Abstract Several issues that affect prevention and treatment of heartworm infections require more intensive research. The incidence of heartworm infection in the USA is increasing, but the factors that underlie this trend remain incompletely understood. The contributions of climate change, vector range expansion, client compliance and resistance to macrocyclic lactones (ML) are likely interrelated and require investigation. Molecular-level research has not yet identified the causative mechanisms underlying ML resistance (MLR), but surveys of genomic markers associated with the trait reveal worrying trends in the presence and frequency of these resistance alleles. Research is needed to confirm the phenotypic relevance of these markers and to identify the gene(s) responsible for it. Developing highly inbred strains of MLR heartworms may be necessary but would require multigenerational studies of targeted breeding of selected parasites in dogs. A second issue of concern for veterinarians is the increasing extra-label use of emodepside products for the treatment of multiple anthelmintic drug resistant (MADR) canine hookworms (Ancylostoma caninum), which are already common throughout the USA. Emodepside is not approved for use in dogs in the USA, but a cat topical product containing the drug is, and is being used orally in hookworm-infected dogs. Emodepside has activity against larval and adult stages of many filarial parasites and the safety of this drug in heartworm-infected dogs has not been reported. It is perhaps unlikely that such studies will be undertaken, given the lack of economic motivation. Nonetheless, a review of the relevant literature leads to the conclusion that the status of heartworm infection in a dog bearing an apparently MADR hookworm infection be determined before starting treatment with emodepside, with caution exercised should it ensue. Graphical Abstract
Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.
Forecasts of hospitalisations of infectious diseases play an important role for allocating healthcare resources during epidemics and pandemics. Large-scale analysis of model forecasts during the COVID-19 pandemic has shown that the model rank distribution with respect to accuracy is heterogeneous and that ensemble forecasts have the highest average accuracy. Building on that work we generated a maximally diverse synthetic dataset of 324 different hospitalisation time-series that correspond to different disease characteristics and public health responses. We evaluated forecasts from 14 component models and 6 different ensembles. Our results show that component model accuracy was heterogeneous and varied depending on the current rate of disease transmission. Going from 7 day to 14 day forecasts mechanistic models improved in relative accuracy compared to statistical models. A novel adaptive ensemble method outperforms all other ensembles, but is closely followed by a median ensemble. We also investigated the relationship between ensemble error and variability of component forecasts and show that the coefficient of variation is predictive of future error. Lastly, we validated the results on data from the COVID-19 pandemic in Sweden. Our findings have the potential to improve epidemic forecasting, in particular the ability to assign confidence to ensemble forecasts at the time of prediction based on component forecast variability.
Recently, the diffusion model has gained significant attention as one of the most successful image generation models, which can generate high-quality images by iteratively sampling noise. However, recent studies have shown that diffusion models are vulnerable to backdoor attacks, allowing attackers to enter input data containing triggers to activate the backdoor and generate their desired output. Existing backdoor attack methods primarily focused on target noise-to-image and text-to-image tasks, with limited work on backdoor attacks in image-to-image tasks. Furthermore, traditional backdoor attacks often rely on a single, conspicuous trigger to generate a fixed target image, lacking concealability and flexibility. To address these limitations, we propose a novel backdoor attack method called "Parasite" for image-to-image tasks in diffusion models, which not only is the first to leverage steganography for triggers hiding, but also allows attackers to embed the target content as a backdoor trigger to achieve a more flexible attack. "Parasite" as a novel attack method effectively bypasses existing detection frameworks to execute backdoor attacks. In our experiments, "Parasite" achieved a 0 percent backdoor detection rate against the mainstream defense frameworks. In addition, in the ablation study, we discuss the influence of different hiding coefficients on the attack results. You can find our code at https://anonymous.4open.science/r/Parasite-1715/.
Matthew A. Reyna, Zuzana Koscova, Jan Pavlus
et al.
Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas disease can cause cardiovascular diseases and digestive problems. Serological testing capacities for Chagas disease are limited, but Chagas cardiomyopathy often manifests in ECGs, providing an opportunity to prioritize patients for testing and treatment. Approach: The George B. Moody PhysioNet Challenge 2025 invites teams to develop algorithmic approaches for identifying Chagas disease from electrocardiograms (ECGs). Main results: This Challenge provides multiple innovations. First, we leveraged several datasets with labels from patient reports and serological testing, provided a large dataset with weak labels and smaller datasets with strong labels. Second, we augmented the data to support model robustness and generalizability to unseen data sources. Third, we applied an evaluation metric that captured the local serological testing capacity for Chagas disease to frame the machine learning problem as a triage task. Significance: Over 630 participants from 111 teams submitted over 1300 entries during the Challenge, representing diverse approaches from academia and industry worldwide.
Enayatullah Hamdard, Ahmadullah Zahir, Babrak Karwand
et al.
Background: Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne viral disease with a Case Fatality Ratio (CFR) of 10–40 %. It spreads from livestock to humans primarily through tick bites. It is crucial to monitor the peak months of this endemic disease in Afghanistan. Currently, the country is grappling with a potential national outbreak of CCHF, facing limitations in both diagnostic and preventive measures. Therefore, this study aims to describe CCHF prevalence during spike months (June-September) from 2015 to 2024, coinciding with Eid-al-Adha, and assess CCHF familiarity among inhabitants in eight regions of Afghanistan. Method: We have analyzed the National Surveillance System data (2015–2024) on retrospective basis. A structured questionnaire was developed to assess CCHF knowledge among inhabitants of eight regions. Data analysis included percentages, frequencies, chi-square tests, using SPSS and power BI. Results: The national surveillance system recorded 1796 CCHF confirmed cases with 238 deaths during spike months from 2015 to 2024. The highest number of Reported cases was in 2023 (734 cases, 78 deaths), followed by 2022 (221 cases). During Eid-al-Adha months from 2015 to 2024, there were 804 CCHF cases and 176 deaths, with the most in 2023 (313 cases, 78 deaths) and the fewest in 2015 (7 cases, 2 deaths).A survey of 1440 inhabitants (80 % male, 20 % female) across eight regions of Afghanistan showed knowledge of CCHF varied within regions. Correct responses were highest in the central region (394/815), followed by north (336/760). Incorrect responses were highest in central highlands (1039/1440), followed by west (450/881), indicating limited knowledge despite annual spikes in cases. Conclusion: The surge in CCHF outbreaks during Eid-al-Adha in Afghanistan underscores the challenge posed by limited knowledge of the disease. Uncontrolled animal movement and self-slaughter during Eid festival emphasize the urgent need for targeted public health strategies by relevant ministries.
Infectious and parasitic diseases, Public aspects of medicine
Asya Stoyanova, Irina Georgieva, Metodi Popov
et al.
Objective: To detect the etiological cause of an acute gastroenteritis outbreak at St. Anna kindergarten in the village of Resilovo, region Kyustendil. Materials and Methods: A total of 22 faecal specimens from children (n = 18) and staff (n = 4) were tested. Multiplex RT-PCR with specific primer pairs detecting the most common viral causes of gastroenteritis (noroviruses, rotaviruses, sapoviruses, intestinal adenoviruses and intestinal astroviruses) was applied to detect the viral causative agent. Noroviruses were detected and sequenced and subsequent phylogenetic analysis was carried out. Results: Genogroup II noroviruses were detected in five samples from children and one sample from staff (6/22) or in 27.3% of the specimens. According to WHO criteria, this proves that noroviruses have caused the epidemic outbreak. Detected noroviruses were subjected to sequencing and subsequent phylogenetic analysis, with data identifying genotype 17 (GII.17) as the causative agent. Conclusion: Norovirus genotype 17 (GII.17) was first detected in Bulgaria in 2015 as the causative agent of an outbreak in a secondary school in the town of Pravets. In 2016, the circulation of this genotype was again established in sporadic cases, and in 2022 it was found to be the cause of the epidemic gastroenteritis outbreak in a kindergarten in the village of Resilovo. To protect public health, continuous monitoring and targeted search for viral intestinal agents is essential, regardless of the transient and usually mild course of the disease.
