Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase.

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

Modeling approaches to inform travel-related policies for COVID-19 containment: a scoping review and future directions.

BACKGROUND: Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies. METHODS: We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor. RESULTS: The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches. CONCLUSION: Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Evolution-guided engineering of trans-acyltransferase polyketide synthases.

Bacterial multimodular polyketide synthases (PKSs) are giant enzymes that generate a wide range of therapeutically important but synthetically challenging natural products. Diversification of polyketide structures can be achieved by engineering these enzymes. However, notwithstanding successes made with textbook cis-acyltransferase (cis-AT) PKSs, tailoring such large assembly lines remains challenging. Unlike textbook PKSs, trans-AT PKSs feature an extraordinary diversity of PKS modules and commonly evolve to form hybrid PKSs. In this study, we analyzed amino acid coevolution to identify a common module site that yields functional PKSs. We used this site to insert and delete diverse PKS parts and create 22 engineered trans-AT PKSs from various pathways and in two bacterial producers. The high success rates of our engineering approach highlight the broader applicability to generate complex designer polyketides.

Singleton-based species names and fungal rarity: Does the number really matter?

Fungi are among the least known organisms on earth, with an estimated number of species between 1.5 and 10 million. This number is expected to be refined, especially with increasing knowledge about microfungi in undersampled habitats and increasing amounts of data derived from environmental DNA sequencing. A significant proportion of newly generated sequences fail to match with already named species, and thus represent what has been referred to as fungal "dark taxa". Due to the challenges associated with observing, identifying, and preserving sporophores, many macro- and microfungal species are only known from a single collection, specimen, isolate, and/or sequence-a singleton. Mycologists are consequently used to working with "rare" sequences and specimens. However, rarity and singleton phenomena lack consideration and valorization in fungal studies. In particular, the practice of publishing new fungal species names based on a single specimen remains a cause of debate. Here, we provide some elements of reflection on this issue in the light of the specificities of the fungal kingdom and global change context. If multiple independent sources of data support the existence of a new taxon, we encourage mycologists to proceed with formal description, irrespective of the number of specimens at hand. Although the description of singleton-based species may not be considered best practice, it does represent responsible science in the light of closing the Linnean biodiversity shortfall.

Role of sentinel lymph node evaluation during hysterectomy for preoperative pathology diagnosis of endometrial intraepithelial neoplasia in a community hospital setting.

OBJECTIVE: To determine the utility of sentinel lymph node (SLN) evaluation during hysterectomy for endometrial intraepithelial neoplasia (EIN) in a community hospital setting and identify descriptive trends among pathology reports from those diagnosed with endometrial cancer (EC). METHODS: We reviewed patients who underwent hysterectomy from January 2015 to July 2022 for a pathologically confirmed diagnosis of EIN obtained by endometrial biopsy (EMB) or dilation and curettage. Data was obtained via detailed chart review. Statistical testing was utilized for between-group comparisons and multivariate logistic regression modeling. RESULTS: Of the 177 patients with EIN who underwent hysterectomy during the study period, 105 (59.3%) had a final diagnosis of EC. At least stage IB disease was found in 29 of these patients who then underwent adjuvant therapy. Pathology report descriptors suspicious for cancer and initial specimen type obtained by EMB were independently and significantly associated with increased odds of EC diagnosis (aOR 8.192, p < 0.001;3.746, p < 0.001, respectively). Operative times were not increased by performance of SLN sampling while frozen specimen evaluation added an average of 28 min to procedure length. Short-term surgical outcomes were also similar between groups. CONCLUSION: Patients treated for EIN at community-based institutions might be more likely to upstage preoperative EIN diagnoses and have an increased risk of later stage disease than previous research suggests. Given no surgical time or short-term outcome differences, SLN evaluation should be more strongly considered in this practice setting, especially for patients diagnosed by EMB or with pathology reports indicating suspicion for EC.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning.

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Inonotus obliquus upregulates muscle regeneration and augments function through muscle oxidative metabolism.

