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Enteric fever remains a major public health problem in South and Southeast Asia. The recent roll-out of the typhoid conjugate vaccine protecting against S. Typhi exhibits great promise for disease reduction in high burden areas. However, some endemic regions remain vulnerable to S. Paratyphi A due to a lack of licensed vaccines and inadequate WASH. Several developmental S. Paratyphi A vaccines exploit O-antigen as the target antigen. It has been hypothesised that O-antigen is under selective and environmental pressure, with mutations in O-antigen biosynthesis genes being reported, but their phenotypic effects are unknown. Here, we aimed to evaluate O-antigen variation in S. Paratyphi A originating from Nepal, and the potential effect of this variation on antibody binding. O-antigen variation was determined by measuring LPS laddering shift following electrophoresis; this analysis was complemented with genomic characterisation of the O-antigen region. We found structural O-antigen variation in <10 % of S. Paratyphi A organisms, but a direct underlying genetic cause could not be identified. High-content imaging was performed to determine antibody binding by commercial O2 monoclonal (mAb) and polyclonal antibodies, as well as polyclonal sera from convalescent patients naturally infected with S. Paratyphi A. Commercial mAbs detected only a fraction of an apparently "clonal" bacterial population, suggesting phase variation and nonuniform O-antigen composition. Notably, and despite visible subpopulation clusters, O-antigen structural changes did not appear to affect the binding ability of polyclonal human antibody considerably, which led to no obvious differences in the functionality of antibodies targeting organisms with different O-antigen conformations. Although these results need to be confirmed in organisms from alternative endemic areas, they are encouraging the use of O-antigen as the target antigen in S. Paratyphi A vaccines., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Mentoring plays an integral role in the success of faculty. This study explores faculty access to formal and informal mentorship and how mentorship impacts faculty engagement. Data are from 2020 to 2023 administrations of the StandPoint Faculty Engagement Survey. We compare clinically active faculty with a PhD or other health doctorate (OHD) in departments of psychiatry and neurology (Doctoral-PN) with both faculty with an MD or equivalent degree in those departments (Physician-PN), and with faculty with a PhD or OHD in other clinical departments (Doctoral-Other). Psychologists who are active in clinical care are represented in these departments although their representation likely varies by institution. Forty-four percent of Doctoral-PN faculty received formal mentoring and 65% received informal mentoring. Those receiving formal mentoring were more satisfied with professional development opportunities and reported increased understanding of promotion than those who were not. They were also more satisfied with their department and would recommend their medical school to others. The literature to date acknowledges the challenges to professional growth and advancement faced by Doctoral-PN faculty, including psychologists, practicing in academic health centers. This paper adds to previous research by presenting data on organizational outcomes associated with mentoring for these faculty., Competing Interests: Declarations Conflict of interest Susan M. Pollart, Elza Mylona, Troy Buer, Jennifer Apps, and Valerie Dandar have no relevant financial or nonfinancial interests to disclose. Ethics Approval AAMC research using StandPoint Surveys data was approved by the American Institutes for Research. Consent to Participate The StandPoint Faculty Engagement Survey is a voluntary survey, and informed consent was obtained from all participants. Consent for Publication The StandPoint Survey disclosure describes that data may be used for research when deidentified and reported in the aggregate. Informed consent was obtained from all participants. Human and Animal Rights Statement All procedures involving human sbujects were in accordance with the ethical standards of the U.S. Office for Human Research Protections and the Belmont Report of 1979. The study was approved by the institutional review board of the American Institutes for Research., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Continual changes in organizational structures within medical schools have contributed to the expanded scope and the centralization of faculty affairs offices, which support faculty administration and supportive functions. Using qualitative interviews, we investigated the perspectives of academic medicine faculty affairs leaders regarding their offices' priorities in sustaining faculty vitality in the face of current and anticipated challenges. A semi-structured interview protocol based on the researchers' practical knowledge, informed by the study's research inquiries, and pertinent academic literature guided the interviews. Deductive thematic analysis approach was used to identify the patterns and themes across the interviews. Our analysis revealed a central theme: the pivotal nature of the leader's role in strengthening faculty identity. Additionally, three sub-themes emerged concerning the leader's role in nurturing faculty well-being within today's academic medicine context: redefining faculty role, acknowledging and appreciating faculty contributions, and maintaining faculty engagement through a whole-person approach. Faculty affairs leaders describe widening roles with an emerging focus on a whole-person approach valuing the diverse contributions of faculty across the academic mission, supporting professional development, reflecting the individual motivations of faculty, and championing institutional processes that holistically evaluate and recognize faculty contributions., Competing Interests: Declarations Conflict of Interest All authors serve as senior leaders in faculty affairs and are members of the American Association for Medical Colleges Group on Faculty Affairs. While closeness to the topic and the participants is a potential conflict, the research protocol ensured that through peer-debriefing and participant member checking, we have minimized any potential bias for this study. Ethical Approval This research study was approved by the Texas Christian University Institutional Review Board (IRB) on September 7, 2023 (IRB#2023-296). Human and Animal Rights This study adhered to all applicable ethical guidelines for research involving human participants. The research protocol was reviewed and approved by the Texas Christian University Institutional Review Board. No animals were used in this study. Informed consent All participants were over the age of 18 and informed about the nature and purpose of the study. Participation was entirely voluntary. Informed consent was obtained from all participants prior to their inclusion in the study. Participants provided informed consent to participate in the study and to have their interviews audio recorded via a Qualtrics online consent form., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Objectives: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI., Methods: Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics., Results: In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance., Conclusion: The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis., Critical Relevance Statement: This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts., Key Points: Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features., (© 2024. The Author(s).)

