COVID

BackgroundVitamin D supplementation has been investigated for potential associations with cardiometabolic risk factors related to cardiovascular disease (CVD); however, findings from randomized controlled trials (RCTs) remain inconsistent. This meta-analysis aimed to assess the effects of vitamin D supplementation on cardiometabolic risk factors--including lipid profile, blood pressure, and glycaemic parameters--and to explore whether age and baseline serum vitamin D concentrations modify these associations. Research Design and MethodsWe conducted a systematic review and meta-analysis of RCTs comparing oral vitamin D supplementation with placebo in adults. PubMed, the Cochrane Library, and ClinicalTrials.gov. Risk of bias was evaluated using the Cochrane tool, and pooled effect sizes with 95% confidence intervals (CIs) were calculated using random-effects models. Results14,051 abstracts were retrieved, of which 45 were used for data analysis. Vitamin D supplementation reduced low-density lipoprotein cholesterol (LDL-C) by 0.136 mmol/L (95%CI: -0.215, -0.56), systolic blood pressure by 2.79 mm Hg (95% CI: -4.648, -0.938), fasting blood glucose by -0.11 (95%CI:-0.185, -0.036), and hemoglobin A1c by 0.164% (95%CI: -0.322, -0.006) compared with placebo. Subgroup analyses revealed reductions in SBP and LDL cholesterol among participants aged [&ge;]55 years and reductions in fasting blood glucose in participants with age < 55 years. While favourable effects on fasting blood glucose and hemoglobin A1c were observed with a baseline blood level of vitamin D of concentrations (<50 nmol/L). ConclusionsVitamin D supplementation may be associated with modest modifications in selected cardiometabolic risk factors; including systolic blood pressure, LDL-cholesterol, fasting blood glucose, and hemoglobin A1c. Age and baseline vitamin D status appear to modulate these effects. The clinical relevance of these modest effects remains uncertain. Well-designed RCTs with standardized protocols are required to clarify potential effect modification by age and baseline vitamin D status. Trial RegistrationPROSPERO (CRD42020165293) FundingThis research received funding from the Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan

Background The Hump-nosed pit viper is a recognized but neglected medically significant species causing morbidity and mortality, with non-availability of a specific antivenom. There are many gaps in our understanding of its envenomation, including burden, clinical syndrome, complications and management. Methodology The study is a retrospective sub analysis of the Prospective VENOMS registry and hospital records of Hump Nosed Pit Viper envenomation from a single tertiary care center in coastal Karnataka from May 2018 to March 2024. Epidemiology, syndrome, complications and treatment strategies have been described. A linear mixed model analysis was conducted to study the effect of different therapeutic interventions in combating venom induced consumptive coagulopathy (VICC) Principal Findings Of 46 cases, 24 patients had VICC. The most common complications were AKI (21.7%), TMA (10.9%) and stroke (4.4%). Anaphylaxis to ASV (23.9%) was the most common therapeutic complication. Therapeutic interventions included ASV, administration of blood products and therapeutic plasma exchange along with supportive care. The linear mixed model revealed that administration of blood products (p=<0.001) had the strongest influence on the INR value, however, often resulting in a transient decline in INR value. ASV (p=0.052) caused only marginally significant change in INR. The role of TPE could not be statistically inferred, however, individual cases with severe VICC improved without complications, therefore it required further study but can be considered in critical cases. Conclusions/Significance This study describes the syndrome of hump-nosed pit viper envenomation, while highlighting the urgent need for a species-specific antivenom, recommends treatment strategies that can be used in the interim. Additionally, geo-spatial mapping draws attention to hotspots and the hypothesis that HNPV in coastal Karnataka have regionally distinct toxicity trends.

