Interaction of RECQL4 with poly(ADP-ribose) is critical for the DNA double-strand break response in human cells.

To overcome genotoxicity, cells have evolved powerful and effective mechanisms to detect and respond to DNA lesions. RecQ Like Helicase-4 (RECQL4) plays a vital role in DNA damage responses. RECQL4 is recruited to DNA double-strand break (DSB) sites in a poly(ADP-ribosyl)ation (PARylation)-dependent manner, but the mechanism and significance of this process remain unclear. Here, we showed that the domain of RECQL4 recruited to DSBs in a PARylation-dependent manner directly interacts with poly(ADP-ribose) (PAR) and contains a PAR-binding motif (PBM). By replacing this PBM with a PBM of hnRNPA2 or its mutated form, we demonstrated that the PBM in RECQL4 is required for PARylation-dependent recruitment and the roles of RECQL4 in the DSB response. These results suggest that the direct interaction of RECQL4 with PAR is critical for proper cellular response to DSBs and provide insights to understand PARylation-dependent control of the DSB response and cancer therapeutics using PARylation inhibitors.

Risk factors of thromboembolic events in patients with scrub typhus.

BACKGROUND: Thromboembolic events are a well-recognized cause of in-hospital deaths of patients with infectious diseases. However, thromboembolic events in patients with scrub typhus, caused by Orientia tsutsugamushi have rarely been reported. This study aimed to assess risk factors associated with thromboembolic events in patients with scrub typhus. METHODS: All 93 scrub typhus patients' diagnoses were confirmed serologically or by positive nested polymerase chain reaction (PCR). The clinical and laboratory findings from 12 scrub typhus patients with thromboembolic events and 81 scrub typhus patients with nonthromboembolic events were retrospectively studied. To determine the factors implicated in thromboembolic events, we performed multivariate logistic regression analysis using the six independent factors identified by the univariate analysis. FINDINGS: The mean age of the patients in the thromboembolic group was 76.4 years (median, 76 years), and in nonthromboembolic group it was 64.6 years (median, 65 years) (P<0·001). Thromboembolic events were observed in 12 patients. These events included acute coronary syndrome (n = 5), acute limb ischemia (n = 4), ischemic stroke (n = 1), deep vein thrombosis combined with pulmonary thromboembolism (n = 1), and left common iliac artery aneurysm with a thrombus (n = 1). According to multivariate analysis, the following four factors were significantly associated with the thromboembolic events: 1) treatment with rifampin (OR = 57.63; P = 0.039; CI 1.230-2700.27)., 2) Taguchi genotype (OR = 41.5; P = 0.028; CI 1.5-1154.6), 3) atrial fibrillation (OR = 9.4; P = 0.034; CI 1.2-74.0), and 4) age (OR = 1.1; P = 0.046; CI 1.0-1.3). CONCLUSIONS: Our study suggests that clinicians should be cautious when managing patients with scrub typhus to avoid the development of thromboembolic events, especially in patients with risk factors such as treatment with rifampin, Taguchi genotype, atrial fibrillation, and advanced age.

Analysis of Clinical and Laboratory Profiles of Patients Hospitalized with Hemorrhagic Fever with Renal Syndrome in Southwestern South Korea.

Hemorrhagic fever with renal syndrome (HFRS) is caused by hantaviruses. Data of 34 patients with HFRS hospitalized at Chosun University Hospital, South Korea, between 2010 and 2021 were retrospectively analyzed. Nested reverse transcription polymerase chain reaction (RT-nPCR) targeting the L segment of hantavirus and sequencing were used for diagnosis. Most cases occurred in men and during the months of October through December. Common symptoms were fever, chills, gastrointestinal symptoms, and myalgia. The common laboratory abnormalities were thrombocytopenia, proteinuria, and elevated levels of serum creatinine, aspartate transaminase, alanine transaminase, and lactate dehydrogenase. Approximately 91.2% of patients had the Hantaan virus with a new genotype cluster, whereas 8.8% had the Seoul virus. Seropositivity based on IgM titer >1:32 on admission was noted in 20.6%, and a 4-fold increase in IgG titer of 1:512 was observed in 11.8%. This study demonstrated that RT-nPCR targeting the L segment of hantaviruses is a more reliable diagnostic method compared to serological testing.

Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study.