Background: Systematic surveillance of Clostridioides difficile infection (CDI) in our institution showed a reduction in the incidence of healthcare associated CDI (HA-CDI) during COVID-19 pandemic. Aim: Our objective was to search for factors related to this reduction. Methods: We retrospectively studied the trend of the incidences of HA-CDI, Multidrug Resistant (MDR) organisms, total antibiotic and chlorine consumptions as well as the influence of the last two on the incidence of HA-CDI. Results: During COVID-19 pandemic, the HA-CDI incidence was reduced with respect to the previous years, although total antibiotic consumption was found to increase ( p < .01). MDR organisms' incidence was found to increase ( p < .01), as well as the chlorine consumption ( p = .04) which was the only factor to be related to the decreased rates of HA-CDI (r = -0.786, p < .05). Discussion: In our institution, COVID-19 epidemic overlapped with the reduction in the HA-CDI's incidence. This could be due to faithful compliance with the contact precaution measures but then, we would expect the incidence of MDR organisms to decrease as well. Chlorine usage for environmental cleaning was generalized during pandemic. It was the only factor to be related to the decreased rates of HA-CDI, highlighting the importance of environmental cleaning as a measure for HA-CDI prevention., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving multi-modal synthetic data generation were also explored. The type of method used for the synthetic data generation process was identified in each study and was categorized into statistical, probabilistic, machine learning, and deep learning. Emphasis was given to the programming languages used for the implementation of each method. Our evaluation revealed that the majority of the studies utilize synthetic data generators to: (i) reduce the cost and time required for clinical trials for rare diseases and conditions, (ii) enhance the predictive power of AI models in personalized medicine, (iii) ensure the delivery of fair treatment recommendations across diverse patient populations, and (iv) enable researchers to access high-quality, representative multimodal datasets without exposing sensitive patient information, among others. We underline the wide use of deep learning based synthetic data generators in 72.6 % of the included studies, with 75.3 % of the generators being implemented in Python. A thorough documentation of open-source repositories is finally provided to accelerate research in the field., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Tetracyclines constitute a unique class of antibiotic agents, widely prescribed for both community and hospital infections due to their broad spectrum of activity. Acting by disrupting protein synthesis through tight binding to the 30S ribosomal subunit, their interference is typically reversible, rendering them bacteriostatic in action. Resistance to tetracyclines has primarily been associated with changes in pump efflux or ribosomal protection mechanisms. To address this challenge, tetracycline molecules have been chemically modified, resulting in the development of third-generation tetracyclines. These novel tetracyclines offer significant advantages in treating infections, whether used alone or in combination therapies, especially in hospital settings. Beyond their conventional antimicrobial properties, research has highlighted their potential non-antibiotic properties, including their impact on immunomodulation and malignancy. This review will focus on third-generation tetracyclines, namely tigecycline, eravacycline, and omadacycline. We will delve into their mechanisms of action and resistance, while also evaluating their pros and cons over time. Additionally, we will explore their therapeutic potential, analyzing their primary indications of prescription, potential future uses, and non-antibiotic features. This review aims to provide valuable insights into the clinical applications of third-generation tetracyclines, thereby enhancing understanding and guiding optimal clinical use.