COVID-19 has been shown to cause a range of harmful long-term effects on nearly every organ system1-3. These findings are based on retrospective studies comparing COVID-19 patients to patients with similar medical histories and demographics but no COVID-19 diagnosis4-16. However, concerns have emerged that these comparisons may be biased if COVID-19 patients had unrelated health conditions or other factors not recorded in their medical records17-21. Here, using a massive dataset of 14.4 billion health insurance claims from 244.7 million U.S. patients, we find that the large majority of long-term effects attributed to COVID-19 by methods used in conventional studies are likely due to bias from selective testing. This bias arises because individuals with non-COVID health conditions producing long-term symptoms were more likely to seek care and be tested for COVID-19. As a result, their non-COVID symptoms are attributed to COVID-19. We develop a study design that reduces this bias by only considering individuals who have taken a COVID-19 PCR test, and then comparing similar patients whose first test came back positive vs. negative. This way the COVID-19 patients and control group are more similar and non-COVID factors play less of a role. We examine 614 clinical outcomes over a 2-year followup period and reveal an order of magnitude smaller--but still clinically significant--number of long-term effects attributed to COVID-19 that persist for up to one year after infection. We confirm that the long-term effects of COVID-19 span many organ systems, including respiratory, cardiovascular, musculoskeletal, and integumentary systems, but are significantly narrower in scope and duration than previously believed. Although some symptoms exist more than one year after COVID-19 infection, they occur at similar rates in individuals who tested negative and are therefore not attributable to COVID-19 infection. Our findings pinpoint the specific long-term effects of COVID-19 and show how large-scale data can be used to enable careful evaluation and design of population health studies.

1The timeliness of infectious disease surveillance systems largely determines the speed at which mitigation interventions may be implemented. However, it is unclear how surveillance timeliness evolves during a pandemic with changing government policies, testing tools, and population-level infection and immunity landscapes. Here, we adapt an agent-based model for COVID-19 transmission to explore the timeliness of the surveillance signals obtained from polymerase chain reaction (PCR) and rapid antigen (RAT) tests relative to true infection incidence. Across different pandemic scenarios, we investigate how surveillance timeliness depends on the prevalence of co-circulating influenza-like-illnesses (ILI) and test quality. If only PCR tests are available with symptom-based eligibility, and if tests can detect post-recovery residual viral load, then a surveillance lag may emerge which is amplified by ILI prevalence. When limited RATs are introduced with symptom-based eligibility, and PCR eligibility requires a recent positive RAT, then RAT/PCR timeliness is sensitive to ILI prevalence but insensitive to RAT failure probability. With unrestricted RAT supply, PCR timeliness varies with both ILI prevalence and RAT failure probability. Our work highlights how the timeliness of test-based surveillance signals can evolve throughout a pandemic, with important implications for interpreting real-time surveillance data and designing more effective, data-driven surveillance systems.

In the three years since Omicron emergence, SARS-CoV-2 dynamics have exhibited persistent twice-yearly waves in the United States, peaking in late summer and winter, with heterogeneity in timing and intensity across states. This semiannual pattern sharply contrasts with typical annual respiratory pathogen dynamics in the US, yet their underlying mechanisms and whether this pattern will persist remain poorly understood. Here, we tested several hypothesized mechanisms and found that a combination of waning immunity, climatic factors of relative humidity and temperature, variant activity, and vaccination captured divergent patterns in COVID-19 hospitalization incidence across 10 US states, from January 2022-November 2024. Applying a compartmental disease model, we identified that waning infection-derived immunity was the dominant driver of semiannual SARS-CoV-2 dynamics, with climate factors shaping the timing and magnitude of seasonal waves across US states. Scenario analyses indicated that if infection-derived immunity remains short in duration, semiannual dynamics influenced by climate are likely to persist, with attenuation in severe disease over time. In contrast, more durable infection-derived immunity, or a slower rate of immune-evading viral evolution, could lead to an epidemiologic transition to annual dynamics. In some states, summer waves approached the magnitude of winter waves, likely reflecting local climatic influences on transmission, suggesting that optimal vaccination strategies may vary by state. These findings have broad implications for understanding epidemic dynamics and informing vaccine policy, including seasonal timing and two-dose vaccine schedules for high-risk persons.