This study aimed to develop a machine learning (ML) model for predicting pulmonary embolism (PE) in patients with gastrointestinal cancers, a group at increased risk for PE. We conducted a retrospective, multicenter study analyzing patients who underwent computed tomographic pulmonary angiography (CTPA) between 2010 and 2020. The study utilized demographic and clinical data, including the Wells score and D-dimer levels, to train a random forest ML model. The model's effectiveness was assessed using the area under the receiver operating curve (AUROC). In total, 446 patients from hospital A and 139 from hospital B were included. The training set consisted of 356 patients from hospital A, with internal validation on 90 and external validation on 139 patients from hospital B. The model achieved an AUROC of 0.736 in hospital A and 0.669 in hospital B. The ML model significantly reduced the number of patients recommended for CTPA compared to the conventional diagnostic strategy (hospital A; 100.0% vs. 91.1%, P < 0.001, hospital B; 100.0% vs. 93.5%, P = 0.003). The results indicate that an ML-based prediction model can reduce unnecessary CTPA procedures in gastrointestinal cancer patients, highlighting its potential to enhance diagnostic efficiency and reduce patient burden.

Effect of physiological and environmental factors on mosquito wingbeat frequency.

Wingbeat frequency may serve as a distinctive physical signature for identifying mosquito species. However, variation in wingbeat frequency within species may compromise reliability of wingbeat frequency-based mosquito identification. We examined the impact of mosquito density (number of females), time of day (day or night), gravid status, and age (days post-emergence) on the wingbeat frequency of three important vector mosquito species using infrared optical sensors. Wingbeat frequency of Culex quinquefasciatus was significantly higher (6.2% and 9.5%) for single females compared to groups of five and ten females, respectively, and 9.4% higher during the daytime compared to nighttime. Wingbeat frequency was also significantly higher for gravid Cx. quinquefasciatus (9.4%) and Aedes aegypti (1.4%) than nongravid conspecifics. Within a short age range (five to seven days post-emergence), wingbeat in Cx. quinquefasciatus did not vary significantly. Our results highlight that both extrinsic (density and time of day) and intrinsic (gravid status) factors contribute to wingbeat variation, potentially posing challenges for development of wingbeat profile libraries and the classification of unknown specimens. Traps that aim to use wingbeat frequency and target specific cohorts of the population (i.e., host-seeking or gravid females) will need to account for differences in wingbeat frequency due to multiple factors.

Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study.

BACKGROUND: To evaluate the stand-alone efficacy and improvements in diagnostic accuracy of early-career physicians of the artificial intelligence (AI) software to detect large vessel occlusion (LVO) in CT angiography (CTA). METHODS: This multicenter study included 595 ischemic stroke patients from January 2021 to September 2023. Standard references and LVO locations were determined by consensus among three experts. The efficacy of the AI software was benchmarked against standard references, and its impact on the diagnostic accuracy of four residents involved in stroke care was assessed. The area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the software and readers with versus without AI assistance were calculated. RESULTS: Among the 595 patients (mean age 68.5±13.4 years, 56% male), 275 (46.2%) had LVO. The median time interval from the last known well time to the CTA was 46.0 hours (IQR 11.8-64.4). For LVO detection, the software demonstrated a sensitivity of 0.858 (95% CI 0.811 to 0.897) and a specificity of 0.969 (95% CI 0.943 to 0.985). In subjects whose symptom onset to imaging was within 24 hours (n=195), the software exhibited an AUROC of 0.973 (95% CI 0.939 to 0.991), a sensitivity of 0.890 (95% CI 0.817 to 0.936), and a specificity of 0.965 (95% CI 0.902 to 0.991). Reading with AI assistance improved sensitivity by 4.0% (2.17 to 5.84%) and AUROC by 0.024 (0.015 to 0.033) (all P<0.001) compared with readings without AI assistance. CONCLUSIONS: The AI software demonstrated a high detection rate for LVO. In addition, the software improved diagnostic accuracy of early-career physicians in detecting LVO, streamlining stroke workflow in the emergency room.

Six personas to adopt when framing theoretical research questions in biology.

Theory is a critical component of the biological research process, and complements observational and experimental approaches. However, most biologists receive little training on how to frame a theoretical question and, thus, how to evaluate when theory has successfully answered the research question. Here, we develop a guide with six verbal framings for theoretical models in biology. These correspond to different personas one might adopt as a theorist: 'Advocate', 'Explainer', 'Instigator', 'Mediator', 'Semantician' and 'Tinkerer'. These personas are drawn from combinations of two starting points (pattern or mechanism) and three foci (novelty, robustness or conflict). We illustrate each of these framings with examples of specific theoretical questions, by drawing on recent theoretical papers in the fields of ecology and evolutionary biology. We show how the same research topic can be approached from slightly different perspectives, using different framings. We show how clarifying a model's framing can debunk common misconceptions of theory: that simplifying assumptions are bad, more detail is always better, models show anything you want and modelling requires substantial maths knowledge. Finally, we provide a roadmap that researchers new to theoretical research can use to identify a framing to serve as a blueprint for their own theoretical research projects.

