Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions used during incubation. Our approach enabled identification with single gene-level sensitivity using two Escherichia coli variants, differing only in green fluorescent protein (GFP) expression, across diverse media and substrate material conditions, despite the interrogation path traversing 3-4 mm thick background material. We integrated traditional density functional theory (DFT)-based material computation with machine learning analysis, achieving over 97.7% classification accuracy, surpassing the performance of standard measurements from opened plates by 10.8% higher mean accuracy and 0.76% less variance. We further demonstrated Raman mapping-based colony identification via Raman peaks characteristic to GFPmut3 chromophore structure generated by DFT. Our approach is robust to changes in algorithms or substrate materials and promises real-time, non-perturbative monitoring of bacterial growth, biofilm formation, and antimicrobial resistance development.
Hyeon Lee, Kassel Liam Hingee, Janice L. Scealy
et al.
Compositional data, also referred to as simplicial data, naturally arise in many scientific domains such as geochemistry, microbiology, and economics. In such domains, obtaining sensible lower-dimensional representations and modes of variation plays an important role. A typical approach to the problem is applying a log-ratio transformation followed by principal component analysis (PCA). However, this approach has several well-known weaknesses: it amplifies variation in minor variables; it can obscure important variation within major elements; it is not directly applicable to data sets containing zeros and zero imputation methods give highly variable results; it has limited ability to capture linear patterns present in compositional data. In this paper, we propose novel methods that produce nested sequences of simplices of decreasing dimensions analogous to backwards principal component analysis. These nested sequences offer both interpretable lower dimensional representations and linear modes of variation. In addition, our methods are applicable to data sets contain zeros without any modification. We demonstrate our methods on simulated data and on relative abundances of diatom species during the late Pliocene. Supplementary materials and R implementations for this article are available online.
Understanding the temporal dynamics of biological growth is critical across diverse fields such as microbiology, agriculture, and biodegradation research. Although vision-language models like Contrastive Language Image Pretraining (CLIP) have shown strong capabilities in joint visual-textual reasoning, their effectiveness in capturing temporal progression remains limited. To address this, we propose CLIPTime, a multimodal, multitask framework designed to predict both the developmental stage and the corresponding timestamp of fungal growth from image and text inputs. Built upon the CLIP architecture, our model learns joint visual-textual embeddings and enables time-aware inference without requiring explicit temporal input during testing. To facilitate training and evaluation, we introduce a synthetic fungal growth dataset annotated with aligned timestamps and categorical stage labels. CLIPTime jointly performs classification and regression, predicting discrete growth stages alongside continuous timestamps. We also propose custom evaluation metrics, including temporal accuracy and regression error, to assess the precision of time-aware predictions. Experimental results demonstrate that CLIPTime effectively models biological progression and produces interpretable, temporally grounded outputs, highlighting the potential of vision-language models in real-world biological monitoring applications.
Fatma Al‐zahraa A. Yehia, Galal Yahya, Eslam M. Elsayed
et al.
ABSTRACT Enterococcus species, natural inhabitants of the human gut, have become major causes of life‐threatening bloodstream infections (BSIs) and the third most frequent cause of hospital‐acquired bacteremia. The rise of high‐level gentamicin resistance (HLGR) in enterococcal isolates complicates treatment and revives bacteriophage therapy. This study isolated and identified forty E. faecalis clinical isolates, with 30% exhibiting HLGR. The HLGR5 isolate, resistant to fosfomycin, vancomycin, and linezolid, was used to isolate the vB_EfaS_SZ1 phage from effluent water. This phage specifically lysed 42% of HLGR isolates. vB_EfaS_SZ1 demonstrated beneficial traits, including thermal stability, acid–base tolerance, a short latent period, and a large burst size. The phage genome comprises a 40,942 bp linear double‐stranded DNA with 65 open reading frames (ORFs). The genome closely resembled Enterococcus phages, classifying it within the Efquatrovirus genus. Phage‐antibiotic synergy was assessed using checkerboard assays and time‐killing analyses, revealing enhanced bacteriolytic activity of ampicillin and fosfomycin, with significant reductions in minimum inhibitory concentration values. In a mouse bacteremia model, phage‐antibiotic combinations significantly reduced E. faecalis liver burden compared to monotherapies. Histopathological analysis confirmed therapeutic synergy, showing reduced inflammation and improved hepatocyte regeneration. These findings underscore the potential of phage vB_EfaS_SZ1 as an adjunct to antibiotic therapy for resistant enterococcal bacteremia.