Herpes simplex virus (HSV) is an endemic pathogen, infecting most adults world-wide. HSV infection can cause a wide spectrum of disease outcomes, ranging from asymptomatic infection or mild lesions to rare cases of infectious keratitis, encephalitis, and death. HSV genome sequences have been shown to differ between individual patients, as well as within individuals. To date, the vast majority of publicly available HSV genomic data has come from Europe and North America. Our current understanding of these patterns are missing data from under-sampled populations, particularly in South America, Africa, and Asia. Also missing have been HSV samples from non-industrial (e.g., agricultural, pastoral) populations, for which the natural environment plays a large role in health and disease dynamics. In this study, we capitalized on Whatman FTA card stabilization of DNA to develop a procedure for capturing oral and genital swabs from a geographically isolated pastoralist population in a desert region of northern Namibia. These are the first data to document HSV diversity in this type of remote setting. These are also the first HSV genomes from Namibia. These approaches may prove useful in broadening the accessibility of viral detection for these chronic pathogens, help improve diagnostics, and raise public health awareness about the burden of these pathogens in under-served populations.

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease model to address two questions: (1) How much error is introduced in key model outcomes by neglecting vaccine unwillingness?, and (2) Can the error be reduced by incorporating vaccine unwillingness into the vaccination rate constant rather than the rate diagram? The answers depend greatly on the time scale of interest. For the endemic time scale, where longterm behavior is studied with equilibrium point analysis, the error in neglecting unwillingess is large and cannot be improved upon by decreasing the vaccination rate constant. For the epidemic time scale, where the first big epidemic wave is studied with numerical simulations, the error can still be significant, particularly for diseases that are relatively less infectious and vaccination programs that are relatively slow.

Strengthening in-country sequencing capacity generated 28 Lassa virus genomes from human clinical cases, expanding our knowledge of Lassa fever in Guinea. Phylogeographic analysis revealed cross-border exchange between Liberia and the NZerekore region, and a Sierra Leone introduction into the Gueckedou area. Enhanced genomic surveillance is crucial to guide future public health actions.

BackgroundMeasles is a highly contagious infectious disease and a leading cause of childhood morbidity and mortality worldwide. In developing country like Ethiopia, effective immunization is a proven strategy for reducing measles related illness and deaths. However, measles second dose vaccination drop out has become a major public health concern. In a densely populated city such as Addis Ababa drop rate tends to be higher than the minimum acceptable threshold, leading to increased number of cases and recurrent outbreaks. Despite of this limited evidence exists on the determinants of second dose drop out and the problem is not well investigated, as a result this study will try to identify determinants of measles second dose vaccination dropout among children 24 - 35 months of age. ObjectivesTo identify determinants of measles second dose vaccination dropout among children 24 - 35 months of age Addis Ababa, Ethiopia in 2025. MethodCommunity based unmatched case control study was conducted in Addis Ababa from September 1/2024 to October /2025 with a total of 636 participants, consisting of 212 cases and 424 controls. Data were collected using structured Quesionariie and entered into EpiData 3.1 then StataSE 18 was used for detailed analysis including Descriptive statistics. Model fitness was checked using Hosmer-Lemeshow and multicollinearity were assessed using variance inflation factor. Furthermore, Bivariable and multivariable logistic regression analyses was employed and Adjusted odds ratio with 95% confidence intervals was used to identify significant variables. ResultsA total of 620 mothers/caregivers participants respond to the study, comprising 206 (97%) cases and 414(97.6%) controls, yielding a total response rate of 97.4%. In this study, waiting time longer than 30 minutes (AOR= 3.34, 95%CI: 1.86-5.9), Lack of counseling (AOR = 2.63, 95% CI: 1.60-4.30), Lack of reminders (AOR = 2.86, 95% CI: 1.89-4.30), Previous adverse event following immunization (AOR = 2.00, 95% CI: 1.39-3.00), postnatal care visit (AOR = 0.58, 95% CI: 0.40-0.85) and family size of greater than 3 (AOR = 1.96, 95% CI: 1.29-2.98) were significantly associated with measles second dose dropout. Conclusion and recommendationIn study shows measles second dose dropout is found to be associated with long waiting time, lack of counseling, lack of reminder, history of adverse event following immunization and postnatal visit. Which suggests Strengthening Immunization Counseling, reducing waiting time, establishing effective reminding system, integrating Immunization with postnatal services and promptly addressing concerns about adverse event following immunization can help reduce measles second dose dropout.