Atomic-Scale Scanning of Domain Network in the Ferroelectric HfO2 Thin Film.

Ferroelectric HfO2-based thin films have attracted much interest in the utilization of ferroelectricity at the nanoscale for next-generation electronic devices. However, the structural origin and stabilization mechanism of the ferroelectric phase are not understood because the film is typically nanocrystalline with active yet stochastic ferroelectric domains. Here, electron microscopy is used to map the in-plane domain network structures of epitaxially grown ferroelectric Y:HfO2 films in atomic resolution. The ferroelectricity is confirmed in free-standing Y:HfO2 films, allowing for investigating the structural origin for their ferroelectricity by 4D-STEM, high-resolution STEM, and iDPC-STEM. At the grain boundaries of <111>-oriented Pca21 orthorhombic grains, a high-symmetry mixed-(R3m, Pnm21) phase is induced, exhibiting enhanced polarization due to in-plane compressive strain. Nanoscale Pca21 orthorhombic grains and their grain boundaries with mixed-(R3m, Pnm21) phases of higher symmetry cooperatively determine the ferroelectricity of the Y:HfO2 film. It is also found that such ferroelectric domain networks emerge when the film thickness is beyond a finite value. Furthermore, in-plane mapping of oxygen positions overlaid on ferroelectric domains discloses that polarization is suppressed at vertical domain walls, while it is active when domains are aligned horizontally with subangstrom domain walls. In addition, randomly distributed 180° charged domain walls are confined by spacer layers.

Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.

Introduction: We developed and externally validated a fully automated algorithm using deep learning to detect large vessel occlusion (LVO) in computed tomography angiography (CTA). Method: A total of 2,045 patients with acute ischemic stroke who underwent CTA were included in the development of our model. We validated the algorithm using two separate external datasets: one with 64 patients (external 1) and another with 313 patients (external 2), with ischemic stroke. In the context of current clinical practice, thrombectomy amenable vessel occlusion (TAVO) was defined as an occlusion in the intracranial internal carotid artery (ICA), or in the M1 or M2 segment of the middle cerebral artery (MCA). We employed the U-Net for vessel segmentation on the maximum intensity projection images, followed by the application of the EfficientNetV2 to predict TAVO. The algorithm's diagnostic performance was evaluated by calculating the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The mean age in the training and validation dataset was 68.7 ± 12.6; 56.3% of participants were men, and 18.0% had TAVO. The algorithm achieved AUC of 0.950 (95% CI, 0.915-0.971) in the internal test. For the external datasets 1 and 2, the AUCs were 0.970 (0.897-0.997) and 0.971 (0.924-0.990), respectively. With a fixed sensitivity of 0.900, the specificities and PPVs for the internal test, external test 1, and external test 2 were 0.891, 0.796, and 0.930, and 0.665, 0.583, and 0.667, respectively. The algorithm demonstrated a sensitivity and specificity of approximately 0.95 in both internal and external datasets, specifically for cases involving intracranial ICA or M1-MCA occlusion. However, the diagnostic performance was somewhat reduced for isolated M2-MCA occlusion; the AUC for the internal and combined external datasets were 0.903 (0.812-0.944) and 0.916 (0.816-0.963), respectively. Conclusion: We developed and externally validated a fully automated algorithm that identifies TAVO. Further research is needed to evaluate its effectiveness in real-world clinical settings. This validated algorithm has the potential to assist early-career physicians, thereby streamlining the treatment process for patients who can benefit from endovascular treatment.

Comparable Efficacy of Lopinavir/Ritonavir and Remdesivir in Reducing Viral Load and Shedding Duration in Patients with COVID-19.

The spread of COVID-19 has significantly increased research on antiviral drugs and measures such as case isolation and contact tracing. This study compared the effects of lopinavir/ritonavir and remdesivir on COVID-19 patients with a control group receiving no antiviral drugs. Patients confirmed to have a SARS-CoV-2 infection via real-time RT-PCR were divided into three groups: lopinavir/ritonavir, remdesivir, and control. We assessed the efficacy of these drugs in reducing viral load and viral shedding duration using real-time RT-PCR and Vero E6 cell cultures. Lopinavir/ritonavir led to no detectable infectious SARS-CoV-2, with a median viral clearance time of one day, whereas one remdesivir-treated case remained culture-positive until day 12. Lopinavir/ritonavir significantly reduced viral load compared to remdesivir and control groups (p = 0.0117 and p = 0.0478). No infectious virus was detected in the lopinavir/ritonavir group, and the non-infectious SARS-CoV-2 proportion remained constant at 90%, higher than in the remdesivir and control groups (p = 0.0097). There was a significant difference in culture positivity among the groups (p = 0.0234), particularly between the lopinavir/ritonavir and remdesivir groups (p = 0.0267). These findings suggest that lopinavir/ritonavir reduces viral load and shortens the viral shedding duration compared to remdesivir, despite not being an effective treatment option.

