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Reconstructing IDF curves from daily rainfall records in data-scarce regions: A statistical method based on temporal disaggregation and gumbel modeling

Urban regions in the tropics often face challenges in hydrological design due to the lack of high-resolution rainfall data. This study presents a method for generating intensity, duration, frequency (IDF) curves using only daily rainfall records, applied to the case of Habana del Este, Cuba. Daily maximum rainfall data from eight pluviometric stations (2010–2024) were combined with subdaily observations from a reference pluviograph. Two strategies were used: direct integration for compatible stations and temporal disaggregation for incompatible ones. Rainfall intensities for return periods between 2 and 1000 years were estimated using Gumbel frequency analysis and fitted to the Sherman model through nonlinear regression. The resulting IDF curves were unified into a single regional model using a weighted average of rainfall intensities from the compatible and disaggregated station groups, followed by final Sherman model fitting. The main contribution of this study is an integrated reconstruction framework that links station compatibility screening, selective use of observed subdaily rainfall structure, disaggregation of non-compatible daily records, and weighted regional unification within a single reproducible workflow. The final curves showed excellent agreement across durations and return levels (R 2  > 0.998), with strong internal consistency between estimation methods. Validation using linear and log log plots confirmed the robustness of the approach. This method provides a practical and statistically sound solution for IDF curve development in data scarce tropical regions, offering direct support for infrastructure planning, hydraulic design, and climate resilience where subdaily rainfall observations are limited.

Genetic yield of next-generation sequencing for detecting monogenic familial hypercholesterolemia in uzbek patients with coronary artery disease

Background Familial hypercholesterolaemia (FH) is an inherited disorder with markedly elevated LDL-C and increased risk of premature atherosclerotic cardiovascular disease, most often caused by pathogenic variants in LDLR and less frequently APOB / PCSK9 (or recessive LDLRAP1 ). FH is commonly assessed using the Dutch Lipid Clinic Network (DLCN) score (definite >8, probable 6–8, possible 3–5). In Uzbekistan, genetic evidence for FH remains limited and largely based on candidate-variant studies, and the diagnostic yield of NGS for monogenic FH in CAD patients is not well defined. Aim For the first time in Uzbekistan and Central Asia, to investigate FH-associated monogenic variants using next-generation sequencing (NGS) and to assess the validity of the DLCN criteria against genetic testing as the diagnostic reference standard in Uzbek patients with CAD and suspected FH. Methods This study included 95 patients with coronary artery disease (CAD) who underwent targeted NGS of LDLR , APOB , PCSK9 , and LDLRAP1 . The suspected/phenotypic FH group comprised 56 patients: 53 with DLCN-predicted heterozygous FH (HeFH)—possible (3–5 points, n = 22), probable (6–8 points, n = 16), and definite (>8 points, n = 15)—and 3 siblings from one family with a homozygous FH (HoFH) phenotype. The control group included 39 CAD patients with hypercholesterolemia without an FH diagnosis (DLCN 1–2 points). Only pathogenic/likely pathogenic (P/LP) variants were used for genetic confirmation of FH. Results Pathogenic/likely pathogenic variants were detected in 10/53 (18.9%) DLCN-predicted HeFH patients and in all three HoFH siblings. Genetic confirmation rates (PPV) were 46.7% (7/15) in definite HeFH, 12.5% (2/16) in probable HeFH, and 4.5% (1/22) in possible HeFH; no P/LP variants were detected in controls (0/39). Using a DLCN >8 threshold, sensitivity was 70.0% (7/10) and specificity was 90.2% (74/82) in the CAD cohort excluding the HoFH family. Conclusion NGS confirmed the highest diagnostic yield in patients with DLCN >8, supporting its use as a practical threshold to prioritise genetic testing; however, monogenic FH may still be present in patients with probable or possible DLCN scores.