According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin. With the rise of AI, self-diagnosis and improved disease recognition have become more promising than ever; in spite of that, existing methods suffer from a lack of large-scale patient databases and outdated methods of study, resulting in studies being limited to only a few diseases or modalities. This study incorporates readily available and easily accessible patient information via image and text for skin disease classification on a new dataset of 26 skin disease types that includes both skin disease images (37K) and associated patient narratives. Using this dataset, baselines for various image models were established that outperform existing methods. Initially, the Resnet-50 model was only able to achieve an accuracy of 70% but, after various optimization techniques, the accuracy was improved to 80%. In addition, this study proposes a novel fine-tuning strategy for sequence classification Large Language Models (LLMs), Chain of Options, which breaks down a complex reasoning task into intermediate steps at training time instead of inference. With Chain of Options and preliminary disease recommendations from the image model, this method achieves state of the art accuracy 91% in diagnosing patient skin disease given just an image of the afflicted area as well as a patient description of the symptoms (such as itchiness or dizziness). Through this research, an earlier diagnosis of skin diseases can occur, and clinicians can work with deep learning models to give a more accurate diagnosis, improving quality of life and saving lives.
Shane Babcock, Carter Benson, Giacomo De Colle
et al.
Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as the Coronavirus Infectious Disease Ontology (CIDO), are essential for organizing and disseminating information related to infectious diseases. The COVID-19 pandemic highlighted the need for updating IDO and its virus-specific extensions. There is an additional need to update IDO extensions specific to bacteria, fungus, and parasite infectious diseases. We adopt the "hub and spoke" methodology to generate pathogen-specific extensions of IDO: Virus Infectious Disease Ontology (VIDO), Bacteria Infectious Disease Ontology (BIDO), Mycosis Infectious Disease Ontology (MIDO), and Parasite Infectious Disease Ontology (PIDO). The creation of pathogen-specific reference ontologies advances modularization and reusability of infectious disease data within the IDO ecosystem. Future work will focus on further refining these ontologies, creating new extensions, and developing application ontologies based on them, in line with ongoing efforts to standardize biological and biomedical terminologies for improved data sharing and analysis.
D. V. Trankvilevsky, O. N. Skudareva, E. P. Igonina
et al.
The aim of the work was to analyze the epizootic and epidemiological situation on leptospirosis in the territory of the Russian Federation in 2023 and to forecast its development for 2024. In the period between 2000 and 2023, the long-term dynamics of morbidity in Russia tended to decrease. Leptospirosis incidence was mainly sporadic. In 2023, cases of this infection in humans were reported in all federal districts, with the exception of the North Caucasian one. The highest incidence rates were recorded in the Southern and Central Federal Districts. The results of testing material from small mammals using bacteriological, immunological and molecular-biological methods confirmed the circulation of pathogenic Leptospira in 50 constituent entities of the Russian Federation in all federal districts. Specific prevention measures were carried out: 20,114 people were vaccinated in 27 entities. The probability of human infection is higher in the territories of the Southern, Central, Northwestern, Volga and Ural Federal Districts. Imported cases of infection from regions with subequatorial and equatorial climates, which are actively visited by tourists, are not excluded.
