Biology

Maternal smoking during pregnancy is associated with adverse effects on offspring health through impaired placental structure and function. Nicotine and other tobacco-related compounds readily cross the placental barrier, disrupt metabolic pathways, and increase the risk of long-term developmental disorders in newborn. Here, placental metabolic alterations associated with maternal smoking exposure were examined with metabolomics. We used placental samples from the Kuopio Birth Cohort study from 23 nonsmoking controls pregnancies, 19 pregnancies with early smoking exposure (cotinine detected in first-trimester but not in at-term samples), and 13 pregnancies with continuous smoking-exposure (cotinine detected in both first-trimester and at-term samples). Differences in placental metabolomic profiles were seen between controls and both smoking-exposed groups. For example, increased activity of xenobiotic metabolism pathways showed as elevated CYP1A2-related metabolites, e.g., aminoamide local anesthetic metabolite detected in both smoking-exposure groups (p=0.0042 and 0.0019, respectively). Disruptions in amino acid metabolism were observed, e.g., reduced placental tryptophan levels (p=0.0209 and 0.0237). Placentas from women who quit smoking during showed markers of reduced oxidative stress, lower oxidized glutathione (p=0.0119) and higher ergothioneine (p=0.0426) levels. These findings indicate that many smoking-related effects on the placental metabolome persist beyond acute nicotine exposure, showing long-term biological effects of maternal smoking during pregnancy. Plain language summarySmoking during pregnancy can possibly change how the placenta functions, which also affects the newborns long-term health. In this study, we compared placentas from nonsmokers, women who quit during pregnancy, and those who kept smoking. Clear chemical differences were seen in the placentas of smoking exposed pregnant women. The main changes included lowered levels of tryptophan and glutathione, which are important for growth and protection from stress. These results show that smoking-related changes in the placenta can persist beyond active nicotine exposure.

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.

Accurate identification of unknown pathogens is critical for medicine and public health, yet current metagenomic workflows remain heavily dependent on specialized bioinformatics expertise and manual interpretation, creating substantial bottlenecks in time-sensitive diagnostic settings1. The key challenges lie in achieving precise species identification amidst high background noise and translating complex microbial data into clinically actionable insights2,3. Here we present the Global Pathogen Analysis System (GPAS), an integrated computational framework that combines rapid and accurate pathogen identification with large language model (LLM)-based semantic interpretation. Central to GPAS is a dynamic-library alignment mechanism informed by prior probabilities of inter-species misclassification. By integrating a hybrid machine learning model that couples elastic neural networks with Bayesian inference, this approach substantially reduces both false positives and false negatives, achieving species-level accuracy superior to existing state-of-the-art tools. To enable clinical interpretation, we constructed a unified microbial knowledge graph integrating global metagenomic and metaviromic sample repositories, and trained a pathogen-specialized LLM agent. Through end-to-end reinforcement learning, the agent autonomously executes multi-step reasoning workflows extracting pathogen-specific insights from complex data and generating human-readable, evidence-based reports. Application to throat swab samples demonstrates that GPAS not only accurately identifies pathogenic microorganisms but also reveals how SLE-associated immune dysregulation reshapes the respiratory microbiome and promotes pathobiont overgrowth, providing clinically instructive interpretations. By substantially lowering technical barriers to pathogen identification, GPAS offers an accessible yet powerful platform for clinical diagnostics, public health surveillance, and microbiome research. The system is freely available at: https://gpas.nh.ac.cn/.