A multi-objective portfolio optimization model incorporating sentiment analysis of quarterly reports and LSTM-based price prediction

Sentiment analysis (SA) of natural language text has become as a powerful instrument for enhancing financial market predictions. Quarterly reports from companies, in particular, offer a rich source of data for sentiment analysis, providing key insights into a company’s performance, strategic actions, and future prospects. These reports can significantly influence investor decisions regarding asset investments. Notwithstanding the potential, prior research has not investigated sentiment analysis concerning these resources in portfolio optimization. To fill this void, we propose an innovative three-stage approach to constructing stock portfolios. In the first stage, we perform sentiment analysis on companies’ quarterly reports using the FinBERT model to assess the sentiment surrounding each company. In the second stage, we utilize a Long-Short-Term Memory (LSTM) model for forecasting future prices, which enables the calculation of expected returns and the covariance matrix. In final stage, we present a three-objective portfolio optimization model that incorporates risk, return, and sentiment-derived trend features. We solve this model using the Weighted Goal Programming (WGP) method. Our results indicate that the proposed model effectively supports portfolio optimization. Moreover, the model is implemented using data from companies that are part of the Dow Jones Industrial Average (DJIA), and findings demonstrate high accuracy, confirming the practical potential of the proposed approach.

An integrated multi-omics study of key mediators and therapeutic targets for doxorubicin-induced atrial fibrillation

Background Doxorubicin (DOX), a widely used chemotherapeutic agent for cancer patients, is associated with a significant risk of inducing atrial fibrillation (AF), a serious cardiac complication that impairs patient prognosis. However, the specific molecular and cellular mechanisms linking DOX cardiotoxicity to AF pathogenesis remain poorly understood. Methods Following processing pharmacovigilance analysis of DOX-related AF events, we employed an integrative multi-omics strategy. Differentially expressed genes (DEGs) were first identified from the atrial transcriptomic dataset. Network toxicology was used to predict DOX targets, which were intersected with AF-related genes and DEGs to identify candidate targets. Functional analyses and protein-protein interaction network analysis was applied to pinpoint hub genes. Their predictive performance was validated in independent datasets. Gene set enrichment analysis (GSEA) and immune infiltration profiling (CIBERSORT) were conducted to elucidate biological functions and immune context. Molecular docking simulations validated direct interactions between DOX and selected proteins. Finally, single-cell RNA sequencing (scRNA-seq) analysis resolved the cell-type-specific expression patterns of key targets. Results Functional analyses implicated the candidate genes in critical pathways. 5 hub genes were further selected from candidate genes using the MCC algorithm. Among 5 hub genes, we identified and validated the combination of CCR2 , PDE5A , and CXCR2 showed high predictive accuracy for AF (mean AUC = 0.87), with identifying and validating CCR2 and PDE5A significantly and differentially expressed. GSEA linked CCR2 and PDE5A showed different pathways. Immune infiltration analysis revealed significant alterations in macrophages, monocytes, and T cell subsets in AF tissues. Molecular docking confirmed stable, high-affinity binding between DOX and both CCR2 and PDE5A (binding energy < −7 kcal/mol). Crucially, scRNA-seq analysis demonstrated that CCR2 and PDE5A were differentially expressed in atrial macrophages and fibroblasts respectively. Conclusion This study suggests that CCR2 and PDE5A may serve as central mediators and potential therapeutic targets for DOX-induced AF, though these findings require experimental validation.

Effect of sex hormones, garlic and fennel extracts in layers’ breeders diet on inherited offspring sex

This study aimed to examine the effect of dietary supplementation with sex hormones, garlic, and fennel extracts in layer breeders on the molecular sex ratio of their progeny. One hundred layer breeders, aged sixty-five weeks, were assigned to five treatments with five replicates of four hens each in a completely randomized design (CRD) for five weeks. The experimental treatments consisted of: (1) a control diet (corn and soybean meal-based), (2) control diet + testosterone (1 mg/kg), (3) control diet + progesterone (1 mg/kg), (4) control diet + fennel extract (400 mg/kg), and (5) control diet + garlic extract (400 mg/kg). In the third and fifth weeks of the experiment, blood samples were collected from the wing vein of layer breeders to measure sex hormone levels. At the end of the fifth week, eggs were gathered over two consecutive days and incubated at 37.5°C. The results suggest that fennel extract increased serum testosterone levels compared to the control throughout the study period ( P  = 0.057). Garlic and fennel extracts and progesterone increased the female sex ratio, while testosterone treatment increased the male sex ratio compared to the control ( P  = 0.056), although these differences were not statistically significant ( P  > 0.05). The experimental treatments significantly influenced the percentage of embryos produced ( P  < 0.05). No significant effect was observed on blood glucose levels ( P  = 0.076), though numerical differences were noted. Treatment 2 (testosterone) resulted in the lowest female sex ratio and blood glucose levels, whereas treatments 3 (progesterone), 4 (fennel extract), and 5 (garlic extract) yielded the highest female sex ratios and blood glucose levels. These findings suggest that the treatments’ effects may be linked to their impact on blood glucose levels. The results indicate that dietary interventions affect the offspring sex ratio.

Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study

Background Patients with type 2 diabetes mellitus (T2DM) prone to acute diabetic complications are at high risk for emergency department (ED) visits, which often precede hospitalization and mortality. Identifying these high-risk phenotypes before deterioration is critical for preventative care. We developed machine learning (ML) models using large-scale, real-world electronic medical records, including prescription data, to predict the possibility of ED visits in patients with T2DM and support proactive interventions in primary care settings. Methods We analyzed the electronic health record data of five independent institutions, creating a comprehensive dataset of 220,720 patients. The data included dynamic clinical parameters such as vital signs, laboratory results, and prescription histories. The cohort was randomly split into a training set ( n  = 176,576) and a test set ( n  = 44,144). The primary outcome was the first ED visit. We developed multiple ML models using an automated ML framework and optimized them using hyperparameter tuning of the training set. Model performances were evaluated using the area under the receiver operating characteristic (AUROC) curve, and feature importance was analyzed using SHAP values to ensure interpretability. Results Among the screened population, 49,770 (22.6%) experienced at least one ED visit, distributed proportionally across the training and test datasets. The CatBoost model demonstrated superior predictive performance, achieving an AUROC of 0.87 (95% CI, 0.862–0.871) on the test dataset. The model identified modifiable risk factors as key predictors; Diastolic blood pressure was the most significant variable, followed by serum creatinine and systolic blood pressure. Conclusions This ML-based predictive model can accurately identify high-risk patients with T2DM who are likely to visit the ED based on readily available clinical variables. By enabling healthcare providers to shift from reactive treatment to proactive risk management, it has the potential to reduce the burden of ED visits due to acute complications in T2DM.

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome

Background Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). While dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis and impaired glucocorticoid-responsive immune (GRI) signaling are proposed links between these disorders, the precise molecular mechanisms remain poorly understood. Methods We performed an integrative transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) from ASD and IBS cohorts. Our approach combined single-sample Gene Set Enrichment Analysis (ssGSEA), differential expression profiling, weighted gene co-expression network analysis (WGCNA), and machine-learning-based feature selection. We utilized single-cell RNA sequencing to resolve cellular sources, while transcription factor, miRNA, and Connectivity Map (CMap) analyses identified regulatory mechanisms and potential drug candidates for reversing GRI-associated signatures. Results GRI-associated transcriptional activity was markedly elevated in the ASD group and moderately upregulated in the IBS group. Network and enrichment analyses revealed a convergence of immune recognition and cytokine signaling pathways. We identified four core genes—LRFN1 , NUAK2 , TMEM154 , and GAPT—that consistently discriminated disease status. These genes were primarily expressed in monocytes, natural killer (NK) cells, and B cells. Regulatory analysis implicated stress-responsive transcriptional control and extensive miRNA modulation in these processes. CMap analysis identified RN-486, saracatinib, and batimastat as compounds predicted to restore GRI homeostasis. Conclusions These findings define a shared GRI-associated molecular signature linking systemic stress adaptation to immune dysregulation along the brain–gut axis. This study provides novel mechanistic insights and identifies potential transcriptomic biomarkers and therapeutic targets addressing the shared molecular architecture between ASD and IBS.

Computational approaches and the future of urban crime research

Cloacal microbiome variation in wild and captive Eastern Indigo Snakes (Drymarchon couperi) with and without Cryptosporidium serpentis infection