BackgroundBoth hemorrhagic fever with renal syndrome (HFRS) and scrub typhus (ST) are acute zoonotic infectious diseases. There is an overlap in their epidemiological characteristics and clinical manifestations, posing challenges for early differential diagnosis. This study aims to identify predictive factors for these two diseases to provide a basis for early diagnosis. Method/FindingsA retrospective analysis was conducted on the clinical data of patients diagnosed with HFRS and ST at the First Affiliated Hospital of Dali University. Logistic regression analysis was employed to explore independent risk factors for the early differential diagnosis of these two diseases, and a nomogram model was constructed based on these risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The nomogram was utilized to visually present the predictive variables. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. ResultsA total of 235 patients each with HFRS and ST were included in this study. After adjusting for confounding factors, the results of multivariate logistic regression analysis revealed that sex (male) (adjusted odds ratio [ajOR]: 2.093, 95% confidence interval [CI]: 1.107 - 3.957, P = 0.018), positive proteinuria (ajOR: 4.937, 95% CI: 2.427 - 10.042, P < 0.001), creatinine (CREA) (ajOR: 1.009, 95% CI: 1.003 - 1.015, P = 0.005), heart rate (ajOR: 0.981, 95% CI: 0.966 - 0.997, P = 0.018), and conjunctival congestion (ajOR: 16.167, 95% CI: 5.326 - 49.072, P < 0.001) were independent risk factors for differentiating HFRS from ST. The AUC of the model constructed based on these five independent risk factors was 0.856. ConclusionSex (male), positive proteinuria, elevated CREA, decreased heart rate, and conjunctival congestion are effective predictive factors.

Health research priorities are generally not aligned with global disease burden. Although genome-wide association studies (GWAS) are correcting a historical bias by including samples from different demographic groups, this does not necessarily translate to improved understanding of the most important causes of disease globally. We demonstrate that while in countries with high socioeconomic development index (SDI) there is some alignment between the traits being analysed in GWAS and those that contribute most to disease burden, there is almost no such alignment in countries with low SDI. Improvement in alignment between GWAS and disease burden has been seen for countries with middle SDI over time, likely due to the contributions to disease burden changing in those regions rather than GWAS responding to the needs of those regions. Low GWAS alignment with disease burden may be partially explained by lower GWAS attention to childhood health. Improving aetiological understanding of high burden neglected conditions should be a priority for emerging biobanks in order to reduce global health inequality. Short abstractWe identify some alignment between the traits being analysed in genome-wide association studies (GWAS) and disease burden in high socioeconomic development index (SDI) countries, while there is almost no such alignment in countries with low SDI, mostly due to neglecting childhood infection. Improvement in alignment between GWAS and disease burden has been seen for countries with middle SDI over time likely due to changing disease burden.