Li-Yu Chen,1 Chen-Yu Wang,1,2 Chi-Ying Lin,3 Ming-Jui Tsai,3 Wei-Hsun Shen,1 Pei-Jhih Li,1 Lin-Chu Liao,1 Chih-Fen Huang,4,5 Chien-Chih Wu4 1Department of Pharmacy, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan; 2National Center for Geriatrics and Welfare Research, National Health Research Institutes, Yunlin, Taiwan; 3Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan; 4Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan; 5School of Pharmacy, College of Medicine, National Taiwan University, Taipei, TaiwanCorrespondence: Chien-Chih Wu, Department of Pharmacy, National Taiwan University Hospital, 7 Chung Shan S. Road, Taipei, Taiwan, Email 101440@ntuh.gov.tw; r93451011@gmail.comBackground: In the field of postoperative care, infections caused by Gram-positive bacteria pose a major clinical challenge. Vancomycin is a key therapeutic agent whose efficacy is greatly influenced by renal function, particularly by augmented renal clearance (ARC). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) estimated glomerular filtration rate (eGFR) is an easy and commonly used method to predict ARC; however, it is not well studied to determine vancomycin dose. In this study, we examined the effectiveness of the CKD-EPI equation in determining ARC and optimizing the dose of vancomycin for surgical ward patients.Methodology: A retrospective observational study was conducted to examine 158 surgical ward patients receiving vancomycin. Data on demographics, medical history, and vancomycin dosing were collected. Renal function was evaluated using the CKD-EPI equation, with ARC defined as eGFR ≥ 96.5 mL/min/1.73 m2. Vancomycin pharmacokinetics were calculated using the ClinCalc tool.Results: ARC was in 54% of the patients. Compared with patients without ARC, those with ARC were younger and had lower serum creatinine levels. They also required higher vancomycin doses but had lower trough concentrations and 24-hour area-under-the-curve values. A significant correlation was observed between eGFR and vancomycin clearance, with eGFR > 96.5 mL/min/1.73 m2 necessitating higher vancomycin doses (> 45 mg/kg/day) to achieve the desired area under the curve to minimum inhibitory concentration ratio.Conclusion: For surgical ward patients with CKD-EPI eGFR ≥ 96.5 mL/min/1.73 m2, a vancomycin dosage of > 45 mg/kg/day may be recommended to reach effective therapeutic levels. Overall, this study emphasizes the importance of tailoring vancomycin therapy depending on renal function to ensure efficacy and mitigate the risk of antimicrobial resistance in surgical ward patients.Keywords: augmented renal clearance, vancomycin, surgery
Rosario Martinez-Vega, Wilfred Fon Mbacham, Innocent Ali
et al.
Abstract Background The World Health Organization 2022 malaria chemoprevention guidelines recommend providing a full course of antimalarial treatment at pre-defined intervals, regardless of malaria status to prevent illness among children resident in moderate to high perennial malaria transmission settings as perennial malaria chemoprevention (PMC) with sulfadoxine-pyrimethamine (SP). The dhps I431V mutation circulating in West Africa has unknown effect on SP protective efficacy. Methods This protocol is for a three-arm, parallel, double-blinded, placebo-controlled, randomised trial in Cameroon among children randomly assigned to one of three directly-observed treatment groups: (i) Group 1 (n = 450) receives daily artesunate (AS) placebo on days − 7 to -1, then active SP plus placebo amodiaquine (AQ) on day 0, and placebo AQ on days 1 and 2; (ii) Group 2 (n = 250) receives placebo AS on days − 7 to -1, then active SP and AQ on day 0, and active AQ on days 1 and 2; and (iii) Group 3 (n = 200) receives active AS on days − 7 to -1, then placebo SP on day 0 and placebo AQ on days 0 to 2. On days 0, 2, 5, 7, and thereafter weekly until day 28, children provide blood for thick smear slides. Dried blood spots are collected on the same days and weekly from day 28 to day 63 for quantitative polymerase chain reaction (qPCR) and genotype analyses. Discussion Our aim is to quantify the chemopreventive efficacy of SP, and SP plus AQ, and measure the effect of the parasite genotypes associated with SP resistance on parasite clearance and protection from infection when exposed to SP chemoprevention. We will report unblinded results including: (i) time-to-parasite clearance among SP and SP plus AQ recipients who were positive on day 0 by qPCR and followed to day 63; (ii) mean duration of SP and SP plus AQ protection against infection, and (iii) mean duration of symptom-free status among SP and SP plus AQ recipients who were parasite free on day 0 by qPCR. Our study is designed to compare the 28-day follow-up of the new WHO malaria chemoprevention efficacy study protocol with extended follow-up to day 63. Trial registration ClinicalTrials.gov NCT06173206; 15/12/2023.