The Eastern Indigo Snake (EIS; Drymarchon couperi ), a federally threatened species native to the southeastern United States, serves as a valuable model for examining the effects of captivity and infection on gastrointestinal microbial composition in reptiles. As an alternative to direct gut sampling, we examined the cloacal microbiomes of EISs to evaluate changes in microbial community structure across our study groups. This study assessed the cloacal microbiome of wild and captive EISs using shotgun metagenomic sequencing. Samples were divided into three groups for comparative microbiome analysis: captive snakes positive for Cryptosporidium serpentis ( C. serpentis ), captive snakes negative for C. serpentis , and wild snakes. Alpha (Shannon index, paired Wilcoxon test) and beta diversity (Bray-Curtis dissimilarity, PERMANOVA, CAP) metrics were used to assess microbial diversity and community composition across groups. Furthermore, a linear discriminant analysis effect size (LEfSe) was used to identify microbial taxa significantly enriched in C. serpentis -positive versus C. serpentis -negative captive snakes. Bacterial, fungal, bacteriophage, nematode, and protozoan taxa were significantly enriched in C. serpentis -positive snakes compared with C. serpentis -negative captive snakes, based on a linear discriminant analysis (LDA) score ≥ 2.5 and p  ≤ 0.05. Total taxa species Shannon diversity was consistent between C. serpentis -positive and negative captive snakes (p = 0.55) while wild snake samples were significantly more diverse (p = 0.026). Wild snakes also exhibited a significantly increased Shannon diversity of fungi (p = 0.044), protozoa (p = 0.012), and nematodes (p = 0.008) compared to their captive counterparts. This study offers the first in-depth characterization of the cloacal microbiome in reptiles, specifically in EISs, using shotgun metagenomic sequencing. The findings establish a foundation for exploring microbiota–host interactions with implications for reptile health, disease ecology, and conservation management.

Evaluation of tri-plate rapid on-farm culture system to make therapeutic decisions for mastitis cases in dairy cattle

The empirical use of antibiotics for clinical mastitis is a principal driver of antimicrobial resistance in dairy farming. While on-farm culture systems represent a promising strategy for targeted therapy, robust evidence of their efficacy in heterogeneous commercial settings is still needed. We conducted a randomized controlled trial across 16 commercial dairy farms. Cows with clinical mastitis (CM) were allocated to a Positive Control Treatment (PCT) group (n = 57), receiving immediate empirical intramammary (IMM) antibiotics, or a Culture Based Group (CBG) (n = 46), where treatment was directed by a tri-plate on-farm culture system after 24-hour incubation. The cows were considered experimental unit with mixed-effect models within cluster correlation. Data was statistically analyzed using chi square tests, paired t-tests and Kaplan Meier survival analysis via SPSS (version 20.0). The culture-guided protocol enabled a reduction in antibiotic use, eliminating treatment for the 45.6% of CBG cases (no bacterial growth or Gram-negative infections). The clinical cure rates between the groups (CBG 82.6% vs. PCT 75.4%) were not statistically significant (p = 0.28). Similarly, bacteriological cure rates were comparable between (PCT 71.9% vs. CBG 71.7%, p = 0.987). However, the CBG approach revealed significantly lower treatment failure rate (17.3% vs. 24.5%, p < 0.001) with a shorter median time to clinical cure (3 days vs. 7 days, p < 0.001). At the herd level, the strategy was associated with a significant increase in milk yield (+6.94 L/day, p < 0.001) and reduction in somatic cell count (−56.8%, p < 0.001). The tri-plate on-farm culture system is an effective antimicrobial stewardship tool, facilitating a substantial reduction in antibiotic use while accelerating clinical recovery and improving udder health in commercial dairy operations.

Predicting the finished fabric width and areal density (Grams per Square Meter) of commercially produced plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)

The purpose of this research is to predict Finished Fabric Width (FW) & Areal Density (GSM) of 100% cotton plain single jersey knitted fabric by building a fuzzy inference model incorporating key input parameters such as Stitch Length (SL), Yarn Fineness or Count (YC) and Machine Diameter (D). More than 30,000 mass production-grade data points have been used to generate the model with remarkable precision. Once the model was prepared, it was verified using new experimental data. The Coefficient of Determination (R 2 ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) between the actual and the predicted FW were found to be 0.979, 1.214%, 1.103, respectively. For GSM the corresponding metrics were 0.940, 1.661%, 3.892, respectively. Both prediction outcomes showed excellent precision, justifying the model's applicability in the textile industry for predicting two important knit fabric parameters namely FW and GSM. The system's reliability was ensured by using a large set of industry standard data. This, combined with the adaptation of carefully designed fuzzy logic rules based on proven scientific method, significantly contributed to producing more accurate results. Together, all these aspects make the system stand out from similar studies, offering a practical and trustworthy approach for real world textile application with enhanced process optimization.