Skeletal muscle wasting related to aging or pathological conditions is critically associated with the increased incidence and prevalence of secondary diseases including cardiovascular diseases, metabolic syndromes, and chronic inflammations. Much effort is made to develop agents to enhance muscle metabolism and function. Inonotus obliquus (I. obliquus; IO) is a mushroom popularly called chaga and has been widely employed as a folk medicine for inflammation, cardiovascular diseases, diabetes, and cancer in Eastern Europe and Asia. However, its effect on muscle health has not been explored. Here, we aimed to investigate the beneficial effect of IO extract in muscle regeneration and metabolism. The treatment of IO in C2C12 myoblasts led to increased myogenic differentiation and alleviation of dexamethasone-induced myotube atrophy. Network pharmacological analysis using the identified specific chemical constituents of IO extracts predicted protein kinase B (AKT)-dependent mechanisms to promote myogenesis and muscle regeneration. Consistently, IO treatment resulted in the activation of AKT, which suppressed muscle-specific ubiquitin E3 ligases induced by dexamethasone. IO treatment in mice improved the regeneration of cardiotoxin-injured muscles accompanied by elevated proliferation and differentiation of muscle stem cells. Furthermore, it elevated the mitochondrial content and muscle oxidative metabolism accompanied by the induction of peroxisome proliferator-activated receptor γ coactivator α (PGC-1α). Our current data suggest that IO is a promising natural agent in enhancing muscle regenerative capacity and oxidative metabolism thereby preventing muscle wasting.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Gut microbiome perturbation, antibiotic resistance, and Escherichia coli strain dynamics associated with international travel: a metagenomic analysis.

BACKGROUND: Culture-based studies have shown that acquisition of extended-spectrum ß-lactamase-producing Enterobacterales is common during international travel; however, little is known about the role of the gut microbiome before and during travel, nor about acquisition of other antimicrobial-resistant organisms. We aimed to identify (1) whether the gut microbiome provided colonisation resistance against antimicrobial-resistant organism acquisition, (2) the effect of travel and travel behaviours on the gut microbiome, and (3) the scale and global heterogeneity of antimicrobial-resistant organism acquisition. METHODS: In this metagenomic analysis, participants were recruited at three US travel clinics (Boston, MA; New York, NY; and Salt Lake City, UT) before international travel. Participants had to travel internationally between Dec 8, 2017, and April 30, 2019, and have DNA extractions for stool samples both before and after travel for inclusion. Participants were excluded if they had at least one low coverage sample (<1 million read pairs). Stool samples were collected at home before and after travel, sent to a clinical microbiology laboratory to be screened for three target antimicrobial-resistant organisms (extended-spectrum ß-lactamase-producing Enterobacterales, carbapenem-resistant Enterobacterales, and mcr-mediated colistin-resistant Enterobacterales), and underwent DNA extraction and shotgun metagenomic sequencing. We profiled metagenomes for taxonomic composition, antibiotic-resistant gene content, and characterised the Escherichia coli population at the strain level. We analysed pre-travel samples to identify the gut microbiome risk factors associated with acquisition of the three targeted antimicrobial resistant organisms. Pre-travel and post-travel samples were compared to identify microbiome and resistome perturbation and E coli strain acquisition associated with travel. FINDINGS: A total of 368 individuals travelled between the required dates, and 296 had DNA extractions available for both before and after travel. 29 travellers were excluded as they had at least one low coverage sample, leaving a final group of 267 participants. We observed a perturbation of the gut microbiota, characterised by a significant depletion of microbial diversity and enrichment of the Enterobacteriaceae family. Metagenomic strain tracking confirmed that 67% of travellers acquired new strains of E coli during travel that were phylogenetically distinct from their pre-travel strains. We observed widespread enrichment of antibiotic-resistant genes in the gut, with a median 15% (95% CI 10-20, p<1 × 10-10) increase in burden (reads per kilobase per million reads). This increase included antibiotic-resistant genes previously classified as threats to public health, which were 56% (95% CI 36-91, p=2 × 10-11) higher in abundance after travel than before. Fluoroquinolone antibiotic-resistant genes were aquired by 97 (54%) of 181 travellers with no detected pre-travel carriage. Although we found that visiting friends or relatives, travel to south Asia, and eating uncooked vegetables were risk factors for acquisition of the three targeted antimicrobial resistant organisms, we did not observe an association between the pre-travel microbiome structure and travel-related antimicrobial-resistant organism acquisition. INTERPRETATION: This work highlights a scale of E coli and antimicrobial-resistant organism acquisition by US travellers not apparent from previous culture-based studies, and suggests that strategies to control antimicrobial-resistant organisms addressing international traveller behaviour, rather than modulating the gut microbiome, could be worthwhile. FUNDING: US Centers for Disease Control and Prevention and National Institute of Allergy and Infectious Diseases.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning.

