Preparing for Fellowship in Internal Medicine With a Focus on Pulmonary or Critical Care Medicine: Major Principles and New Realities.

In this article, the authors provide guidance for applicants to any subspecialty in the medical specialties matching program, with a particular focus on those seeking a match into a pulmonary or critical care medicine training program, or both. The preparation, application, interview, ranking, and match steps are used to discuss available literature that informs this process. Preparing a fellowship application is discussed in terms of personal career goals, and specific strategies are suggested that can help a candidate to assess a program's fit with those goals. In addition to review of recent data on virtual interviewing and interview questioning, the authors provide practical recommendations for candidates seeking to maximize their success in the current interview environment. Finally, key points about generating a rank order list are summarized. This resource will prove useful to any candidate pursuing medical subspecialty training in the current era.

Molecular profiling of EBV associated diffuse large B-cell lymphoma.

Epstein-Barr virus (EBV) associated diffuse large B-cell lymphoma (DLBCL) represents a rare aggressive B-cell lymphoma subtype characterized by an adverse clinical outcome. EBV infection of lymphoma cells has been associated with different lymphoma subtypes while the precise role of EBV in lymphomagenesis and specific molecular characteristics of these lymphomas remain elusive. To further unravel the biology of EBV associated DLBCL, we present a comprehensive molecular analysis of overall 60 primary EBV positive (EBV+) DLBCLs using targeted sequencing of cancer candidate genes (CCGs) and genome-wide determination of recurrent somatic copy number alterations (SCNAs) in 46 cases, respectively. Applying the LymphGen classifier 2.0, we found that less than 20% of primary EBV + DLBCLs correspond to one of the established molecular DLBCL subtypes underscoring the unique biology of this entity. We have identified recurrent mutations activating the oncogenic JAK-STAT and NOTCH pathways as well as frequent amplifications of 9p24.1 contributing to immune escape by PD-L1 overexpression. Our findings enable further functional preclinical and clinical studies exploring the therapeutic potential of targeting these aberrations in patients with EBV + DLBCL to improve outcome.

mTOR inhibition amplifies the anti-lymphoma effect of PI3Kß/δ blockage in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease that exhibits constitutive activation of phosphoinositide 3-kinase (PI3K) driven by chronic B-cell receptor signaling or PTEN deficiency. Since pan-PI3K inhibitors cause severe side effects, we investigated the anti-lymphoma efficacy of the specific PI3Kß/δ inhibitor AZD8186. We identified a subset of DLBCL models within activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCL that were sensitive to AZD8186 treatment. On the molecular level, PI3Kß/δ inhibition decreased the pro-survival NF-κB and AP-1 activity or led to downregulation of the oncogenic transcription factor MYC. In AZD8186-resistant models, we detected a feedback activation of the PI3K/AKT/mTOR pathway following PI3Kß/δ inhibition, which limited AZD8186 efficacy. The combined treatment with AZD8186 and the mTOR inhibitor AZD2014 overcame resistance to PI3Kß/δ inhibition and completely prevented outgrowth of lymphoma cells in vivo in cell line- and patient-derived xenograft mouse models. Collectively, our study reveals that subsets of DLBCLs are addicted to PI3Kß/δ signaling and thus identifies a previously unappreciated role of the PI3Kß isoform in DLBCL survival. Furthermore, our data demonstrate that combined targeting of PI3Kß/δ and mTOR is effective in all major DLBCL subtypes supporting the evaluation of this strategy in a clinical trial setting.

Identifying plastics with photoluminescence spectroscopy and machine learning.