There is an unmet need to identify biomarkers of active thyroid eye disease (TED). scRNAseq revealed that orbital fibroblasts from orbital decompressions in people with TED express high levels of thyroid hormone receptors, growth factor receptors, including insulin-like growth factor 1 receptor (IGF1R), and extracellular matrix proteins including SPARC (osteonectin), whereas orbital fat endothelial cells expressed thyroid peroxidase (TPO). SPARC was significantly raised in the serum of people with thyroid disease compared to healthy controls. Furthermore, those with moderate, severe and sight threatening TED had higher SPARC levels than those with thyroid disease but free of TED or mild TED. Free-triiodothyronine (FT3) levels were positively correlated with SPARC in moderate-sight threatening TED. SPARC and IGF1 were positively correlated across people with thyroid disease alone, as well as TED. Thyroid stimulating hormone (TSH) levels were negatively correlated with SPARC in moderate-sight threatening TED. When participants were followed longitudinally, SPARC decreased after the active phase of TED. At the protein level, immunohistochemistry indicated that SPARC was heterogeneously expressed by fibroblasts in both control and TED orbital fat. SPARC is a key mediator of fibrosis and deposition of extracellular matrix and the correlation of SPARC serum levels to TED status and FT3 make it a promising biomarker of active TED.

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.

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.

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.

RIG-I is a cytosolic immune receptor that provides the first line of defense by detecting viral RNA and triggering antiviral responses. Its physiological role in humans remains unclear, as no patients with complete RIG-I deficiency have yet been reported. We identified a critically ill COVID-19 patient with severe RIG-I deficiency caused by heterozygous RIG-I G731R, a novel dominant loss-of-function variant. The G731R mutation in helicase motif VI disrupts the arginine finger, impairing the ATPase activity of RIG-I, but not its RNA-binding ability. However, viral RNA binding fails to expose the signaling domains, thereby impairing the IFN-{beta} response of G731R. Instead, G731R competes with wild-type RIG-I, exerting a dominant negative effect. The loss-of-function is caused by bulky-charged substitutions at G731, as alanine or leucine substitution results in an unexpected gain-of-function phenotype. These findings highlight the importance of uncompromised RIG-I function for human antiviral immunity and the pleiotropic effects of single mutations.

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.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

ObjectiveThe study aimed to analyze factors associated with cotinine levels as an early risk indicator for chronic diseases and students readiness to quit smoking in Praya Barat District. MethodsThis study used a cross-sectional design involving 563 high school students in Praya Barat District. Data analysis was performed using the Chi-square test and multiple logistic regression to identify determinants of high cotinine levels. ResultsA total of 67% of subjects had high cotinine levels, indicating high levels of nicotine exposure among students. The results of the analysis showed that the main determinants of high cotinine levels were cigarette consumption of [&ge;]5 cigarettes/day (AOR=2.426; 95% CI=1.534-3.838; p<0.001), male gender (AOR=2.100; 95% CI=1.358-3.250; p=0.001), family members who smoke (AOR=2.149; 95% CI=1.359-3.399; p=0.001), rarely of exercise (AOR=2.155; 95% CI=1.350-3.440; p=0.001), and personal history of chronic disease (AOR=2.646; 95% CI=1.653-4.234; p<0.001). Meanwhile, willingness to participate in a smoking cessation program did not show a significant relationship (p=0.093). ConclusionsMost students showed high cotinine levels, indicating significant exposure to nicotine and a potential risk of chronic disease in the future. The most influential factors were active smoking behavior, a family environment of smokers, and low levels of physical activity.