Severe Fever with Thrombocytopenia Syndrome in South Korea, 2016-2021: Clinical Features of Severe Progression and Complications.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infection with a high case fatality rate. The serious clinical features need to be further defined. We performed a retrospective analysis among SFTS patients in South Korea during 2016-2021 to update the current status. The basic epidemiology of all reported cases was analyzed, and the detailed clinical data of the subjects were further collected from study hospitals selected in terms of their geographic location and capability of SFTS care. Cases of SFTS were reported across the country and were greatly increased since the initial endemic phase, even under the passive surveillance system. The case fatality rate remained at approximately 16.8%. Coinfections at admission were present in 7.8% of the patients. Major complications included bleeding (15.2%), hemophagocytic lymphohistiocytosis (6.7%), bacteremia or candidemia (4.0%), and invasive pulmonary aspergillosis (1.7%). It took a median 4 days from the onset of illness to hospital admission. Rapid clinical deterioration was observed with a median 1 day for intensive care unit admission, 3 days for mechanical ventilation, 4 days for renal replacement therapy, and 5 days for death, all after the hospitalization. Multivariate analysis showed that the fatality was associated with older age, bacteremia, or candidemia during hospitalization, and the presence of several variables at admission such as fever, altered mentality, aspartate aminotransferase >200 IU/L, serum creatinine level >1.2 mg/dL, and prolonged prothrombin time and activated partial thromboplastin time. Treatment options to improve clinical outcomes are limited, despite best supportive care. Specific treatment is urgently needed to change the fatal course.

A new species of the genus Euxiphocerus (Diptera, Dolichopodidae) from Korea with checklist and key to species of the genus.

Background: Euxiphocerus Parent, 1935 is a small genus consisting of three species only from the Afrotropical region. New information: During a survey of the long-legged flies from Korea, the authors discovered a new species, Euxiphoceruslignicola sp. nov. Detailed morphological characters and photographs of the new species, as well as a checklist and key to species of this genus, are provided herein. The two species described from Oriental China, Systenusjinxiuensis Lin & Yang, 2022 and S.sinensis Yang & Gaimari, 2004, are transferred to Euxiphocerus Parent, 1935 (comb. nov.) by similarity in external morphological characters.

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis.

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli-electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model's performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs. 0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

Risk Factors for Impaired Cellular or Humoral Immunity after Three Doses of SARS-CoV-2 Vaccine in Healthy and Immunocompromised Individuals.

Background: We aimed to identify the risk factors for impaired cellular and humoral immunity after three doses of the SARS-CoV-2 vaccine. Methods: Six months after the third vaccine dose, T-cell immunity was evaluated using interferon-gamma release assays (IGRAs) in 60 healthy and 139 immunocompromised (IC) individuals, including patients with hematologic malignancy (HM), solid malignancy (SM), rheumatic disease (RD), and kidney transplantation (KT). Neutralizing antibody titers were measured using the plaque reduction neutralization test (PRNT) and surrogate virus neutralization test (sVNT). Results: T-cell immunity results showed that the percentages of IGRA-positive results using wild-type/alpha spike protein (SP) and beta/gamma SP were 85% (51/60) and 75% (45/60), respectively, in healthy individuals and 45.6% (62/136) and 40.4% (55/136), respectively, in IC individuals. IC with SM or KT showed a high percentage of IGRA-negative results. The underlying disease poses a risk for impaired cellular immune response to wild-type SP. The risk was low when all doses were administered as mRNA vaccines. The risk factors for an impaired cellular immune response to beta/gamma SP were underlying disease and monocyte%. In the sVNT using wild-type SP, 12 of 191 (6.3%) individuals tested negative. In the PRNT of 46 random samples, 6 (13%) individuals tested negative for the wild-type virus, and 19 (41.3%) tested negative with omicrons. KT poses a risk for an impaired humoral immune response. Conclusions: Underlying disease poses a risk for impaired cellular immune response after the third dose of the SARS-CoV-2 vaccine; KT poses a risk for impaired humoral immune response, emphasizing the requirement of precautions in patients.

Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy.

There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.

Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images.

BACKGROUND AND PURPOSE: Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS: Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. RESULTS: In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSION: Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Comparison of two automated CT perfusion software packages in patients with ischemic stroke presenting within 24 h of onset.

Background: We compared the ischemic core and hypoperfused tissue volumes estimated by RAPID and JLK-CTP, a newly developed automated computed tomography perfusion (CTP) analysis package. We also assessed agreement between ischemic core volumes by two software packages against early follow-up infarct volumes on diffusion-weighted images (DWI). Methods: This retrospective study analyzed 327 patients admitted to a single stroke center in Korea from January 2021 to May 2023, who underwent CTP scans within 24 h of onset. The concordance correlation coefficient (ρ) and Bland-Altman plots were utilized to compare the volumes of ischemic core and hypoperfused tissue volumes between the software packages. Agreement with early (within 3 h from CTP) follow-up infarct volumes on diffusion-weighted imaging (n = 217) was also evaluated. Results: The mean age was 70.7 ± 13.0 and 137 (41.9%) were female. Ischemic core volumes by JLK-CTP and RAPID at the threshold of relative cerebral blood flow (rCBF) < 30% showed excellent agreement (ρ = 0.958 [95% CI, 0.949 to 0.966]). Excellent agreement was also observed for time to a maximum of the residue function (T max) > 6 s between JLK-CTP and RAPID (ρ = 0.835 [95% CI, 0.806 to 0.863]). Although early follow-up infarct volume showed substantial agreement in both packages (JLK-CTP, ρ = 0.751 and RAPID, ρ = 0.632), ischemic core volumes at the threshold of rCBF <30% tended to overestimate ischemic core volumes. Conclusion: JLK-CTP and RAPID demonstrated remarkable concordance in estimating the volumes of the ischemic core and hypoperfused area based on CTP within 24 h from onset.

SARS-CoV-2 antigenemia and RNAemia in association with disease severity in patients with COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, causes a spectrum of symptoms ranging from mild upper to severe lower respiratory tract infections. However, the dynamics of nucleocapsid (N) protein antigenemia and RNAemia are not fully understood. We conducted a cohort study involving 117 patients with clinically confirmed COVID-19, focusing on the kinetics of antigenemia and RNAemia and their association with various clinical characteristics. The patients had a median age of 66.0 years (52.0-79.0 years), with a gender distribution of 46.2% male and 53.8% female. Antigenemia reached 100% in fatal cases during the first week after admission. The sensitivity/specificity of antigenemia for diagnosis were 64.7%/73.0% at admission, 69.1%/100% in Week 1, and 66.3%/100% in Week 2. Additionally, the rates of antigenemia in asymptomatic patients were 27.3% upon admission and 22.0% in Week 1, respectively; however, no antigenemia was in samples collected in Week 2. Viral RNAemia was not detected in asymptomatic patients, but RNAemia viral loads were elevated in fatal cases. Kaplan-Meier survival curves demonstrated a higher mortality rate when antigenemia concentrations were elevated in the follow-up samples (P = 0.005). Our study provides a comprehensive analysis of the kinetics of viral N-protein antigenemia and RNAemia according to disease severity and clinical classification. Our findings suggest that highest concentrations of antigenemia in fatal cases occur in the first week after admission, indicating that early elevated antigenemia may serve as a marker of mortality risk.

Solving the 250-year-old mystery of the origin and global spread of the German cockroach, Blattella germanica.

The origin of the German cockroach, Blattella germanica, is enigmatic, in part because it is ubiquitous worldwide in human-built structures but absent from any natural habitats. The first historical records of this species are from ca. 250 years ago (ya) from central Europe (hence its name). However, recent research suggests that the center of diversity of the genus is Asian, where its closest relatives are found. To solve this paradox, we sampled genome-wide markers of 281 cockroaches from 17 countries across six continents. We confirm that B. germanica evolved from the Asian cockroach Blattella asahinai approximately 2,100 ya, probably by adapting to human settlements in India or Myanmar. Our genomic analyses reconstructed two primary global spread routes, one older, westward route to the Middle East coinciding with various Islamic dynasties (~1,200 ya), and another younger eastward route coinciding with the European colonial period (~390 ya). While Europe was not central to the early domestication and spread of the German cockroach, European advances in long-distance transportation and temperature-controlled housing were likely important for the more recent global spread, increasing chances of successful dispersal to and establishment in new regions. The global genetic structure of German cockroaches further supports our model, as it generally aligns with geopolitical boundaries, suggesting regional bridgehead populations established following the advent of international commerce.