A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no universal mechanism organizing chromosome segregation in all bacteria. Apparently, some bacteria even use combinations of different segregation mechanisms such as protein machines or rely on physical forces. The identification of the relevant mechanisms is a difficult task. Here, we introduce a new machine learning approach to this problem. It is based on the analysis of trajectories of individual loci in the course of chromosomal segregation obtained by fluorescence microscopy. While machine learning approaches have already been applied successfully to trajectory classification in other areas, so far it has not been possible to use them to discriminate segregation mechanisms in bacteria. A main obstacle for this is the large number of trajectories required to train machine learning algorithms that we overcome here by using trajectories obtained from molecular dynamics simulations. We used these trajectories to train four different machine learning algorithms, two linear models and two tree-based classifiers, to discriminate segregation mechanisms and possible combinations of them. The classification was performed once using the complete trajectories as high-dimensional input vectors as well as on a set of features which were used to transform the trajectories into low-dimensional input vectors for the classifiers. Finally, we tested our classifiers on shorter trajectories with duration times comparable (or even shorter) than typical experimental trajectories and on trajectories measured with varying temporal resolutions. Our results demonstrate that machine learning algorithms are indeed capable of discriminating different segregation mechanisms in bacteria and to even resolve combinations of the mechanisms on rather short time scales.

Molecular and functional profiling identifies therapeutically targetable vulnerabilities in plasmablastic lymphoma.

Plasmablastic lymphoma (PBL) represents a rare and aggressive lymphoma subtype frequently associated with immunosuppression. Clinically, patients with PBL are characterized by poor outcome. The current understanding of the molecular pathogenesis is limited. A hallmark of PBL represents its plasmacytic differentiation with loss of B-cell markers and, in 60% of cases, its association with Epstein-Barr virus (EBV). Roughly 50% of PBLs harbor a MYC translocation. Here, we provide a comprehensive integrated genomic analysis using whole exome sequencing (WES) and genome-wide copy number determination in a large cohort of 96 primary PBL samples. We identify alterations activating the RAS-RAF, JAK-STAT, and NOTCH pathways as well as frequent high-level amplifications in MCL1 and IRF4. The functional impact of these alterations is assessed using an unbiased shRNA screen in a PBL model. These analyses identify the IRF4 and JAK-STAT pathways as promising molecular targets to improve outcome of PBL patients.

Balancing Demands: Determinants of Burnout Reported by Fellows in Pulmonary and Critical Care Medicine.

Background: Burnout is common among physicians who care for critically ill patients and is known to contribute to worse patient outcomes. Fellows training in pulmonary and critical care medicine (PCCM) have risk factors that make them susceptible to burnout; for example, clinical environments that require increased intellectual and emotional demands with long hours. The Accreditation Council for Graduate Medical Education has recognized the increasing importance of trainee burnout and encourages training programs to address burnout. Objective: To assess factors related to training and practice that posed a threat to the well-being among fellows training in PCCM and to obtain suggestions regarding how programs can improve fellow well-being. Methods: We conducted a qualitative content analysis of data collected from a prior cross-sectional electronic survey with free-response questions of fellows enrolled in pulmonary, PCCM, and critical care medicine training programs in the United States. Fellows were asked what factors posed a threat to their well-being and what changes their training program could implement. Responses were qualitatively coded and categorized into themes using thematic analysis. Results: A total of 427 fellows (44% of survey respondents) completed at least one free-response question. The majority of respondents (60%) identified as male and white/non-Hispanic (59%). The threats to well-being and burnout were grouped into five themes: clinical burden, individual factors, team culture, limited autonomy, and program resources. Clinical burden was the most common threat discussed by fellows. Fellows highlighted factors contributing to burnout that specifically pertained to trainees including challenging interpersonal relationships with attending physicians and limited protected educational time. Fellows proposed solutions addressing clinical care, changes at the program or institution level, and organizational culture changes to improve well-being. Conclusion: This study provides insight into factors fellows report as contributors to burnout and decreased well-being in addition to investigating fellow-driven solutions toward improving well-being. These solutions may help pulmonary, PCCM, and critical care medicine program directors better address fellow well-being in the future.

Twelve tips for teaching in the ICU.

The intensive care unit (ICU) provides unique educational opportunities for both undergraduate and postgraduate learners, including procedural training, ventilator management guidance, complex communication scenarios, and didactic lectures on dynamic topics like multi-system organ failure. However, certain challenges are inherent in this setting that can make teaching difficult. Different trainee educational backgrounds, variability in disease states, time limitations and urgent patient care considerations highlight some challenges that limit teaching opportunities. The following twelve tips address these unique aspects of the ICU environment and provide strategies to optimize teaching. These tips focus on three main goals: creating an optimal learning environment, increasing learner engagement, and critically challenging learners.