Sodium Bicarbonate versus N-Acetylcysteine plus hydration versus hydration alone for preventing contrast-associated acute kidney injury: A single-center retrospective analysis with propensity score matching

Background Contrast-associated acute kidney injury (CA-AKI, historically termed contrast-induced nephropathy, CIN) is a leading cause of iatrogenic acute kidney injury (AKI) following iodinated contrast administration; yet the optimal preventive strategy remains controversial, especially in mild-to-moderate-risk patients. Methods This single-center retrospective observational study was conducted in strict accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We consecutively screened adult patients undergoing contrast-enhanced computed tomography (CT) or angiography at a tertiary hospital in China between June 2022 and January 2025. After propensity score matching (PSM), 240 adult patients were included in the final analysis, allocated 1:1:1 into three groups: conventional hydration alone (control), hydration plus N-acetylcysteine (NAC), and hydration plus sodium bicarbonate. 1:1:1 nearest-neighbor PSM was performed to minimize confounding bias, with a caliper of 0.05. The primary endpoint was the incidence of CA-AKI within 72 hours after contrast exposure, defined per the 2024 European Society of Urogenital Radiology (ESUR) guidelines. Secondary endpoints included dynamic changes in renal function, renal replacement therapy (RRT) requirement, hospital stay duration, adverse events, and subgroup analyses by comorbidities and contrast modalities. Results After PSM, 240 patients (80 per group) were included in the final analysis, with well-balanced baseline characteristics across groups (all standardized mean differences <0.1, all P > 0.05). The overall incidence of CA-AKI was 15.00% in the control group, 2.50% in the NAC group, and 7.50% in the sodium bicarbonate group. Hydration plus NAC significantly reduced CA-AKI risk compared with hydration alone (RR = 0.17, 95% CI: 0.04–0.74, Bonferroni-adjusted P = 0.004). Sodium bicarbonate showed a numerically lower CA-AKI incidence than control, but the difference did not reach statistical significance after correction (RR = 0.50, 95% CI: 0.19–1.31, adjusted P = 0.121). Repeated-measures ANOVA revealed significant group, time, and group × time interaction effects on serum creatinine (Scr), blood urea nitrogen (BUN), and estimated glomerular filtration rate (eGFR) (all P < 0.001), with the mildest renal function fluctuations in the NAC group. The renoprotective efficacy of NAC was consistent across contrast-enhanced CT and angiography modalities. Advanced age, comorbid diabetes, comorbid hypertension, higher baseline Scr, and lower baseline eGFR were independent risk factors for CA-AKI [5,6,28] (all P < 0.05). No patients required RRT in any group, with no significant difference in hospital stay duration or mild adverse event incidence across groups (all P > 0.05). Conclusions For patients with eGFR ≥ 30 mL·min ⁻ ¹·(1.73 m² ⁻ ¹), hydration combined with high-dose intravenous NAC significantly reduces the short-term incidence of CA-AKI compared with hydration alone, with a favorable safety profile and consistent efficacy across contrast modalities. Hydration plus sodium bicarbonate is a safe alternative for patients intolerant to NAC. These findings are hypothesis-generating and require verification in large-sample, multicenter prospective trials.

Interactions between self-help and hospice and palliative care – Opportunities, barriers and needs (Self-Pall): A study protocol

Introduction A progressive and life-limiting disease can cause enormous psychological distress for patients and family caregivers. Health-related collective self-help could be a coping strategy by facilitating interaction with others in similar situations. Scarce literature is available on how those patients and their caregivers could benefit from self-help activities, how self-help groups deal with death and grief, and whether there are interactions between self-help and hospice and palliative care. The Self-Pall project aims at developing recommendations tailored at different actor groups to support the interactions between self-help and hospice and palliative care. Methods We will use a qualitative, multi-method, sequential research design. The project started in 07/2025 and will end in 06/2027. Semi-structured interviews will be conducted with patients, family caregivers, and representatives of hospice and palliative care and of self-help to explore personal and professional experiences focussed on opportunities, barriers, and needs. A draft of recommendations will be derived, which will then be confirmed and expanded upon within focus groups. A representative expert panel will refine and agree upon the recommendations through an iterative, multi-level Delphi process. The engagement of a Patient and Public Involvement group will ensure the relevance of our research to the public and provide transparency. Discussion We will explore awareness, needs, and factors that promote or hinder self-help activities from the perspective of patients, caregivers, and professionals. From a self-help perspective, we will assess how to deal with dying, death, and grief, as well as knowledge and use of hospice and palliative care services, any barriers and how to overcome them. We are the first to explore interactions between self-help and hospice and palliative care bilaterally to develop practical recommendations and key principles with significant implications for practice.

