care

Abstract In the 1950s and early 1960s, Harry Benjamin and his colleague Elmer Belt corresponded at length about which transsexuals they would and would not approve for genital surgery. Benjamin defined transsexuality primarily through a desire for medical transition, but merely being a transsexual in this definition did not automatically result in surgical eligibility. Benjamin and Belt remained preoccupied with the possibility that transsexuals would regret their surgeries and seek legal or personal revenge, and thus their assessments of who should have surgery focused more on the possibility of a bad outcome than adherence to gender norms or classification as transsexual. The informal clinical practices they worked out to protect themselves in these early years of American trans medicine would ultimately go on to structure more formalized Standards of Care. Benjamin and Belt's fears, and their resulting decision-making processes, thus played a crucial role in the production of the category “transsexual.” Throughout their correspondence and clinical practice, the transsexual emerged as a threat to medical providers, and a subject incapable of making their own bodily decisions, needing to be protected from themselves. While assessments of gender identity and gendered behavior factored into these decisions, their decisions about who might regret transition treated gender as primarily practical and functional, and made an unshakable internal gender identity a necessary but insufficient criterion for granting a patient access to surgery.</jats:p

Binary Logic: Race, Expertise, and the Persistence of Uncertainty in American Sex Research

Between the mid-nineteenth and mid-twentieth centuries, American researchers tried to sort the living world by sex. Their efforts created more taxonomic problems than they solved, but as they encountered vast quantities of evidence that did not at all show an obvious or stable division between male and female bodies, they used sexual uncertainty to their professional and ideological advantage. This dissertation traces a network of scientists who built their own claims to expertise, as well as theories of racial hierarchy rooted in sexual difference, out of sexually unruly bodies. While much scholarship on the history of sex science describes the crystallization of categories and the increasing precision of definitions of sex, this dissertation focuses instead on ambiguities in the meaning of male and female, and in the meaning of sex itself. “Binary Logic” shows that the power to sort bodies by sex emerged not from solidified, agreed-upon parameters, or inherent bodily forms, but out of a mobile and malleable understanding of sex that enabled scientists to redefine their terms of classification at every turn. It also makes visible the constant categorical work required to make it appear that most humans and non-humans easily fit into binary male and female categories. This dissertation deploys the methods of Science and Technology Studies (STS), especially an attention to on-the-ground practices of fact-building and a refusal to take the pre-existence of discrete categories for granted, to integrate the history of sexuality, especially trans history, with histories of race, the life sciences, and clinical practice. It draws primarily on unpublished materials like scientists’ research notes, correspondence, and administrative records, alongside the published scientific and medical texts that emerged from them. The introduction pairs STS approaches to classification with pressing historiographical questions about who and what counts as the object of trans history, and develops a trans history methodology that accounts for the prevalence of people whose lack of conformity to standards of sex and gender was ultimately drawn back into normative categories rather than excluded from them. I demonstrate that method in action in the four chapters that follow. Each chapter examines a site in which sorting out sex became particularly fraught for the scientists attempting to do so. Chapter One investigates how nineteenth-century zoologists managed their encounters with animals that did not neatly fit into male and female categories even as they used the so-called natural world as fodder for claims about human racial hierarchies. Chapter Two looks at the disparate uses of sex as an analytic category at two eugenics laboratories in Cold Spring Harbor, New York, in the first decades of the twentieth century. At one lab, sex was a