Antimicrobial resistance (AMR) poses a serious threat to the clinical management of typhoid fever. AMR in Salmonella Typhi (S. Typhi) is commonly associated with the H58 lineage, a lineage that arose comparatively recently before becoming globally disseminated. To better understand when and how H58 emerged and became dominant, we performed detailed phylogenetic analyses on contemporary genome sequences from S. Typhi isolated in the period spanning the emergence. Our dataset, which contains the earliest described H58 S. Typhi organism, indicates that ancestral H58 organisms were already multi-drug resistant (MDR). These organisms emerged spontaneously in India in 1987 and became radially distributed throughout South Asia and then globally in the ensuing years. These early organisms were associated with a single long branch, possessing mutations associated with increased bile tolerance, suggesting that the first H58 organism was generated during chronic carriage. The subsequent use of fluoroquinolones led to several independent mutations in gyrA. The ability of H58 to acquire and maintain AMR genes continues to pose a threat, as extensively drug-resistant (XDR; MDR plus resistance to ciprofloxacin and third generation cephalosporins) variants, have emerged recently in this lineage. Understanding where and how H58 S. Typhi originated and became successful is key to understand how AMR drives successful lineages of bacterial pathogens. Additionally, these data can inform optimal targeting of typhoid conjugate vaccines (TCVs) for reducing the potential for emergence and the impact of new drug-resistant variants. Emphasis should also be placed upon the prospective identification and treatment of chronic carriers to prevent the emergence of new drug resistant variants with the ability to spread efficiently., (© 2024. The Author(s).)

Salmonella Paratyphi A, one of the major etiologic agents of enteric fever, has increased in prevalence in recent decades in certain endemic regions in comparison to S. Typhi, the most prevalent cause of enteric fever. Despite this increase, data on the prevalence and molecular epidemiology of S. Paratyphi A remain generally scarce. Here, we analysed the whole genome sequences of 216 S. Paratyphi A isolates originating from Kathmandu, Nepal between 2005 and 2014, of which 200 were from patients with acute enteric fever and 16 from the gallbladder of people with suspected chronic carriage. By exploiting the recently developed genotyping framework for S. Paratyphi A (Paratype), we identified several genotypes circulating in Kathmandu. Notably, we observed an unusual clonal expansion of genotype 2.4.3 over a four-year period that spread geographically and systematically replaced other genotypes. This rapid genotype replacement is hypothesised to have been driven by both reduced susceptibility to fluoroquinolones and genetic changes to virulence factors, such as functional and structural genes encoding the type 3 secretion systems. Finally, we show that person-to-person is likely the most common mode of transmission and chronic carriers seem to play a limited role in maintaining disease circulation., Competing Interests: ‘The authors have declared that no competing interests exist.’, (Copyright: © 2024 Mylona et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