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities.

Bacteria have long been a source of natural products with diverse bioactivities that have been developed into therapeutics to treat human disease. Historically, researchers have focused on a few taxa of bacteria, mainly Streptomyces and other actinomycetes. This strategy was initially highly successful and resulted in the golden era of antibiotic discovery. The golden era ended when the most common antibiotics from Streptomyces had been discovered. Rediscovery of known compounds has plagued natural product discovery ever since. Recently, there has been increasing interest in identifying other taxa that produce bioactive natural products. Several bioinformatics studies have identified promising taxa with high biosynthetic capacity. However, these studies do not address the question of whether any of the products produced by these taxa are likely to have activities that will make them useful as human therapeutics. We address this gap by applying a recently developed machine learning tool that predicts natural product activity from biosynthetic gene cluster (BGC) sequences to determine which taxa are likely to produce compounds that are not only novel but also bioactive. This machine learning tool is trained on a dataset of BGC-natural product activity pairs and relies on counts of different protein domains and resistance genes in the BGC to make its predictions. We find that rare and understudied actinomycetes are the most promising sources for novel active compounds. There are also several taxa outside of actinomycetes that are likely to produce novel active compounds. We also find that most strains of Streptomyces likely produce both characterized and uncharacterized bioactive natural products. The results of this study provide guidelines to increase the efficiency of future bioprospecting efforts. ONE-SENTENCE SUMMARY: This paper combines several bioinformatics workflows to identify which genera of bacteria are most likely to produce novel natural products with useful bioactivities such as antibacterial, antitumor, or antifungal activity.

Pediatric Antiretroviral Therapy Coverage and AIDS Deaths in the "Treat All" Era.

OBJECTIVES: In 2015, CD4-based clinical staging criteria for antiretroviral therapy (ART) initiation were removed, expanding ART eligibility ("Treat All") for children, who shoulder an outsized burden of HIV-related deaths. To quantify the impact of "Treat All" on pediatric HIV outcomes, we examined shifts in pediatric ART coverage and AIDS mortality before and after "Treat All" implementation. METHODS: We abstracted country-level ART coverage (proportion of children <15 years on ART) and AIDS mortality (deaths per 100 000 population) estimates over 11 years. For 91 countries, we also abstracted the year "Treat All" was incorporated into national guidelines. We used multivariable 2-way fixed effects negative binomial regression to estimate changes in pediatric ART coverage and AIDS mortality potentially attributable to "Treat All" expansion, reported as adjusted incidence rate ratios (adj.IRR) with 95% confidence intervals (95% CI). RESULTS: From 2010 to 2020, pediatric ART coverage tripled (16% to 54%), and AIDS-related deaths were halved (240 000 to 99 000). Compared with the pre-implementation period, observed ART coverage continued increasing after "Treat All" adoption, but this rate of increase declined by 6% (adj.IRR = 0.94, 95% CI: 0.91-0.98). AIDS mortality continued declining after "Treat All" adoption, but this rate of decline decreased by 8% (adj.IRR = 1.08, 95% CI: 1.05-1.11) in the post-implementation period. CONCLUSIONS: Although "Treat All" called for increased HIV treatment equity, ART coverage continues lagging in children and comprehensive approaches that address structural issues, including family-based services and intensified case-finding, are needed to close pediatric HIV treatment gaps.