spectrum, and scientific tools could be used to gain more control over it for the purpose of improving human breeding; at the other, sex was a static binary, useful for tracking the heredity of undesirable traits to better weed them out. Chapter Three focuses in on the theoretical and clinical work of early twentieth-century gynecologist Robert Latou Dickinson, whose ideas about the commonality of intersex traits clashed with both his reluctance to classify any of his patients outside of womanhood and his ideas about the eugenic superiority of sexual dimorphism. Chapter Four explores one outcome of these debates and resulting anxieties about the meaning of sex: an early iteration of trans medicine in the 1950s and 1960s, in which the purported straightforward definition of the transsexual belied the anxiety of medical doctors convinced they might make the wrong choice in who to allow to transition. Close attention to how scientists reclassified bodies, redefined categorical criteria, and reconstituted what they considered sex itself at these four sites makes apparent the persistent flexibility of a sorting system of tremendous, frequently violent, social import—sex—that is often portrayed as immutable biological fact

Machine learning and deep learning approaches based on spatio-temporal information for scar detection applied to cine cardiac magnetic resonance images

LAUREA MAGISTRALELa Risonanza Magnetica Cardiovascolare (CMR) è la modalità di imaging principale per la valutazione di cardiomiopatie gravi, come la cardiomiopatia ischemica dilatativa. Il convenzionale protocollo clinico di imaging si basa sull’acquisizione di immagini usando due tipologie di sequenze CMR: le sequenze cine, che forniscono un'analisi dettagliata del movimento e dell’ispessimento della parete miocardica del ventricolo sinistro (LV) durante il ciclo cardiaco, e le immagini CMR Late Gadolinium Enhancement (LGE), che sfruttano l’utilizzo di agenti di contrasto a base di Gadolinio (Gd) (GBCA) per valutare la presenza di fibrosi e la formazione di tessuto cicatriziale nella parete ventricolare. Poiché la tossicità a lungo termine causata dall’accumulo di Gd nei tessuti biologici è ancora in fase di studio e sono ad oggi emerse preoccupazioni generali in merito alla sicurezza dei GBCA, è necessario valutare il ruolo delle immagini non contrastate nell’individuazione di tessuto cicatriziale. L'idea alla base di questa tesi è che le differenze di texture e le anomalie di contrazione dovute alla presenza di aree miocardiche affette da tessuto cicatriziale possano essere valutate con modelli di machine learning (ML) e deep learning (DL) applicati alle immagini cine non contrastate. Più nel dettaglio, in questo lavoro l'informazione di texture del frame di fine diastole (ED) è stata arricchita da informazioni dinamiche contenute all’interno delle sequenze cine. Per condensare le informazioni dinamiche sono state utilizzate le immagini parametriche, in grado di riassumere tali informazioni in un numero ridotto di frame. In questo lavoro sono state testate le immagini parametriche basate su due approcci: analisi di Fourier e segnale monogenico. Lo studio retrospettivo è stato condotto utilizzando le immagini di 150 pazienti affetti da cardiomiopatia ischemica dilatativa, acquisite presso IRCCS Centro Cardiologico Monzino: sono state messe a disposizione le sequenze CMR cine e le corrispondenti immagini LGE di ciascuno paziente, insieme al gold standard rappresentato dai contorni tracciati a mano di endocardio ed epicardio del ventricolo sinistro, oltre che l’informazione in ciascuna immagine LGE sulla presenza di tessuto cicatriziale in ognuno dei sei settori in cui il miocardio è stato suddiviso secondo le linee guida del modello a 17 settori definito dall'American Heart Association (AHA). Il dataset originale è stato filtrato per eliminare tutti i settori che non sarebbero stati elaborati correttamente a causa della mancanza di informazioni, ottenendo quindi 5088 settori sani e 1907 settori patologici. Sono stati utilizzati e successivamente messi a confronto i metodi di Random forest (RF), Support Vector Machine (SVM) e Convolutional Neural Network (CNN). In questo lavoro sono stati definiti tre diversi protocolli per classificare i settori del miocardio, ognuno con un differente dataset di ingresso per i modelli di classificazione. Nel primo protocollo (P1) i frame ED di ogni sequenza cine sono stati usati come input, basando quindi la classificazione solamente su informazioni statiche di