The ongoing COVID-19 pandemic has seen the emergence of numerous SARS-CoV-2 variants, each posing distinct public health challenges. The XEC variant, a recombinant Omicron subvariant, has rapidly gained prevalence globally, raising critical questions about its potential implications on health systems and public policy. This paper examines the emergence, spread, and unique characteristics of XEC, especially in the context of the global decline of KP.3.1.1, another significant Omicron lineage. We discussed the public health implications, including vaccine effectiveness, genomic surveillance, and healthcare system preparedness, underscoring the need for adaptive strategies in response to evolving SARS-CoV-2 variants.

The SARS-CoV-2 genome encodes 16 nonstructural proteins (Nsps), with Nsp2 being the least conserved and understood. This study highlights a crucial role for Nsp2 in the early phase of the viral life cycle, particularly its interaction with GIGYF2, which relocates near double-membrane vesicles (DMVs) and enhances viral protein production. Deletion of the Nsp2-coding region from the viral genome led to a drastic reduction in viral RNA synthesis early in infection (3-4 h after infection). Interactome analysis in virus-infected cells identified GIGYF2, a host-encoded translational regulation protein, as a key Nsp2 partner. This interaction was confirmed for both SARS-CoV-1 and SARS-CoV-2. Depletion of GIGYF2 or its cofactor ZNF598 phenocopied the replication defects observed with Nsp2 deletion, suggesting their critical roles in viral reproduction. Upon infection, GIGYF2 and ZNF598 relocate to areas near DMVs, viral replication sites. This relocation does not occur with the Nsp2-deleted virus, indicating Nsp2's role in directing GIGYF2 to DMVs. Formaldehyde crosslinking and immunoprecipitation sequencing (fCLIP-seq) identified regions within viral RNAs that potentially interact with GIGYF2, including those encoding M and Orf6. Depletion of GIGYF2 resulted in decreased protein expression of M and Orf6. Our findings reveal the function of Nsp2 in supporting viral protein production by exploiting GIGYF2 as a host factor.

Both alcohol and cannabinoid misuse cause substantial societal problems individually, and cannabis is the most popular illicit drug used simultaneously with alcohol. The role of endocannabinoids (eCB) and cognate receptors in the regulation of inflammation is clinically relevant; however, the role of cannabinoid receptors (CBRs) specifically in pulmonary inflammation and associated lung pathobiology remains elusive. For this study, we investigated the effects of binge cannabinoid exposure on pathogen-induced pulmonary inflammation. We also describe a binge ethanol + cannabinoid adolescent mouse model of pathogen-induced pulmonary inflammation by Klebsiella pneumoniae (K. pneumoniae) infection. We show that adolescent cannabinoid exposure primes the lung to a more severe inflammation in adulthood, and this response is mitigated by cannabinoid antagonists. We also show that ethanol and cannabinoid pre-exposure followed by microbial challenge yielded CBR-dependent pulmonary immune activation via danger-associated molecular pattern (DAMP) release. This research may shed light on CB signaling as it relates to DAMPs and can provide a framework to develop potential novel therapeutics in polysubstance use disorders.

Interleukin-6 (IL-6), a pleiotropic cytokine, is induced by infection of influenza A virus (IAV), where it plays a pivotal role in immune defense and the regulation of inflammation. IAV induces IL-6 transcription upon recognition by pattern recognition receptors, which activate downstream signaling cascades, leading to the activation of transcription factors. Activated transcription factors subsequently regulate the expression of IL-6 in innate immune cells, such as macrophages, dendritic cells, and epithelial cells. IL-6 contributes to antiviral immunity by promoting the recruitment of immune cells to sites of infection and amplifying the inflammatory response. While optimal IL-6 production is essential for effective anti-IAV immunity, excessive IL-6 production can contribute to a dysregulated immune response, leading to a cytokine storm. In this context, IL-6 signaling, in coordination with other proinflammatory cytokines such as TNF-α and IL-1β, not only enhances its own production but can also serve as a key mediator of inflammation. This cascade can result in exaggerated immune responses, causing tissue damage and potentially leading to severe outcomes, including organ failure and death. Understanding the molecular mechanisms underlying cytokine storms presents important therapeutic opportunities. However, the precise pathways responsible for excessive IL-6 production and its dysregulation during IAV infection is not fully understood. This review explores the reported mechanisms regulating IL-6 induction in response to IAV and its innate immune role in IAV pathogenesis, highlighting existing research gaps in understanding IAV-induced IL-6 production and its impact on immune modulation. A deeper understanding of IAV-induced IL-6 production and signaling could inform the development of targeted therapies to more effectively manage influenza.