Giuseppe Fabio Parisi, Maria Papale, Giulia Pecora
et al.
The introduction of CFTR modulators has dramatically shifted the clinical management of cystic fibrosis (CF) from a life-limiting pediatric condition to a chronic disease with broader health implications. This review explores the impact of these advancements on lung immunology and the emerging spectrum of health challenges. While these modulators have reduced traditional pulmonary complications by mitigating inflammation and infection, they also introduce new considerations for long-term health management. As patients experience longer lives, issues such as the increased risk of certain cancers and other systemic complications like CF-related diabetes and liver disease are gaining attention. Understanding the interplay between CFTR modulators, immune response, and the development of these conditions is essential for optimizing patient outcomes. This review highlights the importance of integrated care strategies that address both the respiratory improvements and emerging health risks associated with longer life expectancy in CF patients. By fostering a comprehensive approach, we aim to enhance the overall quality of life and address the complex needs of individuals navigating CF in the modern therapeutic landscape.
Deema O. Qasrawi, Adriano M. C. Pimenta, Evgeniy V. Petrotchenko
et al.
<b>Background:</b> Quantifying urinary catecholamines and metanephrines is essential for the clinical screening and diagnosis of neuroendocrine tumours. HPLC with electrochemical detection (HPLC-ECD) is commonly used for this type of analysis but requires extensive sample cleanup. Simple and rapid dilute-and-shoot LC–multiple-reaction monitoring (MRM)-MS assays have been developed for quantitating these analytes in urine but have not yet been validated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. <b>Methods:</b> A simple dilute-and-shoot sample preparation without derivatization was used. C18 RP-UPLC-MRM-MS and positive-ion ESI were used, usually with two transitions per analyte being monitored. Certified deuterated internal standards were used for each analyte. <b>Results:</b> This assay was validated according to the CLSI C62-A guidelines, including accuracy/trueness, imprecision, sensitivity, specificity, carryover, stability, and linearity. The final MRM-MS method was compared to the established HPLC-ECD clinical chemistry reference method. The run time was reduced from 25 min to 5 min. <b>Conclusions:</b> A simple, robust, rapid, and cost-effective LC-MRM-MS assay for measuring urinary catecholamines and metanephrines was developed and validated according to the CLSI guidelines. This validated method requires minimal sample manipulation before analysis and provides sensitivity, specificity, and improved precision. The implementation of this assay in clinical laboratories will facilitate early and accurate diagnosis.
Javier Ureña Santiago, Thomas Ströhle, Antonio Rodríguez-Sánchez
et al.
Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previous research suggests automating this task using computer vision and machine learning methods, primarily through instance segmentation or density estimation techniques. This study conducts a comparative analysis of vision transformers (ViTs) for weakly-supervised counting in microorganism enumeration, contrasting them with traditional architectures such as ResNet and investigating ViT-based models such as TransCrowd. We trained different versions of ViTs as the architectural backbone for feature extraction using four microbiology datasets to determine potential new approaches for total microorganism enumeration in images. Results indicate that while ResNets perform better overall, ViTs performance demonstrates competent results across all datasets, opening up promising lines of research in microorganism enumeration. This comparative study contributes to the field of microbial image analysis by presenting innovative approaches to the recurring challenge of microorganism enumeration and by highlighting the capabilities of ViTs in the task of regression counting.
Alexandre Chaussard, Anna Bonnet, Elisabeth Gassiat
et al.
When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or product types in retail businesses, offering valuable insights into entity relationships. Despite their significance, current count-data models do not leverage this structured information. In particular, the widely used Poisson log-normal (PLN) model, known for its ability to model interactions between entities from count data, lacks the possibility to incorporate such hierarchical tree structures, limiting its applicability in domains characterized by such complexities. To address this matter, we introduce the PLN-Tree model as an extension of the PLN model, specifically designed for modeling hierarchical count data. By integrating structured variational inference techniques, we propose an adapted training procedure and establish identifiability results, enhancing both theoretical foundations and practical interpretability. Experiments on synthetic datasets and human gut microbiome data highlight generative improvements when using PLN-Tree, demonstrating the practical interest of knowledge graphs like the taxonomy in microbiome modeling. Additionally, we present a proof-of-concept implication of the identifiability results by illustrating the practical benefits of using identifiable features for classification tasks, showcasing the versatility of the framework.