Amyloid-bodies in the evolution of malignancies

Tumorigenesis depends on the capacity for cancer cells to survive in the presence of various environmental stressors. An emerging paradigm in the study of cancer cell stress responses is their regulation by membrane-less intracellular compartments known as biomolecular condensates. While there has been considerable progress in our understanding of biomolecular condensates in cancer in vitro , a paucity of evidence remains for their presence and function in settings which more closely reflect in vivo tumor physiology. In this study, we use human tissues and an in vivo orthotopic mouse model to study the role of the Amyloid-body, a stress-induced condensate, in tumorigenesis. We present methodology to visualize Amyloid-bodies in tumors by multiple immunohistochemical approaches in addition to a semi-automated analysis pipeline. Analysis of multiple tumor types reveals that Amyloid-bodies are detectable at all tumor grades and stages, to varying degrees, and negatively correlate with the cell proliferation marker Ki-67. Finally, in an orthotopic mouse model of breast cancer, we show that silencing long noncoding ribosomal intergenic spacer RNA (rIGSRNA) involved in Amyloid-body formation accelerates tumorigenesis in vivo . Together, these results suggest that Amyloid-body formation occurs in human cancers, further establishing the physiological relevance of biomolecular condensates.

Air-permeable hydrogels through viscoelastic phase separation of aerogels

Contextual image caption creation using object positional embedding and generative models

Automated image captioning remains a challenge, as it enables machines to generate context-aware textual descriptions of visual content. Traditional deep learning approaches often rely on lexical overlap and fail to capture semantic relationships among objects, leading to captions that lack contextual richness. This study proposes an encoder–decoder framework that integrates YOLOv5 with a generative transformer to generate descriptive image captions. The proposed model was evaluated against two baselines: CNN-LSTM (M1) and a BERT-based transformer model (M2). M1 achieves BLEU-1 (0.45) and ROUGE-L (0.42) but demonstrates limited semantic understanding with METEOR (0.18) and SPICE (0.07). M2 improves with higher METEOR (0.24) and CIDEr (0.62), although its BLEU scores remain low. The proposed model achieves the highest CIDEr (1.10) and SPICE (0.25), reflecting superior semantic understanding and better capture of object relationships. Despite a lower BLEU (0.40), it significantly outperforms traditional methods in caption quality. To further validate these results, we conducted an expert-based evaluation to assess semantic accuracy, visual grounding, and caption usefulness. The proposed model achieved 93% accuracy in expert evaluations across 500 images, indicating strong contextual alignment with human interpretation. Additionally, we employed exploratory data analysis to examine and visualize the text captions, aiming to gain a deeper understanding of the optimal caption.

Serological evidence of SARS-CoV-2 exposure in marine mammals in the United States between 2020 and 2025

Natural infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been documented in over 60 animal species, some distantly related. Several marine mammals have been predicted as highly susceptible to SARS-CoV-2 infection based on the homology of their ACE2 receptors to those of humans. To assess potential exposure of marine mammals to SARS-CoV-2, we conducted an opportunistic survey from 2020 to 2025 and tested 1,808 swabs and 378 serum samples from 21 marine mammal species in the United States. All the swabs tested by RT-qPCR were negative, indicating the absence of active infection. A low level of SARS-CoV-2 neutralizing antibodies was detected in three pinniped species, including Phoca vitulina (harbor seal, 13.6%, 95% CI: 5.2–27.4%), Zalophus californianus (California sea lion, 7%, 95% CI: 1.9–17.0%), and Halichoerus grypus (grey seal, 3.7%, 95% CI: 0.5–12.7%). These findings represent the first serological evidence of SARS-CoV-2 exposure in marine mammals in the United States, highlighting the need for continued monitoring of these populations and further research on SARS-CoV-2 transmission dynamics in wildlife.

Modelling and simulation of smart city drainage system based on digital twin five-dimensional models

The construction of smart cities has entered a new stage of industrial development in light of the industrial revolution. The aim is to create digital twin cities that integrate the dual systems of physical and digital bodies. The drainage system of a city forms the foundation and core of its digital twin, hence the need for a construction and simulation study proposed in this research. The study is based on the five-dimensional model of the digital twin. First, the research aims to develop a five-dimensional digital twin model that incorporates twin data and connections, building upon a three-dimensional model to improve its applicability. Second, the research looks to create a digital twin drainage system model using a lightweight framework, employing a dynamic scheduling algorithm and model-view-controller to facilitate intelligent scheduling of the drainage system and enable real-time data collection and transmission. The experimental outcomes indicate that in normal conditions, the overflow loss of the mathematical twin drainage system was 30m 3 /s, 34m 3 /s, and 25m 3 /s under the conventional fixed-priority scheduling algorithm and dynamic scheduling digital twin drainage system algorithm, respectively. Due to the ratio of the improved overflow loss to the pre improved overflow loss being called the improvement ratio, the improvement ratios generated by the mathematical twin drainage system in different situations were 48.67%, 48.1%, and 48.57%, respectively, which significantly enhanced the performance and durability of the urban drainage system. The model effectively transforms and enhances the current urban drainage system by increasing the efficiency of scheduling pumping station clusters. It also offers valuable reference for the implementation of digital twin concept and technology.