Background: Enteric fever, caused by Salmonella enterica serovars Typhi and Paratyphi A, is a major public health problem in low- and middle-income countries. Moderate sensitivity and scalability of current methods likely underestimate enteric fever burden. Determining the serological responses to organism-specific antigens may improve incidence measures., Methods: Plasma samples were collected from blood culture-confirmed enteric fever patients, blood culture-negative febrile patients over the course of 3 months, and afebrile community controls. A panel of 17 Salmonella Typhi and Paratyphi A antigens was purified and used to determine antigen-specific antibody responses by indirect ELISAs., Results: The antigen-specific longitudinal antibody responses were comparable between enteric fever patients, patients with blood culture-negative febrile controls, and afebrile community controls for most antigens. However, we found that IgG responses against STY1479 (YncE), STY1886 (CdtB), STY1498 (HlyE), and the serovar-specific O2 and O9 antigens were greatly elevated over a 3-month follow up period in S. Typhi/S. Paratyphi A patients compared to controls, suggesting seroconversion., Conclusions: We identified a set of antigens as good candidates to demonstrate enteric fever exposure. These targets can be used in combination to develop more sensitive and scalable approaches to enteric fever surveillance and generate invaluable epidemiological data for informing vaccine policies., Clinical Trial Registration: ISRCTN63006567., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Klebsiella pneumoniae is a major human pathogen, because it causes both community- and hospital-acquired infections. Several multidrug-resistant high-risk clones of K. pneumoniae have been reported worldwide, and these are responsible for high numbers of difficult-to-treat infections. In Greece, a K. pneumoniae ST39 high-risk clone was detected in 2019 in a survey of carbapenem- and/or colistin-resistant Enterobacteriacae. The present study included nine carbapenem-resistant K. pneumoniae (CRKP) isolates collected during a retrospective analysis from October 2020 to December 2020. They were isolated from nine different patients hospitalized in the intensive care unit (ICU) of a hospital in Volos, Greece, and they were selected for analysis due to their phenotypic profile. In this study, we analyzed A165 strain K. pneumoniae ST39 isolated from a blood culture in November 2020. Whole-genome sequencing (WGS) was performed using Ion Torrent Platform, and resistance genes, virulence determinants, capsular types, insertion sequences, phage regions, and clustered regularly interspaced palindromic repeats (CRISPR) regions were detected by bioinformatic analysis. The molecular characterization revealed antimicrobial resistance genes, including sul2 for sulfamethoxazole; dfrA1 for trimethoprim; blaVIM-1 and blaKPC-2 for carbapenems; aac(6')-II for aminoglycosides; fosA for fosfomycin and aad1 for streptomycin, blaSHV-40, blaSHV-85, blaSHV-79, blaSHV-56, and blaSHV-89 for beta-lactams. Point mutations were identified in ompK36, and ompK37 and in acrR, gyrA, parC. Several replicons were found, including CoIRNA, IncC, IncFIB(K), IncFIB(pQiL), and IncFII(K). The capsular typing revealed that the strain was KL23, O2afg. The genome sequence of A165 was submitted to NCBI under PRJNA1074377 and have been assigned to Genbank accession number JAZIBV000000000.

Purpose: Journals have begun to expand the racial diversity of editors as a first step to countering institutional racism. Given the power editors hold as gatekeepers, a diverse team helps ensure that minoritized scholars have equal opportunity to contribute. In 2021, Teaching and Learning in Medicine ( TLM ) created an editorial internship for racially minoritized individuals. This study examines the first 6 months of this program to better understand its creation and initial successes., Method: The authors employed critical collaborative autoethnography, a qualitative methodology, focusing on the underlying assumptions around power and hierarchy that are implicit in the design and implementation of the TLM internship. Participants included 13 TLM editorial board members (10 internship selection committee members, 3 mentors, 2 independent researchers), 3 external selection committee members, and 3 interns, with some holding multiple roles. Ten participants served as authors of this report. Data included archival emails, planning documents, and focus groups. The initial analysis explored what happened and how and was followed by a thematic analysis in which participants reflected on their responsibility for implementing an antiracist program., Results: While the program developed interns' editorial skills, which they greatly valued, and diversified the TLM editorial board, it did not achieve the goal of fostering antiracism. Mentors focused on conducting joint peer reviews with interns, assuming that racial experiences can and should be separate from the editorial process, thus working within, rather than trying to change, the existing racist system., Conclusions: Given these findings, greater structural change is needed to disrupt the existing racist system. These experiences underscore the importance of recognizing the harmful impact a race-neutral lens can have on antiracist efforts. Moving forward, TLM will implement lessons learned ahead of offering the internship again with the goal of creating the transformative change intended with the creation of the program., (Copyright © 2023 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a "work of the United States Government" for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Objective: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories., Methods: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors., Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories., Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being., (© 2023 John Wiley & Sons Ltd.)