Carolien Bastiaanssen, Pilar Bobadilla Ugarte, Kijun Kim
et al.
Abstract Argonaute proteins are the central effectors of RNA-guided RNA silencing pathways in eukaryotes, playing crucial roles in gene repression and defense against viruses and transposons. Eukaryotic Argonautes are subdivided into two clades: AGOs generally facilitate miRNA- or siRNA-mediated silencing, while PIWIs generally facilitate piRNA-mediated silencing. It is currently unclear when and how Argonaute-based RNA silencing mechanisms arose and diverged during the emergence and early evolution of eukaryotes. Here, we show that in Asgard archaea, the closest prokaryotic relatives of eukaryotes, an evolutionary expansion of Argonaute proteins took place. In particular, a deep-branching PIWI protein (HrAgo1) encoded by the genome of the Lokiarchaeon ‘Candidatus Harpocratesius repetitus’ shares a common origin with eukaryotic PIWI proteins. Contrasting known prokaryotic Argonautes that use single-stranded DNA as guides and/or targets, HrAgo1 mediates RNA-guided RNA cleavage, and facilitates gene silencing when expressed in human cells and supplied with miRNA precursors. A cryo-EM structure of HrAgo1, combined with quantitative single-molecule experiments, reveals that the protein displays structural features and target-binding modes that are a mix of those of eukaryotic AGO and PIWI proteins. Thus, this deep-branching archaeal PIWI may have retained an ancestral molecular architecture that preceded the functional and mechanistic divergence of eukaryotic AGOs and PIWIs.
Abstract Background West Nile virus (WNV) is a rapidly spreading mosquito-borne virus accounted for neuroinvasive diseases. An insight into WNV-host factors interaction is necessary for development of therapeutic approaches against WNV infection. CD11b has key biological functions and been identified as a therapeutic target for several human diseases. The purpose of this study was to determine whether CD11b was implicated in WNV infection. Methods SH-SY5Y cells with and without MEK1/2 inhibitor U0126 or AKT inhibitor MK-2206 treatment were infected with WNV. CD11b mRNA levels were assessed by real-time PCR. WNV replication and expression of stress (ATF6 and CHOP), pro-inflammatory (TNF-α), and antiviral (IFN-α, IFN-β, and IFN-γ) factors were evaluated in WNV-infected SH-SY5Y cells with CD11b siRNA transfection. Cell viability was determined by MTS assay. Results CD11b mRNA expression was remarkably up-regulated by WNV in a time-dependent manner. U0126 but not MK-2206 treatment reduced the CD11b induction by WNV. CD11b knockdown significantly decreased WNV replication and protected the infected cells. CD11b knockdown markedly increased TNF-α, IFN-α, IFN-β, and IFN-γ mRNA expression induced by WNV. ATF6 mRNA expression was reduced upon CD11b knockdown following WNV infection. Conclusion These results demonstrate that CD11b is involved in maintaining WNV replication and modulating inflammatory as well as antiviral immune response, highlighting the potential of CD11b as a target for therapeutics for WNV infection.
Hyungseok Kim, Soobin Kim, Jeffrey A. Kimbrel
et al.
Multidimensional scaling (MDS) is a dimensionality reduction technique for microbial ecology data analysis that represents the multivariate structure while preserving pairwise distances between samples. While its improvement has enhanced the ability to reveal data patterns by sample groups, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination, $F$-informed MDS, which configures the data distribution based on the $F$-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using simulated compositional datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that $F$-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader compositional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.
Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat.