ObjectiveTo introduce and evaluate the clinical utility of the "adipo-B index" as a novel metric of the adipose tissue-pancreatic beta cell axis. To our knowledge, no prior clinical metric has integrated adipose tissue insulin resistance and pancreatic beta-cell function into a single index applicable across therapeutic classes. MethodsTreatment-naive subjects with T2DM received monotherapy with modified traditional diet for diabetes (MJDD, n=61), canagliflozin (n=67), pioglitazone (n=54), or sitagliptin (n=63). Correlations between the baseline and changes in adipo-IR or adipo-B and clinical parameters were analyzed. This is a prospective, non-randomized observational study. ResultsAt baseline, among all the subjects, adipo-B significantly correlated with FBG, HbA1c, non-HDL-C and BMI, while adipo-IR did not. At 3 months, across all therapeutic strategies, significant negative correlations were observed between the changes in ({Delta})adipo-B and baseline adipo-B. By contrast, in MJDD, canagliflozin and pioglitazone, significant negative correlations were seen between {Delta}adipo-IR and baseline adipo-IR, while with sitagliptin, no correlations were noted. {Delta}adipo-B, but not {Delta}adipo-IR, correlated with the improvements of glycemic (FBG, HbA1c) and lipid (non-HDL-C) parameters across all these therapies. While significant correlations were seen between {Delta}adipo-B and {Delta}adipo-IR with MJDD, pioglitazone and sitagliptin, canagliflozin uniquely "decoupled" this axis. With sitagliptin and pioglitazone, adipo-B improved despite weight gain. ConclusionThe adipo-B index is a superior indicator of systemic metabolic status and therapeutic response and could serve as a useful tool for precision therapy for diabetes.

Identification of early interventions to reduce/eliminate asthma - the most common chronic disease among children - could significantly reduce burden on the healthcare system. Large-scale asthma Exposome-Wide Association Studies (ExWAS) could identify potential interventions, however integration of diverse data is required to address association confounders. The CHILD Cohort Study has followed 3,454 healthy Canadian children and their families from early pregnancy, collecting exceptionally diverse data including 24,852 variables from participant questionnaires, clinical data, household and neighbourhood-level exposures, and sample-derived chemical analytic/omic datasets. Here, we report integration of these datasets into the CHILDdb database platform, and use these data to perform ExWAS and machine learning analyses, identifying and further characterizing associations between childhood asthma and 2,954 diverse early exposures (pregnancy to age 5). Significant asthma associations include antibiotic use, human milk components, DEHP phthalate, and mothers prenatal cleaning product/disinfectant exposure. Subsequent analysis revealed epigenetic changes in the cord blood at birth, after prenatal cleaner exposure, and different microbiome and/or inflammatory cytokine changes associated with different asthma-associated exposures in the child. Collective results support asthma as a heterogeneous condition involving multiple etiologies, with associated endotypes, including prenatal exposures with potential transgenerational effects, and suggest targets for early interventions.

AimsGestational diabetes mellitus (GDM) is the most common pregnancy-related medical complication. GDM is linked to aberrant immune responses in both mothers and offsprings, specifically, the subsequent development of inflammatory diseases. Whereas prior research has focused on specific immune cell subsets, a comprehensive overview of the impacts of GDM on maternal and fetal immune landscape is lacking. Here, we aim to comprehensively decipher how GDM modulates various immune cell populations in mothers and offsprings. MethodsA prospective, longitudinal case-control study was carried out. Maternal blood from GDM-affected (GDM, n=18) and non-GDM-affected (Ctrl, n=21) mothers were collected at ante-(36-38 weeks of gestation) and post-partum (6-8 weeks post-partum) timepoints. Cord blood from GDM (n=7) and Ctrl (n=11) pregnancies were collected upon C-section. They were analyzed with the state-of-the-art cytometry by time of flight (CyTOF) with a 40-marker panel. Additionally, a publicly available RNA-seq dataset for cord blood mononuclear cells was re-analyzed to confirm results from CyTOF experiments. ResultsCompared to Ctrl, GDM was associated with more activated maternal T cell subsets ante-partum, including increased CD45RO+ and Ki67+ CD4+ T cell populations, which reverted post-partum. GDM-affected maternal innate lymphoid cell (ILC) also exhibited increased granzyme B production ante-partum. On the other hand, in GDM-impacted cord blood, fetal T and B cells were more activated, displaying less naive and more effector phenotypes, further supported by RNA-seq analyses. ConclusionsOur comprehensive analyses revealed that GDM is linked to profound changes in the immune landscapes of the mothers (ante-/post-partum) and foetuses (at birth), casting novel insights towards GDM pathophysiology. Longitudinal immune profiling might be warranted for early detection and stratification of risk, and development of targeted interventions to prevent inflammatory disorders in GDM mothers and their offspring. Research in contextO_LIWhat is already known about this subject? O_LIThe maternal and intrauterine environments are important contributors to long-term health outcomes of mothers and offsprings. C_LIO_LISome maternal and fetal immunity changes have been observed in gestational diabetes mellitus (GDM)-affected pregnancies. C_LIO_LIGDM is associated with increased risk of later-life metabolic and inflammatory diseases in mothers as well as offsprings. C_LI C_LIO_LIWhat is the key question? O_LIWhat are the longitudinal alterations in maternal and fetal immune landscapes in GDM-affected pregnancies? C_LI C_LIO_LIWhat are the new findings? O_LIHigh-dimensional immune profiling provided the most comprehensive overview of alterations in maternal and fetal immune landscapes associated with GDM. C_LIO_LIGDM is associated with skewing of maternal CD4+ T cell and ILC towards activated phenotypes ante-partum. C_LIO_LIGDM is linked to more activated fetal T and B cell profiles. C_LI C_LIO_LIHow might this impact on clinical practice in the foreseeable future? O_LIUnderstanding the complex alterations in the maternal and fetal immune landscape in GDM-affected pregnancy provides insights into the long-term impacts of GDM on the mother and offspring. C_LI C_LI