Magnetic Resonance (MR) images suffer from spatial inhomogeneity, known as bias field corruption. The N4ITK filter is a state-of-the-art method used for correcting the bias field to optimize MR-based quantification. In this study, a novel approach is presented to quantitatively evaluate the performance of N4 bias field correction for pelvic prostate imaging. An exploratory analysis, regarding the different values of convergence threshold, shrink factor, fitting level, number of iterations and use of mask, is performed to quantify the performance of N4 filter in pelvic MR images. The performance of a total of 240 different N4 configurations is examined using the Full Width at Half Maximum (FWHM) of the segmented periprostatic fat distribution as evaluation metric. Phantom T2weighted images were used to assess the performance of N4 for a uniform test tissue mimicking material, excluding factors such as patient related susceptibility and anatomy heterogeneity. Moreover, 89 and 204 T2weighted patient images from two public datasets acquired by scanners with a combined surface and endorectal coil at 1.5 T and a surface coil at 3 T, respectively, were utilized and corrected with a variable set of N4 parameters. Furthermore, two external public datasets were used to validate the performance of the N4 filter in T2weighted patient images acquired by various scanning conditions with different magnetic field strengths and coils. The results show that the set of N4 parameters, converging to optimal representations of fat in the image, were: convergence threshold 0.001, shrink factor 2, fitting level 6, number of iterations 100 and the use of default mask for prostate images acquired by a combined surface and endorectal coil at both 1.5 T and 3 T. The corresponding optimal N4 configuration for MR prostate images acquired by a surface coil at 1.5 T or 3 T was: convergence threshold 0.001, shrink factor 2, fitting level 5, number of iterations 25 and the use of default mask. Hence, periprostatic fat segmentation can be used to define the optimal settings for achieving T2weighted prostate images free from bias field corruption to provide robust input for further analysis., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier Inc.)

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation., (© 2023. The Author(s).)

Background and Purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours., Materials and Methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter., Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations., Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Pelvic organ prolapse is rarely associated with severe bilateral ureteral hydronephrosis and renal dysfunction. The etiopathogenetic mechanism has not been fully elucidated. Contemporary imaging methods of the urinary tract play a decisive role in assessing the morphological function of the kidneys. In cases of moderate and severe pelvic organ prolapse, surgery appears to be the main choice of treatment. Our case concerns a post-menopausal patient with three vaginal deliveries in her obstetric history and with a history of bilateral hydronephrosis and impaired renal function who was referred to the outpatient clinic for a gynecological examination due to complete uterine prolapse. Bilateral hydroureteronephrosis due to prolapse was assessed as the main cause of renal dysfunction. A surgical intervention was decided to the pelvic floor and a vaginal hysterectomy was performed with simultaneous correction of the cystocele and rectocele. The postoperative course was uneventful. Three months later, re-examination of the urinary tract showed complete remediation of kidney morphology and function. The present case report emphasizes the significant degree of bilateral hydroureteronephrosis and deterioration of renal function rarely seen in patients with complete uterine prolapse. At the same time, it is pointed out that the exclusion of renal dysfunction related to complete uterine prolapse should be the main concern of the modern gynecologist even for complex cases with coexisting etiological factors for renal disease, in order to avoid permanent renal parenchymal damage and ensure the best health and quality of life of these patients., Competing Interests: The authors declare no competing interests., (Copyright: Anna Thanasa et al.)

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models' predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate's gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images., (© 2023. The Author(s).)