A National Survey of Burnout and Depression Among Fellows Training in Pulmonary and Critical Care Medicine: A Special Report by the Association of Pulmonary and Critical Care Medicine Program Directors.

BACKGROUND: The prevalence of burnout and depressive symptoms is high among physician trainees. RESEARCH QUESTION: What is the burden of burnout and depressive symptoms among fellows training in pulmonary and critical care medicine (PCCM) and what are associated individual fellow, program, and institutional characteristics? STUDY DESIGN AND METHODS: We conducted a cross-sectional electronic survey of fellows enrolled in pulmonary, PCCM, and critical care medicine training programs in the United States to assess burnout and depressive symptoms. Burnout symptoms were measured using the Maslach Burnout Index two-item measure. The two-item Primary Care Evaluation of Mental Disorders Procedure was used to screen for depressive symptoms. For each of the two outcomes (burnout and depressive symptoms), we constructed three multivariate logistic regression models to assess individual fellow characteristics, program structure, and institutional polices associated with either burnout or depressive symptoms. RESULTS: Five hundred two of the 976 fellows who received the survey completed it-including both outcome measures-giving a response rate of 51%. Fifty percent of fellows showed positive results for either burnout or depressive symptoms, with 41% showing positive results for depressive symptoms, 32% showing positive results for burnout, and 23% showing positive results for both. Reporting a coverage system in the case of personal illness or emergency (adjusted OR [aOR], 0.44; 95% CI, 0.26-0.73) and access to mental health services (aOR, 0.14; 95% CI, 0.04-0.47) were associated with lower odds of burnout. Financial concern was associated with higher odds of depressive symptoms (aOR, 1.13; 95% CI, 1.05-1.22). Working more than 70 hours in an average clinical week and the burdens of electronic health record (EHR) documentation were associated with a higher odds of both burnout and depressive symptoms. INTERPRETATION: Given the high prevalence of burnout and depressive symptoms among fellows training in PCCM, an urgent need exists to identify solutions that address this public health crisis. Strategies such as providing an easily accessible coverage system, access to mental health resources, reducing EHR burden, addressing work hours, and addressing financial concerns among trainees may help to reduce burnout or depressive symptoms and should be studied further by the graduate medical education community.

Chromosome Segregation in Bacillus subtilis Follows an Overall Pattern of Linear Movement and Is Highly Robust against Cell Cycle Perturbations.

Although several proteins have been identified that facilitate chromosome segregation in bacteria, no clear analogue of the mitotic machinery in eukaryotic cells has been identified. In order to investigate if recognizable patterns of segregation exist during the cell cycle, we tracked the segregation of duplicated origin regions in Bacillus subtilis for 60 min in the fastest practically achievable resolution, achieving 10-s intervals. We found that while separation occurred in random patterns, often including backwards movement, overall, segregation of loci near the origins of replication was linear for the entire cell cycle. Thus, the process of partitioning can be best described as directed motion. Simulations with entropy-driven separation of polymers synthesized by two polymerases show sudden bursts of movement and segregation patterns compatible with the observed in vivo patterns, showing that for Bacillus, segregation patterns can be modeled based on entropic forces. To test if obstacles for replication forks lead to an alteration of the partitioning pattern, we challenged cells with chemicals inducing DNA damage or blocking of topoisomerase activity. Both treatments led to a moderate slowing down of separation, but linear segregation was retained, showing that chromosome segregation is highly robust against cell cycle perturbation.IMPORTANCE We have followed the segregation of origin regions on the Bacillus subtilis chromosome in the fastest practically achievable temporal manner, for a large fraction of the cell cycle. We show that segregation occurred in highly variable patterns but overall in an almost linear manner throughout the cell cycle. Segregation was slowed down, but not arrested, by treatment of cells that led to transient blocks in DNA replication, showing that segregation is highly robust against cell cycle perturbation. Computer simulations based on entropy-driven separation of newly synthesized DNA polymers can recapitulate sudden bursts of movement and segregation patterns compatible with the observed in vivo patterns, indicating that for Bacillus, segregation patterns may include entropic forces helping to separate chromosomes during the cell cycle.