Effect of Predeparture Testing on Postarrival SARS-CoV-2-Positive Test Results Among International Travelers - CDC Traveler-Based Genomic Surveillance Program, Four U.S. Airports, March-September 2022.

Beginning December 6, 2021, all international air passengers boarding flights to the United States were required to show either a negative result from a SARS-CoV-2 viral test taken ≤1 day before departure or proof of recovery from COVID-19 within the preceding 90 days (1). As of June 12, 2022, predeparture testing was no longer mandatory but remained recommended by CDC (2,3). Various modeling studies have estimated that predeparture testing the day before or the day of air travel reduces transmission or importation of SARS-CoV-2 by 31%-76% (4-7). Postarrival SARS-CoV-2 pooled testing data from CDC's Traveler-based Genomic Surveillance program were used to compare SARS-CoV-2 test results among volunteer travelers arriving at four U.S. airports during two 12-week periods: March 20-June 11, 2022, when predeparture testing was required, and June 12-September 3, 2022, when predeparture testing was not required. In a multivariable logistic regression model, pooled nasal swab specimens collected during March 20-June 11 were 52% less likely to be positive for SARS-CoV-2 than were those collected during June 12-September 3, after adjusting for COVID-19 incidence in the flight's country of origin, sample pool size, and collection airport (adjusted odds ratio [aOR] = 0.48, 95% CI = 0.39-0.58) (p<0.001). These findings support predeparture testing as a tool for reducing travel-associated SARS-CoV-2 transmission and provide important real-world evidence that can guide decisions for future outbreaks and pandemics.

Serous Tubal Intraepithelial Carcinoma in a Risk-reducing Salpingo-oophorectomy Specimen From a RAD51D Mutation Carrier: A Case Report.

The RAD51D gene codes a protein-paralog of the RAD51 DNA recombinase, which catalyzes DNA strand exchange during homologous recombination. Similar to BRCA1 / BRCA2 , mutations in RAD51D both predispose to ovarian carcinoma and impart sensitivity to poly (ADP-ribose) polymerase inhibitors in preclinical studies. Based on cancer risk prediction models, RAD51D mutations pose a moderate-to-high risk for ovarian cancer warranting consideration for risk-reducing surgery. We report a case of serous tubal intraepithelial carcinoma in a patient undergoing risk-reducing total hysterectomy with bilateral salpingo-oophorectomy for a RAD51D pathogenic variant. The histopathologic and p53-immunophenotypic features of this lesion are similar to those reported previously in BRCA1 / BRCA2 mutation carriers and those of serous tubal intraepithelial carcinoma associated with sporadic high-grade serous carcinomas. These features include marked increase in nuclear-to-cytoplasmic ratio, loss of cell polarity, absence of ciliation, prominent nucleoli, mitotic activity, epithelial stratification, surface exfoliative changes, and complete loss of p53 staining. Although familial ovarian cancers with mutations in RAD51D -or other genes in the Fanconi anemia pathway-are much less common those with BRCA1 / BRCA2 mutations, our findings support a common phenotype for early serous cancers in this pathway.

Early Detection of Severe Acute Respiratory Syndrome Coronavirus 2 Variants Using Traveler-based Genomic Surveillance at 4 US Airports, September 2021-January 2022.

We enrolled arriving international air travelers in a severe acute respiratory syndrome coronavirus 2 genomic surveillance program. We used molecular testing of pooled nasal swabs and sequenced positive samples for sublineage. Traveler-based surveillance provided early-warning variant detection, reporting the first US Omicron BA.2 and BA.3 in North America.

Lessons Learned from CDC's Global COVID-19 Early Warning and Response Surveillance System.