Rumen Harizanov, Iskra Rainova, Nina Tsvetkova
et al.
The aim of this report is to review and assess the dynamics of parasitic diseases in Bulgaria during 2020-2021. Materials and methods. The analysis is based on the annual reports of the Regional Health Inspectorates (RHIs) about the cases of registered parasitic diseases among humans in the country and on data from the National Reference Laboratory “Diagnosis of Parasitic Diseases” at the National Centre for Infectious and Parasitic Diseases (NCIPD), Sofia, for all examined cases. Results. For the study period a total of 1,225,485 individuals were examined in the country’s parasitological laboratories at the Regional Health Inspectorates, stand-alone medical diagnostic laboratories and at the National Center for Infectious and Parasitic diseases (NCIPD), of whom 19,509 (1.59%) were diagnosed with a positive result for parasitic pathogens. Among the zoonotic helminth infections with local transmission, a special attention deserve cystic echinococcosis and trichinellosis as the incidence of these parasitoses in Bulgaria is the highest among the European member states. The prevalence of ascariasis and trichuriasis in the country have been reduced to such an extent that they do not represent a public health danger any more. Data regarding community acquired giardiasis and hymenolepiasis can be interpreted in a similar way, while for enterobiasis, an increasing prevalence among both children and adults has been observed in recent years. Although imported parasitic pathology is relatively limited in volume, Bulgarian climate and fauna are quite favorable for local transmission of a number of imported parasitic diseases. Control measures regarding this pathology consist in the timely detection and removal of infection sources. Conclusion. In Bulgaria, there is a well-established system for surveillance and control of human parasitic diseases, which allows the acquisition of comprehensive information including patients demographic data and characteristics of the causative agents. This enables the monitoring of parasitic pathology among the population and an accurate assessment of the the endemic-related risks.
Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.
Abstract Objective To analyze the distribution of blaOXA among global Klebsiella pneumoniae and the characteristics of blaOXA-carrying K. pneumoniae. Materials and Methods The genomes of global K. pneumoniae were downloaded from NCBI by Aspera software. After quality check, the distribution of blaOXA among the qualified genomes was investigated by annotation with the resistant determinant database. The phylogenetic tree was constructed for the blaOXA variants based on the single nucleotide polymorphism (SNP) to explore the evolutionary relationship between these variants. The MLST (multi-locus sequence type) website and blastn tools were utilized to determine the sequence types (STs) of these blaOXA-carrying strains. and sample resource, isolation country, date and host were extracted by perl program for analyzing the characteristics of these strains. Results A total of 12,356 K. pneumoniae genomes were downloaded and 11,429 ones were qualified. Among them, 4386 strains were found to carry 5610 blaOXA variants which belonged to 27 varieties of blaOXAs, blaOXA-1 (n = 2891, 51.5%) and blaOXA-9 (n = 969, 17.3%) were the most prevalent blaOXA variants, followed by blaOXA-48 (n = 800, 14.3%) and blaOXA-232 (n = 480, 8.6%). The phylogenetic tree displayed 8 clades, three of them were composed of carbapenem-hydrolyzing oxacillinase (CHO). Totally, 300 distinct STs were identified among 4386 strains with ST11 (n = 477, 10.9%) being the most predominant one followed by ST258 (n = 410, 9.4%). Homo sapiens (2696/4386, 61.5%) was the main host for blaOXA-carrying K. pneumoniae isolates. The blaOXA-9-carrying K. pneumoniae strains were mostly found in the United States and blaOXA-48-carrying K. pneumoniae strains were mainly distributed in Europe and Asia. Conclusion Among the global K. pneumoniae, numerous blaOXA variants were identified with blaOXA-1, blaOXA-9, blaOXA-48 and blaOXA-232 being the most prevalent ones, indicating that blaOXA rapidly evolved under the selective pressure of antimicrobial agents. ST11 and ST258 were the main clones for blaOXA-carrying K. pneumoniae.
Stella E. Mushy, Expeditho Mtisi, Eric Mboggo
et al.