BackgroundDengue fever is a major neglected tropical disease with a rapidly rising global burden, and localized outbreaks are increasingly reported in southern subtropical China. Fujian Province, a coastal subtropical region with favorable ecological conditions for Aedes albopictus breeding and frequent cross-border exchanges with dengue-endemic areas, has had continuous local dengue cases for over a decade, raising concerns about the establishment of a stable natural endemic focus. Sustained local dengue transmission is defined by four core criteria, but no systematic assessment of these criteria has been conducted for Fujian using long-term multi-dimensional surveillance data. We aimed to evaluate whether a natural endemic focus for sustained local dengue transmission has been established in Fujian Province from 2014 to 2024 using four core evidence dimensions. MethodsWe extracted data on imported and locally acquired dengue cases in Fujian from 2014 to 2024 from Chinas National Notifiable Disease Reporting System (NNDRS). Serological surveillance for dengue IgG antibodies and virological surveillance for dengue virus in Aedes albopictus were conducted at seven sentinel sites. The study period was stratified into three phases based on the impact of COVID-19 non-pharmacological interventions: pre-pandemic (2014-2019), pandemic(2020-2022), and post-pandemic(2023-2024). Descriptive epidemiological analysis and data visualization were performed using R software (version 4.4.1), with t-tests for continuous variables and {chi}{superscript 2} tests for categorical variables. ResultsA total of 3,606 dengue cases were reported in Fujian during the study period, including 1,229 imported and 2,377 locally acquired cases. Key findings were as follows: (1) Temporal distribution: Local dengue transmission was completely interrupted during the 2020-2022 COVID-19 pandemic (0 local cases, only 26 imported cases), and resumed at a low level in 2023-2024 (160 local cases). (2) Serology: The overall population dengue IgG antibody positivity rate was 4.2% (66/15,736), with no statistically significant difference between pre-epidemic (3.8%, 30/7,835) and post-epidemic seasons (4.5%, 36/7,901; P=0.48), and no year with a positivity rate exceeding 10%. (3) Vector surveillance: Only one dengue virus-positive sample was detected among 385,000 Aedes albopictus mosquitoes collected during routine surveillance (Taijiang District, Fuzhou, October 2017), with no viral nucleic acid detected in all other samples. (4) Age distribution: The mean age of locally acquired cases (46.1{+/-}19.8 years) was significantly higher than that of imported cases (35.8{+/-}11.2 years, P<0.001), and local cases were concentrated in the middle-aged group (40-60 years) with no child-dominant pattern observed. ConclusionsFujian Province has not established a stable natural endemic focus for sustained local dengue transmission, and imported cases are the primary driver of local outbreaks in the region. Strengthened surveillance and early management of imported cases, integrated vector control targeting Aedes albopictus, and targeted public health education are critical and essential strategies to prevent the establishment of a dengue natural endemic focus in Fujian and other subtropical coastal regions with similar epidemiological characteristics. Author SummaryDengue fever is a rapidly spreading neglected tropical disease worldwide, and southern China faces persistent threats of local transmission due to favorable ecological conditions for mosquito breeding and frequent cross-border travel. Fujian Province, a subtropical coastal region in southeastern China, has reported annual local dengue cases for over a decade, raising public health concerns about the potential establishment of a stable natural endemic focus--where the virus circulates sustainably without relying on imported cases. To address this critical question, we conducted a comprehensive 11-year assessment (2014-2024) of dengue transmission in Fujian using four key evidence dimensions defined for identifying dengue endemic foci: the continuity of local cases independent of imported sources, population antibody levels, dengue virus detection in local mosquitoes (Aedes albopictus), and the age distribution of infected patients. We also leveraged the COVID-19 pandemic(2020-2022) as a unique natural experiment, during which strict travel restrictions drastically reduced imported dengue cases, to test whether local transmission could persist on its own. Our findings showed that local dengue transmission in Fujian completely stopped during the COVID-19 pandemic and only resumed when cross-border travel and imported cases recovered, confirming local transmission is entirely dependent on imported virus sources. Additionally, the local population had a very low dengue antibody positivity rate (4.2%), dengue virus was detected in only one mosquito sample over 11 years of surveillance, and local cases were concentrated in middle-aged adults (not children--the typical group affected in endemic areas). Together, these results confirm that Fujian Province has not established a stable natural endemic focus for dengue fever. While no endemic focus exists yet, Fujian remains at high risk of imported-driven local outbreaks due to its climate and cross-border exchanges. Our study highlights three critical strategies to prevent the future establishment of a dengue endemic focus in Fujian and other similar subtropical coastal regions: strengthening surveillance and early response for imported dengue cases, implementing targeted mosquito control measures during peak transmission seasons, and conducting public health education to raise awareness of dengue prevention. These evidence-based interventions are key to blocking the formation of sustained local dengue transmission and protecting regional population health.