texture e intensità. Il secondo protocollo (P2) ha combinato le immagini parametriche di ampiezza normalizzata e fase di Fourier con i settori cine statici usati in P1, con lo scopo di includere informazioni temporali relative al movimento del miocardio durante il ciclo cardiaco. Nel terzo protocollo (P3), sono state utilizzate immagini parametriche monogeniche della differenza in ampiezza e fase tra i frame di fine diastole e fine sistole al posto delle immagini di Fourier, come metodo parametrico alternativo per condensare l’informazione temporale. Tutti e tre i protocolli sono stati adottati per tutti i modelli di classificazione usati in questa tesi (RF, SVM e CNN) e per una valutazione robusta delle prestazioni è stata utilizzata una procedura di 10-fold cross validazione, assegnando ogni paziente ad un solo gruppo per l’intera procedura. Durante l’allenamento dei modelli RF e SVM l’algoritmo di classificazione è stato ottimizzato via grid-search, che considera tutte le possibili combinazioni dei parametri del modello. Per la CNN, il dataset di training è stato ulteriormente diviso nei sottogruppi di training e validation (70% e 30% rispettivamente). I risultati hanno mostrato che P2 e P3 garantiscono un miglioramento complessivo rispetto a P1. Le migliori prestazioni sono state ottenute in P2: la più alta recall è stata raggiunta attraverso l’utilizzo delle SVM (mediana = 69%, range inter-quartile (IQR) = 4%), con una corrispondente precisione del 70% (IQR = 2%), F1-score pari al 70% (IQR = 4%) e un’area sotto la curva (AUC) media del 69% (deviazione standard (sd) = 5%), mentre l’applicazione della CNN ha portato ad una recall mediana pari al 68% (IQR = 4%), una precisione del 71% (IQR = 5%), F1-score del 69% ( IQR = 5%) ed una AUC media pari al 70% (sd = 1%). Non è stata riscontrata differenza significativa (test di Tukey, p-value &gt; 0.05) tra i risultati di P2 e P3. Sulla base dei risultati ottenuti, questo lavoro ha evidenziato come l’informazione dinamica contenuta nelle immagini parametriche permetta di avere prestazioni migliori rispetto all’utilizzo dei soli frame statici ED. L’analisi di Fourier valuta l’oscillazione della video-intensità di ogni pixel durante il ciclo cardiaco e sfrutta questa informazione per la stima del movimento. D’altra parte, le immagini parametriche basate sul segnale monogenico utilizzano l’applicazione di filtri opportunamente definiti, considerando solo i frame di fine diastole e fine sistole senza richiedere ipotesi di simmetria. Il vantaggio di condurre un’analisi puntuale ha garantito al metodo basato su Fourier di raggiungere i risultati migliori, ma il segnale monogenico appare come uno strumento alternativo valido per la classificazione. Considerando gli approcci di ML, le SVM hanno superato le prestazioni del modello RF ed hanno raggiunto i risultati migliori. Inoltre, architetture complesse di DL sono state in grado di sfruttare le informazioni spazio-temporali garantendo una buona classificazione con il nostro dataset di ingresso. Questi risultati hanno mostrato come modelli avanzati di ML e DL possano rappresentare un valido approccio per la classificazione dei settori del miocardio con tessuto cicatriziale nel momento in cui si utilizzano informazioni dinamiche, e il loro utilizzo potrebbe rappresentare un passo avanti verso l’individuazione di tessuto cicatriziale in immagini acquisite senza l’utilizzo di Gd.Cardiovascular Magnetic Resonance (CMR) is the main imaging modality for the evaluation of severe cardiomyopathies, such as ischemic dilated cardiomyopathy. The conventional clinical imaging protocol is based on the acquisition of images using two types of CMR sequences: cine sequences, which provide detailed analysis of left ventricular (LV) myocardial wall motion and thickening during the cardiac cycle, and Late Gadolinium Enhancement (LGE) CMR images, enhanced by Gadolinium (Gd)-based contrast agent (GBCA), which are used to assess regional myocardial fibrosis and scar formation. As long-term toxicity caused by deposition of Gd in biological tissues is still under investigation and there are general concerns about the administration of GBCAs, the role of not contrast-enhanced images for scar tissue analysis needs to be investigated. The underlying idea of this thesis is that textural differences and contraction anomalies due to presence of myocardial fibrotic areas can be evaluated by machine learning (ML) and deep learning (DL) models applied to non-contrast cine CMR images. More specifically, in