Vaccination frequently elicits suboptimal immunogenicity in organ transplant recipients, particularly those on long-term immunosuppressive therapy, highlighting the need for improved understanding of immunosuppression mechanisms and optimized vaccination strategies. This study enrolled a cohort of 132 individuals and observed significantly lower antibody levels in kidney transplant recipients (KTRs) compared to non-transplant controls (non-KTRs). Antibody levels were inversely associated with both the dosage and duration of immunosuppressive therapy. Complementary small animal studies demonstrated that immunosuppressive treatment dosage-dependently and reversibly impaired antibody production, primarily by depleting immune cells, notably B cells. A single shot of adenoviral vector-based vaccines demonstrated enhanced immunogenicity relative to two shots of alum-adjuvanted protein vaccines, inducing potent neutralizing antibodies (NAbs) and a Th1-biased T-cell response even under continuous immunosuppression. The enhanced response was driven by reduced interference from pre-existing antibodies, sustained transgene expression, and the reprogramming of lipid metabolism to activate T and B cells. Our findings advocate for tailored vaccination strategies, positioning adenoviral vectors as a candidate modality for this vulnerable population.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks of X-rays and the subjectivity of physical examinations. Here, we present PointScol, a radiation-free triage system leveraging 3D back surface point clouds. To reconcile the conflicting clinical demands for "zero-miss" screening and "fine-grained" severity assessment, we developed a two-stage deep learning framework. First, an automated segmentation module extracts the dorsal region of interest (ROI) to standardize input geometry. Second, the system employs a dual-branch diagnostic strategy: a binary classification network designed for maximal sensitivity to rule out health, and a 5-class grading network designed to stratify severity (0-10{degrees}, 11-20{degrees}, 21-30{degrees}, 31-40{degrees}, >40{degrees}). Validation on a multi-center dataset (n=128) confirmed the distinct utility of this hierarchical approach. For the scoliosis screening task using a 10{degrees} Cobb angle threshold, the binary classification model achieved a sensitivity of 100.00% in the external cohort, ensuring that no cases requiring further clinical attention were missed. While the 5-class grading task inherently faces greater complexity, it successfully achieved an overall accuracy of 84.48% and, crucially, demonstrated a high specificity of 98.42% for severe surgical cases (>40{degrees}). This performance profile establishes PointScol as a safe clinical filter: the binary module reliably excludes healthy individuals, while the 5-class module flags high-risk patients for prioritized intervention, collectively offering a non-invasive, resource-efficient paradigm for scoliosis management.

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.

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.