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

BackgroundFood systems--particularly livestock production--account for substantial greenhouse gas (GHG) emissions, while unhealthy diets, characterized by excessive animal-based and insufficient plant-based food consumption, are a major risk factor for all-cause mortality in Europe. Implementing climate mitigation policies related to the GHG emissions of the food system could therefore bring important health co-benefits. MethodsWe developed a health impact assessment model based on a life table approach and evaluated the mortality impact of transitions in food consumption through four contrasting scenarios leading to net-zero GHG emissions for France in 2050. These involved varying dietary shifts, all moving toward more plant-based foods. For each scenario, we modeled the evolution of the diet, as well as the impacts on all-cause mortality by applying the most recent and robust dose-response relationships derived from meta-analyses for 13 food groups. FindingsThe different trajectories of dietary shifts translated into a health impact ranging from 19% [uncertainty interval, UI: 17%-21%] to 24% [UI: 21%-26%] of all-cause mortality prevented in 2050 in the French population. Variation in intakes of nuts, red meat, processed meat, whole grains and legumes bring most of the health benefits. Whatever the parameters chosen in the sensitivity analyses, the results remained robust, with about 100,000-200,000 deaths that could be prevented yearly by 2050 in France. InterpretationThe present study highlights the considerable potential health benefits that trajectories toward net-zero emissions can bring, especially through shifts toward sustainable diets. These results reinforce the strong convergence of environmental and human health issues in the agri-food sector. FundingFrench High Council for the Future of Health Insurance (HCAAM) and the National Agency for Ecological Transition (Ademe). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSFood systems are a significant contributor to climate change and in parallel, dietary risks are one of the leading causes of all-cause mortality globally, notably in high-income countries such as France. A recent systematic review by Moutet et al. revealed that only two studies evaluating health co-benefits through dietary shifts in net-zero GHG emissions scenarios were published to date. This suggests a convergence and a possible win-win situation between climate change and human health challenges regarding food production and consumption. In order to face the climate crises, governments around the world, and particularly those of the countries historically the largest contributors to climate change, must cut their greenhouse gas emissions to achieve net-zero emission by 2050. Dietary shifts would be a major driver to pursue this objective and could bring important health benefits to the population conducting these changes. For instance, Hamilton et al. showed that dietary changes in line with the Paris Agreements could result in 188 deaths prevented per 100,000 persons in 2040 in Germany and 141 in the UK. Added value of this studyOf the two previously published studies, only one assumed a gradual implementation of changes in diets, combined with a time lag in health effects. We also made these assumptions and considered the gradual change in consumption of thirteen food groups for which recent meta-analyses provided all-cause mortality dose-response relationships with a high level of quality. This study is also among the first to combine nutritional and environmental optimization, through four scenarios; all of which are expected to lead to net-zero emission by 2050 via very contrasting climate change mitigation trajectories. Nevertheless, all of them require a dietary shift toward more plant-based foods. We conducted a health impact assessment for France and showed that achieving net-zero emission by 2050 while considering nutrition references set by national guidelines would provide health co-benefits. Depending on the scenarios, health gains could range from 19% to 24 % of all-cause mortality prevented in the adult French population in 2050, compared to a scenario assuming that we would maintain the current observed dietary habits in the future. Implications of all the available evidenceThis study adds to the available evidence that taking action to mitigate climate change is an opportunity to strongly improve public health. Engaging populations in a shift toward a healthier and more sustainable diet could bring major human health and environmental benefits.