Mycobacterial lipoarabinomannan negatively interferes with macrophage responses to Aspergillus fumigatus in-vitro

Over 1 million people have chronic pulmonary aspergillosis (CPA) secondary to pulmonary tuberculosis. Additionally, Aspergillus fumigatus ( Af ) has been reported as one of the most common pathogens associated with mycobacteria in patients with cystic fibrosis. Mycobacterial virulence factors, like lipoarabinomannan, have been shown to interfere with host’s intracellular pathways required for an effective immune response, however, the immunological basis for mycobacterial-fungal coinfection is still unknown. We therefore investigated the effect of lipoarabinomannan on macrophage responses against Af . Bone marrow-derived macrophages (BMDMs) were stimulated with non-mannose-capped lipoarabinomannan (LAM) from Mycobacterium smegmatis or mannose-capped lipoarabinomannan (ManLAM) from Mycobacterium tuberculosis for 2 hours and then infected with swollen Af conidia. Cell death was assessed by lactate dehydrogenase release. Cytokine release was measured in supernatant using Enzyme Linked Immuno-Sorbent Assay (ELISA). Colony forming units counting and time-lapse fluorescence microscopy was performed for studying conidia killing by macrophages. BMDMs stimulated with LAM showed increased cell death and inflammatory cytokine release in a dose-dependent manner, characterised by a significant increase of IL-1β release. Time-lapse fluorescence microscopy and CFUs revealed that both LAM and ManLAM significantly decrease the capacity of macrophages to kill Af conidia within the first 6 hours of infection. The mycobacterial virulence factor, lipoarabinomannan, disrupts macrophage capacity to efficiently clear Af at early stages of infection in-vitro .

A practical inflammatory blood-cell marker for cardiovascular risk stratification in psoriasis: Development of the Platelet-Leukocyte Adjusted Cardiovascular (PLAC) score

Background Psoriasis is an inflammatory disease associated with atherosclerotic cardiovascular disease (ASCVD). Although blood-cell markers predict ASCVD in the general population, the utility of these markers in cardiovascular risk stratification in psoriasis remains unclear given heightened inflammatory burdens among these patients. Objectives We aimed to develop a composite ASCVD risk score for psoriasis and evaluate its performance by integrating a novel inflammatory blood-cell marker with traditional cardiovascular risk factors. Methods We conducted a retrospective cohort study using All of Us (enrollment:May 2018-October 2023). ASCVD included acute coronary syndrome, cerebrovascular accident, or coronary artery disease. Independent predictors of ASCVD in Cox regression informed the Platelet-Leukocyte Adjusted Cardiovascular (PLAC) score, incorporating the Neutrophil-to-Platelet-to-Monocyte Ratio (NuPMoR=neutrophils/[platelets x monocytes]), age ≥ 65, male sex, hypertension, and diabetes. Results Among 1,572 psoriasis patients (median follow-up 7.2 years), the PLAC score (AUC 0.69, 95% CI 0.65–0.74), which incorporates NuPMoR, stratified patients into low-, medium-, and high-risk groups with corresponding 10-year ASCVD incidences of 4.9%, 11.8%, and 39.9%. Compared with the low-risk group, medium- (HR 2.27, 95% CI 1.53–3.39) and high-risk (HR 6.40, 95% CI 3.97–10.33) groups had significantly higher ASCVD hazard. The PLAC score demonstrated similar or numerically higher discrimination than the Framingham and PCE models in limited samples. Conclusions The PLAC score is a practical, psoriasis-specific ASCVD risk tool that integrates a novel inflammatory marker with traditional risk factors. It enables clinically meaningful ASCVD risk stratification using routine laboratory values in a high-risk population and may help identify psoriasis patients warranting closer cardiovascular monitoring.