Abstract Background Antiretroviral therapy (ART) programs have expanded rapidly, and they are now accessible free of charge, yet "loss to follow-up, LTFU" is still a national public health issue. LTFU may result in treatment failure, hospitalization, increased risk of opportunistic infections and drug-resistant strains, and shortening the quality of life. This study described the rates and predictors of LTFU among adults living with human immunodeficiency virus (PLHIV) on ART in the Tanga region, Tanzania. Methods A retrospective longitudinal cohort study was conducted between October 2018 and December 2020 in Tanga's care and treatment health services facilities. The participants were HIV adult PLHIV aged 15 years and above on ART and attended the clinic at least once after ART initiation. LTFU was defined as not taking ART refills for 3 months or beyond from the last attendance of a refill and not yet classified as dead or transferred out. Cox proportional hazard regression models were employed to identify risk factors for LTFU. P values were two-sided, and we considered a p < 0.05 statistically significant. Results 57,173 adult PLHIV were on ART of them, 15,111 (26.43%) were LTFU, of whom 10,394 (68.78%) were females, and 4717 (31.22%) were males. Factors independently associated with LTFU involved age between 15 and 19 years (HR: 1.85, 95% CI 1.66–2.07), male sex (HR: 2.00 95% CI 1.51–2.62), divorce (HR: 1.35, 95% CI 1.24–1.48), second-line drug type (HR: 1.13, 95% CI 1.09–1.18), poor drug adherence (HR: 1.50, 95% CI 1.23–1.75), unsuppressed viral load (HR: 2.15, 95% CI 2.02–2.29), not on DTG-related drug (HR: 7.51, 95% CI 5.88–10.79), advanced HIV disease WHO stage III and IV (HR: 2.51, 95% CI 2.32–2.72). In contrast to cohabiting, ART duration < 1 year, and being pregnant showed a reduced likelihood of LTFU. Conclusion A high prevalence of LTFU was observed in this study. Young age, not using DTG-based regimen, WHO clinical stage IV, poor drug adherence, male sex, unsuppressed viral load, divorcee, and second-line regime were independently associated with LTFU. To reduce LTFU, evidence-based interventions targeting the identified risk factors should be employed.
Hans-Jonas Meyer, Bohdan Melekh, Andreas Wienke
et al.
Background: Thoracal lymphadenopathy may predict prognosis in patients with coronavirus disease 2019 (COVID-19), albeit the reported data is inconclusive. The aim of the present analysis was to analyze the affected lymph node stations and the cumulative lymph node size derived from computed tomography (CT) for prediction of 30-day mortality in patients with COVID-19. Methods: The clinical database was retrospectively screened for patients with COVID-19 between 2020 and 2022. Overall, 177 patients (63 female, 35.6%) were included into the analysis. Thoracal lymphadenopathy was defined by short axis diameter above 10 mm. Cumulative lymph node size of the largest lymph nodes was calculated and the amount of affected lymph node stations was quantified. Results: Overall, 53 patients (29.9%) died within the 30-day observation period. 108 patients (61.0%) were admitted to the ICU and 91 patients needed to be intubated (51.4%). Overall, there were 130 patients with lymphadenopathy (73.4%). The mean number of affected lymph node levels were higher in non-survivors compared to survivors (mean, 4.0 vs 2.2, p < 0.001). The cumulative size was also higher in non-survivors compared to survivors (mean 55.9 mm versus 44.1 mm, p = 0.006). Presence of lymphadenopathy was associated with 30-day mortality in a multivariable analysis, OR = 2.99 (95% CI 1.20 – 7.43), p = 0.02. Conclusions: Thoracal lymphadenopathy comprising cumulative size and affected levels derived from CT images is associated with 30-day mortality in patients with COVID-19. COVID-19 patients presenting with thoracic lymphadenopathy should be considered as a risk group.
Infectious and parasitic diseases, Public aspects of medicine
Nicolas J. Wheeler, Kendra J. Gallo, Elena J. G. Rehborg
et al.
Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating relevant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of phenotypes, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds promise for enabling collaboration among investigators with diverse interests.