BackgroundThe never-ending emergence of superbugs casts a shadow over the victorious age of antibiotics. In fact, the triumph of antibiotics was previously viewed in retrospection as our final victory over bacteria. Bacteria like Klebsiella pneumoniae, Acinetobacter baumannii, and Escherichia coli are now raising an alarming number of infections across hospitals and communities around the globe. The objective was to evaluate the implications for antimicrobial stewardship based on identifying the antibiotic resistance profiles, genotype mechanisms, and trends in common pathogenic bacteria found in various hospitals across Iraq. MethodsWe used a two-fold approach that was comprehensive in scope and involved both efficient multicenter surveillance as well as cutting edge genetic analysis to unravel the complex topography of antibiotic resistance. We provided a geographically heterogeneous but diverse set of clinically obtained isolates to participate in hospitals for a period of 24 months and concentrated our efforts on prioritized pathogens K. pneumoniae, A. baumannii, E. coli, P. aeruginosa, and S. aureus that are well known to pose serious threats. Beginning with clinically obtained isolates sourced across the entire globe, we used standardized techniques such as broth microdilution to first undertake phenotyping in a central reference lab to establish microbial identity based on resistance phenotypes to a set of prioritized antibiotics that include carbapenems, third generation cephalosporins, or fluoroquinolones. Finally, we derived data concerning the emergence patterns and geographic distribution of resistant microbes such as MRSA or CRE. We used genome-wide sequencing to unlock information concerning the genetic blueprints for a set of specifically chosen isolates based on their representational diversity across geographic locales, resistance phenotypes, and specific times. ResultsThe sample was made up of Escherichia coli (n = 225), Klebsiella pneumoniae (n = 185), Staphylococcus aureus (n = 135), Pseudomonas aeruginosa (n= 90), and Acinetobacter baumannii (n = 125). Ceftriaxone resistance was found in 80.4% of E. Coli, ciprofloxacin resistance in 45.6%, and meropenem resistance in 15.1%. K. pneumoniae exhibited 38.9% resistance to aminoglycosides and 70.2% resistance to carbapenems. The percentage of MRSA in S. aureus was 55.5%. P. aeruginosa showed 22.2% resistance to colistin, 37.8% resistance to piperacillin tazobactam, and 50.0% resistance to ceftazidime. Imipenem resistance was found in 85.6% of A. baumannii isolates, whereas colistin resistance was found in 28.8% of isolates. In all, 3.4% of isolates are pan-drug-resistant (PDR), 14.6% are extensively drug-resistant (XDR), and 52.1% are multidrug-resistant (MDR). WGS identified common genes such bla_NDM-1, bla_OXA-48, mcr-1, aac (6)-Ib, and plasmid replicons IncF, IncL/M, and IncI2. Carbapenem resistance in Gram-negative bacteria rose by around 18% over the course of five years. ConclusionsThis study shows that the rapid spread of complex genetic information in bacteria causes antibiotic resistance problems. High-level resistance represents an expected consequence of the spread of resistance genes and successful bacteria within healthcare systems. We demonstrate in our results that our expertise in overcoming resistance at a molecular level will play a crucial role in combating infectious diseases in the coming years.

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan during the 2024-2025 seasonal influenza peak, highlighting the continuous need for improved predictive tools. However, forecasting hospitalization surges at a local scale remains a methodological challenge because the (very) low incidence numbers are subject to strong stochasticity and therefore require additional input of information and dedicated approaches. This study investigates the potential for early forecasting of respiratory infection peaks by analyzing ER visit trends. By clustering all-cause ER visits during the 2023-2025 winter seasons from the Nimes University Hospital (France), we identified a strong temporal correlation between early pediatric hospitalizations ([&le;]5 years old) and the following weeks adult hospitalization incidence for respiratory infections. The results suggest that tracking hospital admissions of pediatric ER visits, even without hospital care needs, can serve as a valuable early warning signal for upcoming peaks in respiratory-related hospitalizations. This predictive approach could improve hospital preparedness and resource management during seasonal influenza outbreaks. Author summaryThe epidemics of respiratory viruses present a significant challenge to hospitals in the temperate zone on an annual basis. Frequently, the hospital overload is mitigated by the late reactive allocation of human and material resources that are, hence, suboptimal. This study proposes a statistical framework to assist hospitals in anticipating bed requirements during seasonal influenza waves, despite high noise at the local level, by enhancing hospitalization forecasting with emergency room (ER) visit data. The prediction of the adult epidemic peak is possible through the analysis of the respiratory pediatric ER visits, which facilitates hospital management.

This exploratory analysis of PAX LC, a Phase 2, 1:1 randomized, double-blind, superiority, placebo-controlled trial examined whether treatment with nirmatrelvir/ritonavir (NMV/r) versus placebo/ritonavir (PBO/r) in individuals with Long COVID could reveal immune features associated with symptom improvement. Eighty-two participants (n=45 PBO/r; n=37 NMV/r) provided blood samples at baseline (Day 0) and post-treatment (Day 28). Baseline demographic and immunological phenotypes were similar in the two groups. No significant differences were observed in major immune cell populations or organ function markers between NMV/r vs. PBO/r groups, or before vs. after the treatment. Modest hematologic changes were noted in the NMV/r arm. SARS-CoV-2-specific IgG levels remained constant, with changes in total immunoglobulin subtypes and isotypes in both arms. Both arms showed similar shifts in cytokine levels. Notably, the levels of S1 and Spike proteins in circulation remained unchanged post-treatment. Regardless of the treatment arm, participants with self-reported symptom improvement showed reductions in the level of the inflammatory chemokine RANTES. Taken together, the findings of this study demonstrate limited virological and immunological changes in response to nirmatrelvir, contributing insights into the reason for the lack of benefit of the 15-day NMV/r treatment in Long COVID.