The COVID-19 pandemic may have affected morbidity patterns of residents in refugee centres, but empirical evidence is scarce. We utilised linked data from a health surveillance network in refugee centres of three German federal states, employing a quasi-experimental design to examine the effects of the COVID-19 pandemic on newly diagnosed medical conditions. Doctors coded routine diagnoses on-site in healthcare facilities for refugee patients. Our analysis encompasses the timeframe from October 2018 to April 2023 and includes individual-level data for 109,175 refugees. This data resulted in 76,289 patient-months across 21 refugee centres, with a total occupancy of 144,012 person-months. Here, we employ segmented regression analyses, adjusting for time trends, socio-demographic factors, occupancy, and centre characteristics, to evaluate the COVID-19 pandemic's impact on incident diagnosis patterns among refugees. We show how the COVID-19 pandemic altered diagnosis patterns among refugees in German centres. Notably, incidents of injuries, mental disorders, psychotherapeutic drug prescriptions, and genitourinary diseases rose, while respiratory diseases decreased, later rebounding. An increase in injury-related diagnoses suggests heightened violence experiences during flight or in centres. Mental disorder diagnoses and psychotherapeutic drug prescriptions rose, reflecting pandemic-related stressors and the pandemic's multifaceted impact on refugee health.

Patients infected with SARS-COV-2 were observed to have a significant occurrence of indeterminate results in interferon-gamma (IFN-ɣ) release assays (IGRAs). However, the results of IGRAs in a post-COVID-19 pandemic context were not studied. We aimed to compare the results of the QuantiFERON®(QFT)-TB Plus test before and after the SARS-CoV-2 pandemic.

We conducted a comparative study at the Immunology department comparing two groups of QFT-TB requests received for the screening of latent tuberculosis infection (LTBI): (A) a control group: including QFT-TB tests received before the COVID-19 pandemic (January 2018-December 2019), and (B) a "post-pandemic" group including QFT-TB tests performed between January and December 2022.

The study included 122 patients, with an average age of 44 years (standard deviation: 1.27) and a female-to-male ratio of 1.17. Of these, 12% exhibited lymphopenia, and 41% were undergoing treatment with immunosuppressive or corticosteroid medications. The control group (n = 60) and the post-pandemic group (n = 62) had comparable rates of negative and positive QFT-TB results (67% vs. 86%, and 5% vs. 6%, respectively). Indeterminate QFT-TB results were significantly higher in the post-pandemic group (8%vs.28%, p = 0.002). Lymphocyte count was significantly lower in the post-pandemic group (1540/μL [400-3430] vs. 2035/μL, p = 0.004) but remained within the lower limit of normal. There was an increased mitogen-induced IFN- ɣ production in the post-pandemic group (6.89 UI/mL [0.1-10] vs. 3.08[0.12-10], p = 0.007). IFN- ɣ production in Nil, Tb1, and Tb2 tubes, white blood cell count, and neutrophil count did not differ between the groups.

In a post-pandemic context, the specific response of T-cells to TB antigens does not seem to be affected. The increased mitogen-induced IFN- ɣ production contrasting with the decreased lymphocyte count is in favor of a sustained cellular immune activation.

Sampling of mammal communities across the United States during 2022-2023 detected evidence of SARS-CoV-2 antibodies in 3 new species and 2 previously described species and evidence of influenza A antibodies in 2 previously described species. Our analysis provides surveillance and sampling guidance for detection of rare exposure events.