Salmonella enterica Typhi ( S . Typhi) and Paratyphi A ( S . Paratyphi A) are the causative agents of enteric fever, a systemic human disease with a burden of 300 000 cases per year in India. The majority of enteric fever cases are associated with S . Typhi, resulting in a paucity of data regarding S . Paratyphi A, specifically with respect to genomic surveillance and antimicrobial resistance (AMR). Here, we exploited whole-genome sequencing (WGS) to identify S . Paratyphi A genotypes and AMR determinants associated with an outbreak of S . Paratyphi A in Vadodara, India, from December 2018 to December 2019. In total 117  S . Paratyphi A were isolated and genome sequenced, most were genotype 2.4.2 (72.6 % of all cases), which is the globally dominant genotype. The remainder were genotype 2.3 (25.6 %), while only two isolates belonged to genotype 2.4.1. A single base-pair mutation in gyrA , associated with reduced susceptibility to fluoroquinolones, was present in all of the outbreak isolates; with 74.35 % of isolates having a S83F substitution and the remainder having an S83Y substitution. Our surveillance study suggests that S . Paratyphi A is an emergent pathogen in South Asia, which may become increasingly relevant with the introduction of Vi conjugate vaccines.

Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.

Although several studies have utilized AI (artificial intelligence)-based solutions to enhance the decision making for mechanical ventilation, as well as, for mortality in COVID-19, the extraction of explainable predictors regarding heparin's effect in intensive care and mortality has been left unresolved. In the present study, we developed an explainable AI (XAI) workflow to shed light into predictors for admission in the intensive care unit (ICU), as well as, for mortality across those hospitalized COVID-19 patients who received heparin. AI empowered classifiers, such as, the hybrid Extreme gradient boosting (HXGBoost) with customized loss functions were trained on time-series curated clinical data to develop robust AI models. Shapley additive explanation analysis (SHAP) was conducted to determine the positive or negative impact of the predictors in the model's output. The HXGBoost predicted the risk for intensive care and mortality with 0.84 and 0.85 accuracy, respectively. SHAP analysis indicated that the low percentage of lymphocytes at day 7 along with increased FiO 2 at days 1 and 5, low SatO 2 at days 3 and 7 increase the probability for mortality and highlight the positive effect of heparin administration at the early days of hospitalization for reducing mortality.

Introduction: Our breast screening unit was identified as high outlier for B3 lesions with a low positive predictive value (PPV) compared to the England average. This prompted a detailed internal audit and review of B3 lesions and their outcomes to identify causes and address any variation in practice., Patients and Methods: The B3 rate was calculated in 4168 breast core biopsies from 2019, using the subsequent excision to determine the PPV. Atypical intraductal epithelial proliferation (AIDEP) cases were subject to microscopic review to reassess the presence of atypia against published criteria. The B3 rate was re-audited in 2021, and the results compared., Results: Screening cases had a high B3 rate of 12.4% (30% above the national average), and a PPV of 7.7% (9.7% with atypia). AIDEP was identified as a possible cause of this outlier status. On review and by consensus, AIDEP was confirmed in only 66% of cases reported as such, 17% were downgraded, and 16% did not reach consensus, the latter highlighting the difficulty and subjectivity in diagnosis of these lesions. Repeat audit of B3 rates after this extended review revealed a reduction from 12.4% to 9.11%, which is more in line with national standards., Conclusion: Benchmarking against national reporting standards is critical for service improvement. Through a supportive environment, team working, rigorous internal review and adherence to guidelines, interobserver variation and outlier status in breast pathology screening outliers can both be addressed. This study can serve as a model to other outlier units to identify and tackle underlying causes., (Copyright © 2022. Published by Elsevier Inc.)

The prevention of acid generation from sulfidic mine wastes is a problem that challenges the global scientific community for decades. A promising strategy is related to the formation of coating layer around sulfides for inhibiting surface oxidation. In the current research, the conditions favoring the formation of an efficient silicate-based coating around pyrite and arsenopyrite were studied, using batch tests. The coating solutions contained silicate-oxyanions, an oxidant (H 2 O 2 ) and buffered at pH 6. The effect of Si concentration (0.1-50 mM), liquid/solid ratio (5-100 mL/g) and contact time (up to 24 h) was investigated. Pyrite tailings treated with a solution of 1 mM Si/0.1 M H 2 O 2 at L/S:100 mL/g for 24 h resulted in the optimum formation of a coating, which reduced the amount of SO 4 -2 -released by 72%, compared to the sample treated in the absence of Si. However, silicate treatment had a negative effect on arsenopyrite tailings inducing As mobilization., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality., (Copyright © 2022 Elsevier Ltd. All rights reserved.)