Introduction Heat waves are increasingly frequent and linked to higher mortality risks in Hong Kong. However, estimates of total excess mortality associated with heat waves remain unavailable. This study quantifies excess deaths associated with heat waves in Hong Kong from 2014 to 2023. Methods Daily age- and sex-specific mortality rates and population data were obtained from the Hong Kong Life Tables and Census and Statistics Department. Temperature data came from the Hong Kong Observatory, and relative risks were derived from local research. A Monte Carlo simulation was used to estimate heat-attributable deaths under different heat wave definitions, calculating total excess deaths and annualized death rates per 100,000 population. Results Between 2014 and 2023, heat exposure resulted in an estimated 1,455 (95% CI: 1,098-1,812) to 3,238 (95% CI: 3,234-3,242) excess deaths. In 2023, annualized excess death rates ranged from 2.95 (95% CI: 2.41-3.50) to 5.09 (95% CI: 5.07-5.12) per 100,000 people. Males and individuals aged 65 or older were disproportionately affected. Conclusion Over the 10-year study period, 1,455 to 3,238 excess deaths in Hong Kong were attributed to extreme heat. Heat waves now rank among the top ten causes of death in Hong Kong, with mortality rates comparable to diabetes. These findings underscore the need for urgent public health interventions to mitigate the impact of extreme heat.

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending lifestyle modifications. In the present study, we developed NHS based on genetic, lifestyle and biochemical variables associated with eight neurological disorders - dementia, stroke, Parkinsons disease, amyotrophic lateral sclerosis, schizophrenia, bipolar disorder, multiple sclerosis and migraine. UK Biobank data from Caucasian individuals was used to develop the model, and the data from individuals of Indian ethnicity was used to validate the model. Logistic regression and XGBoost algorithms were used in selecting the significant variables for the disorders. NHS developed from the selected variables was found to be very significant after adjusting for age and sex (AUC:0.6, OR: 0.95). Higher NHS was associated with a lower risk of neurological disorders and better social well-being. Highest NHS group (top 25%) showed 1.3 times lower risk compared to the rest of the individuals. Results of our study help in developing a framework for quantifying the neurological health in clinical setting.

BackgroundInterleukin-6 (IL-6) is a cytokine that plays a key role in systemic hyperinflammation and may mediate the relationship between acute COVID-19 and severe long-term outcomes such as Long COVID or death. IL-6 modulating drugs may reduce patients risk of severe post-COVID-19 outcomes. MethodsWe conducted an emulated target trial in a retrospective cohort of patients with moderate-to-severe rheumatoid arthritis who were prescribed IL-6 receptor antagonists (sarilumab or tocilizumab, pooled treatment) or other biologic agents (anakinra or baricitinib, pooled comparator) in 2022. We compared the 12-month cumulative incidence of mortality and Long COVID (diagnosed and probable) between groups using Super Learner and targeted maximum likelihood estimation, adjusting for covariates of interest. ResultsIn our cohort of 3,553 patients, we found that prescription of IL-6 receptor antagonists was associated with a lower 12-month cumulative mortality (adjusted relative risk (aRR) 0.40, 95% CI 0.27, 0.59), diagnosed Long COVID aRR 0.42, 95% CI 0.23, 0.78), and probable Long COVID (aRR 0.71, 95% CI 0.61, 0.83), compared to prescription of other biologic agents, among rheumatoid arthritis patients. ConclusionsIL-6 receptor antagonists may prevent the incidence of severe post-COVID-19 outcomes, such as Long COVID or mortality. This supports the hypothesis that IL-6 may be a mechanistic biomarker of COVID-19 sequelae and that acute COVID-19 severity may mediate this relationship.