Limited surveillance data have hindered understanding of SARS-CoV-2 transmission within prisons. We integrated routine surveillance data with viral sequencing to investigate transmission dynamics and associated factors during a Delta variant outbreak in a maximum-security prison in Sydney, New South Wales, Australia. Infection incidence and associated factors were determined by using person-time and Cox regression. We generated transmission chains by integrating epidemiologic and viral sequencing data. Of 1,562 patients, SARS-CoV-2 infection was diagnosed in 169 (11%), predominantly acquired in prison and asymptomatic. Prisonwide testing identified substantial unrecognized transmission, and 4 subvariants indicated multiple viral introductions. Infection was associated with housing location, having a cellmate (regardless of infection status), and vaccination status. Our findings underscore the inadequacy of symptom-based testing and the efficacy of entry-quarantine, strategic housing, extensive testing, and vaccination in reducing transmission. This integrated approach to surveillance and genomic sequencing offers a valuable model for enhancing infectious disease surveillance in correctional settings.

As observed in the case of COVID-19, effective vaccines for an emerging pandemic tend to be in limited supply initially and must be allocated strategically. The allocation of vaccines can be modeled as a discrete optimization problem that prior research has shown to be computationally difficult (i.e., NP-hard) to solve even approximately. Using a combination of theoretical and experimental results, we show that this hardness result may be circumvented. We present our results in the context of a metapopulation model, which views a population as composed of geographically dispersed heterogeneous subpopulations, with arbitrary travel patterns between them. In this setting, vaccine bundles are allocated at a subpopulation level, and so the vaccine allocation problem can be formulated as a problem of maximizing an integer lattice function [Formula: see text] subject to a budget constraint [Formula: see text]. We consider a variety of simple, well-known greedy algorithms for this problem and show the effectiveness of these algorithms for three problem instances at different scales: New Hampshire (10 counties, population 1.4 million), Iowa (99 counties, population 3.2 million), and Texas (254 counties, population 30.03 million). We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the submodularity ratio of the objective function g. The submodularity ratio of a function is a measure of how distant g is from being submodular; here submodularity refers to the very useful "diminishing returns" property of set and lattice functions, i.e., the property that as the function inputs are increased the function value increases, but not by as much.

This study aimed to develop novel nomogram models capable of predicting early neurological deterioration (END) and 90-day prognosis outcomes in acute ischemic stroke (AIS) patients treated with IV thrombolysis.

A retrospective derivation cohort comprising 298 AIS patients was enrolled from January 2021 to March 2024. A separate validation cohort of 94 patients was retrospectively enrolled between April 2024 and December 2024. END was defined as a sustained NIHSS increase of ≥ 2 points within 24 h, and poor prognosis outcome a mRS score of 3-6 at 90 days. Multivariate logistic regression analysis was employed to construct nomogram models for the prediction of outcomes.

In the derivation cohort, 23.83% patients with END experienced a poor 90-day outcome, as compared to 12.42% of those from NO-END group. Independent risk factors for END included a lower initial NIHSS score, a delayed DNT, a reduced ASPECTS score, and incomplete Willis Artery. END patients were found to be at a significantly increased risk of poor 90-day prognosis (odds ratio 0.163, p < 0.001). In addition, elevated initial NIHSS and glucose levels, the presence of nonlacunar infarction, and history of hypertension were predictive of poor prognosis. Two separate nomogram models, developed based on the previously identified risk factors to predict the occurrence of END and a poor 90-day prognosis, demonstrated AUC values of 0.740 (95% CI 0.686-0.789) and 0.859 (95% CI 0.814-0.896). These models also exhibited good discriminatory capacity in the validation cohort, with corresponding AUC values of 0.716 (95% CI 0.541-0.892) and 0.795 (95% CI 0.689-0.900).

This study has successfully constructed reliable nomogram models for forecasting END and poor 90-day outcomes in AIS patients treated with IV thrombolysis; thereby, facilitating personalized prediction of adverse outcomes and adjustment of treatment strategy.