this implementation the textural information found in the end-diastolic (ED) frame was enriched by dynamical information naturally encoded in the cine sequences. Parametric images were used to condense this temporal information into a reduced number of synthetic still frames. In this work, two types of parametric images were tested: based on Fourier analysis and on monogenic signal. The study was conducted retrospectively by using the CMR images of 150 patients affected by ischemic dilated cardiomyopathy acquired at IRCCS Centro Cardiologico Monzino: for each of them the short-axis cine CMR sequences and the corresponding LGE images were available, together with the gold standard represented by LV endocardial and epicardial manually traced contours, and the information in each LGE image about the presence or not of the scar in each of the six sectors in which the myocardium is divided following the guidelines of the American Heart Association (AHA) 17-sector model. The original dataset was filtered to eliminate all the sectors that could not have been correctly processed due to missing information, obtaining 5088 healthy sectors and 1907 pathological ones. Random forest (RF), Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) were applied and their performance compared. In this work, three different protocols were set up to classify the myocardial sectors, each with a different input dataset for the classification models. In the first protocol (P1) the ED frames of each cine sequence were used as input to the models, thus basing the classification only on static information of texture and intensity. The second protocol (P2) combined the Fourier parametric images of normalized amplitude and phase with the static cine sectors used in P1, with the aim of including temporal information relevant to myocardial movement during the cardiac cycle. In the third protocol (P3), monogenic parametric images of the difference in amplitude and phase between ED and end-systolic (ES) frames were used instead of Fourier images, as an alternative parametric method to condensate temporal information. All three protocols were adopted for all classification models used in this thesis (RF, SVM and CNN), and for robust performance evaluation a 10-fold cross validation was used, assigning each patient to only one fold for the entire procedure. During the training of RF and SVM models, the classification algorithm was optimized via grid-search, which considers all combinations of model parameters. For the CNN, instead, the complete training fold was divided into training and validation subgroups (70% and 30%, respectively). The results showed that P2 and P3 guaranteed an overall improvement compared to P1. The best performances were obtained in P2: the highest recall was achieved by using SVM (median = 69%, inter-quartile range (IQR) = 4%), with a corresponding precision of 70% (IQR = 2%), F1-score equal to 70% (IQR = 4%) and a mean area under the curve (AUC) of 69% (standard deviation (sd) = 5%), while the application of CNN resulted in median recall equal to 68% (IQR = 4%), a precision of 71% (IQR = 5%), F1-score of 69% (IQR = 5%) and mean AUC equal to 70% (sd = 1%). No significant difference (Tukey’s test, p-values&gt; 0.05) was found between the results of P2 and P3. Based on these results, this work highlighted that dynamical information encoded in parametric images allowed for better performance than just using static ED cine frames. Fourier analysis evaluates the oscillation of each pixel video-intensity during cardiac cycle and uses this information as an estimation of the movement. On the other hand, parametric images based on monogenic signal exploit the application of properly defined filters, taking into account only ED and ES frames without requiring symmetricity assumptions. The advantage of having a punctual analysis allowed Fourier to guarantee the best results, but the monogenic signal appears as a valid alternative tool for the classification. Considering the ML approaches, SVM better performed for our task compared to RF and reached the best results. Moreover, complex DL architectures were capable of exploiting spatio-temporal information guaranteeing a good classification with our input dataset. These results showed that advanced ML and DL models could constitute a valid approach for classification of myocardial sectors with scar when using dynamical information, and their utilization could represent a step towards the detection of scars in Gd-free images