Virtually Hosting a National Medical Society Conference. Lessons Learned from the 2020 Association of Pulmonary and Critical Care Medicine Program Directors Conference.

The coronavirus pandemic forced the Association of Pulmonary and Critical Care Medicine Program Directors to change the 2020 annual conference to a virtual format with relatively short notice. Using the experience of the planning committee and survey feedback from attendees, we describe the steps taken to implement a virtual conference and lessons learned in the process. The lessons described include frequent and concise communication, establishment of roles within a discrete production team, preparing speakers with a protocolized training session, active moderation of the chat box, using interactive polling and online documents to improve interactivity, a shorter agenda with more frequent breaks, encouraging "virtual happy hours" to connect with colleagues, and establishing facilitators for breakout rooms.

Quantitative Particle Uptake by Cells as Analyzed by Different Methods.

There is a large number of two-dimensional static in vitro studies about the uptake of colloidal nano- and microparticles, which has been published in the last decade. In this Minireview, different methods used for such studies are summarized and critically discussed. Supplementary experimental data allow for a direct comparison of the different techniques. Emphasis is given on how quantitative parameters can be extracted from studies in which different experimental techniques have been used, with the goal of allowing better comparison.

Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technologies. It is a highly challenging task to infer from these data the generating effects and cooperating genes. Here, we present an unsupervised hypothesis-generating learning concept termed signal dissection by correlation maximization (SDCM) that dissects large high-dimensional datasets into signatures. Each signature captures a particular signal pattern that was consistently observed for multiple genes and samples, likely caused by the same underlying interaction. A key difference to other methods is our flexible nonlinear signal superposition model, combined with a precise regression technique. Analyzing gene expression of diffuse large B-cell lymphoma, our method discovers previously unidentified signatures that reveal significant differences in patient survival. These signatures are more predictive than those from various methods used for comparison and robustly validate across technological platforms. This implies highly specific extraction of clinically relevant gene interactions.

Arrays of rectangular subcritical speech bands: Intelligibility improved by noise-vocoding and expanding to critical bandwidths.

A previous study [Warren, Bashford, and Lenz (2017). J. Acoust. Soc. Am. 141, EL222-EL227] reported that arrays of subcritical width rectangular speech bands can produce near ceiling sentence intelligibility. The present study used noise-vocoded subcritical band speech arrays with analysis bandwidths of 4%, 2%, 1%, or 0.5% of center frequency. Intelligibility decreased when analysis and noise carrier bandwidths were matched. However, expanding carrier noise bandwidths to a critical bandwidth of 1/3-octave (26%) produced array intelligibilities either equaling or substantially exceeding that of the original speech band arrays. Implications concerning bandwidth requirements of envelope processing and the redundancy of envelope cues are discussed.

Up-regulation of hexokinase II contributes to rituximab-chemotherapy resistance and is a clinically relevant target for therapeutic development.

In order to identify cellular pathways associated with therapy-resistant aggressive lymphoma, we generated rituximab-resistant cell lines (RRCL) and found that the acquirement of rituximab resistance was associated with a deregulation in glucose metabolism and an increase in the apoptotic threshold leading to chemotherapy resistance. Hexokinase II (HKII), the predominant isoform overexpressed in cancer cells, has dual functions of promoting glycolysis as well as inhibiting mitochondrial-mediated apoptosis. We found that RRCL demonstrated higher HKII levels. Targeting HKII resulted in decreased mitochondrial membrane potential, ATP production, cell viability; and re-sensitization to chemotherapy agents. Analyzed gene expression profiling data from diffuse large B-cell lymphoma patients, high-HKII levels were associated with a shorter progression free survival (PFS) and/or overall survival (OS). Our data suggest that over-expression of HKII is associated with resistance to rituximab and chemotherapy agents in aggressive lymphoma and identifies this enzyme isoform as a potential therapeutic target.