Early warning and response surveillance (EWARS) systems were widely used during the early COVID-19 response. Evaluating the effectiveness of EWARS systems is critical to ensuring global health security. We describe the Centers for Disease Control and Prevention (CDC) global COVID-19 EWARS (CDC EWARS) system and the resources CDC used to gather, manage, and analyze publicly available data during the prepandemic period. We evaluated data quality and validity by measuring reporting completeness and compared these with data from Johns Hopkins University, the European Centre for Disease Prevention and Control, and indicator-based data from the World Health Organization. CDC EWARS was integral in guiding CDC's early COVID-19 response but was labor-intensive and became less informative as case-level data decreased and the pandemic evolved. However, CDC EWARS data were similar to those reported by other organizations, confirming the validity of each system and suggesting collaboration could improve EWARS systems during future pandemics.

High-risk US International Travelers Seeking Pretravel Consultation During the COVID-19 Pandemic.

Background: To assess the implications of coronavirus disease 2019 (COVID-19)-related travel disruptions, we compared demographics and travel-related circumstances of US travelers seeking pretravel consultation regarding international travel at US Global TravEpiNet (GTEN) sites before and after the initiation of COVID-19 travel warnings. Methods: We analyzed data in the GTEN database regarding traveler demographics and travel-related circumstances with standard questionnaires in the pre-COVID-19 period (January-December 2019) and the COVID-19 period (April 2020-March 2021), excluding travelers from January to March 2020. We conducted descriptive analyses of differences in demographics, travel-related circumstances, routine and travel-related vaccinations, and medications. Results: Compared with 16 903 consultations in the pre-COVID-19 period, only 1564 consultations were recorded at GTEN sites during the COVID-19 period (90% reduction), with a greater proportion of travelers visiting friends and relatives (501/1564 [32%] vs 1525/16 903 [9%]), individuals traveling for >28 days (824/1564 [53%] vs 2522/16 903 [15%]), young children (6 mo-<6 y: 168/1564 [11%] vs 500/16 903 [3%]), and individuals traveling to Africa (1084/1564 [69%] vs 8049/16 903 [48%]). A smaller percentage of vaccine-eligible travelers received vaccines at pretravel consultations during the COVID-19 period than before, except for yellow fever and Japanese encephalitis vaccinations. Conclusions: Compared with the pre-COVID-19 period, a greater proportion of travelers during the COVID-19 period were young children, were planning to visit friends and relatives, were traveling for >28 days, or were traveling to Africa, which are circumstances that contribute to high risk for travel-related infections. Fewer vaccine-eligible travelers were administered travel-related vaccines at pretravel consultations. Counseling and vaccination focused on high-risk international travelers must be prioritized during the COVID-19 pandemic.

Management of secondary Paget's disease of the vulva associated with transitional cell carcinoma.

PURPOSE: Secondary extramammary Paget's disease (EMPD) related to urothelial carcinoma is rare, with some cases presenting synchronously with either a primary neoplasm or recurrence of a neoplasm and other cases presenting up to 13 years prior to the detection of urothelial carcinoma. In this report, we will review the presentation, diagnosis, pathophysiology, management, and literature review of cases of secondary EPMD associated with urothelial carcinoma. METHODS: We reviewed the English literature for all cases of secondary EMPD presenting synchronously with or in patients with a history of urothelial carcinoma, as well as treatment data for secondary vulvar Paget's. RESULTS: We identified 16 case reports and case series with a total of 20 cases of vulvar EMPD associated with urothelial carcinoma. Twelve cases presented asynchronously and 8 had EMPD preceding the diagnosis of the underlying neoplasm. There is a paucity in the literature regarding management and surgical resection is a common treatment strategy; however, nonsurgical interventions may also be effective. CONCLUSION: There is a paucity in the literature regarding management of secondary EPMD of urothelial origin, but consideration of radiation and systemic chemotherapy may be a reasonable treatment approach.