Toxoplasma gondii is a protozoan parasite that is widely distributed in the human population and is responsible for corresponding global morbidity. Specifically, T. gondii causes toxoplasmosis, leading to miscarriage, stillbirth, and neural disorders. This parasite attacks different human organs and glands, such as the thyroid gland, and causes various corresponding health issues. Recently, studies have established a link between T. gondii and autoimmune thyroid diseases (AITD), which contributes to preterm delivery, miscarriage, low birth weight, and death. Therefore, the aim of this study was to detect the prevalence of toxoplasmosis and its association with AITD among pregnant women. A total of 180 blood samples were collected from pregnant women and examined using an enzyme-linked immunosorbent assay (ELISA). The patients were within the age range of 15–50 years old, and lived in Duhok City, Iraq; samples and clinical information was collected from August 2021 to February 2022. The corresponding blood samples were tested for anti-Toxoplasma IgG antibody, Toxoplasma IgG avidity, FT3, FT4, and TSH hormones, and TPO, Tg, and TSHR antibodies. Overall, our results showed that out of 180 pregnant women, 110 (61.1%) were seropositive for anti-Toxoplasma IgG antibody; specifically, 25 (22.7%) and 85 (77.3%) had recent and past infections, respectively. Approximately 54.4% (98) of the pregnant women had thyroid disorders; further, 22 (12.2%), 13 (7.2%), and 8 (4.4%) women had TPO, Tg, and TSHR antibodies, respectively. A total of 43 (23.8%) patients screened positive for AITD. Out of the 110 Toxoplasma IgG–positive women, 35 (31.8%) had AITD. The older women, rural residents, restaurant food consumers, and women with cat contact had relatively high infection rates. Toxoplasma seropositive women had more elevated autoantibodies than seronegative ones. In conclusion, this study demonstrated a high rate of toxoplasmosis and a corresponding association with thyroid hormones changes and AITD in pregnant women in Duhok, Iraq. Further, it is necessary to reduce overall infection rates through effective health and educational programs. Therefore, it is essential to measure Toxoplasma antibodies, screen for thyroid hormones and autoantibodies, and encourage gynecologist visits to reduce the risks to mothers and fetuses.
Abstract Background HIV and malaria are serious public health concerns, particularly in Tanzania. HIV-infected individuals are more likely to get malaria and its complications. However, data on the interaction of the two diseases in Tanzania are limited. This cross-sectional study aimed to determine the prevalence of malaria infection and associated factors among HIV-infected adults attending HIV care and treatment clinic at Kitete regional referral hospital in Tabora region, Tanzania. Methodology The cross-sectional study was carried out between March and May 2022 at Kitete regional referral hospital in Tanzania. A total of 246 HIV-infected adults were selected by systematic random sampling. Malaria was diagnosed using both malaria rapid diagnostic test (mRDT) and malaria microscopy. Social demographic data were collected using a structured questionnaire, while clinical history and laboratory parameters were extracted from patients’ files. Data were analyzed using STATA version 15.1, and a p value < 0.05 was considered statistically significant. The study included consenting HIV+ adults and excluded pregnant women and recent antimalarial users. Results Twenty-six out of 242 participants 10.7% (95% CI 6.9–15.6%) tested positive for malaria using mRDT, while 20 out of 242 participants 8.3% (95% CI 4.9–13.1%) tested positive for malaria using blood smear for malaria microscopy. Independent factors associated with malaria infection were living in rural areas (aOR = 2.81, 95% CI = 1.06–7.45, p = 0.038), poor adherence to anti-retroviral therapy (aOR = 3.66, 95% CI = 1.04–12.7, p = 0.043), HIV viral load of ≥ 1000 copies/mL (aOR = 3.2, 95% CI = 1.00–10.5, p = 0.02, CD4 count ≤ 350 cells/µL (aOR = 2.8, 95% CI = 1.10–7.30, p = 0.03), while using mosquito nets (aOR = 0.27, 95% CI = 0.08–0.90, p = 0.033) and receiving health education (aOR = 0.26, 95% CI = 0.09–0.71, p = 0.009) were protective factors. Conclusions The study reveals a concerning prevalence of malaria infection among adult HIV patients at Kitete regional referral hospital. Risk factors identified include a high HIV viral load, low CD4 count, lack of health education, rural residence, and non-use of mosquito nets. The results emphasize the necessity for focused health education, enhanced access to preventive measures, and consistent adherence to ART to reduce the risk of malaria in this vulnerable population.
Selective single beam tweezers open tremendous perspectives in microfluidics and microbiology for the micromanipulation, assembly and mechanical properties testing of microparticles, cells and microorganisms. In optics, single beam optical tweezers rely on tightly focused laser beams, generating a three-dimensional (3D) trap at the focal point. In acoustics, 3D traps have so-far only been reported experimentally with specific wavefields called acoustical vortices. Indeed, many types of particles are expelled (not attracted to) the center of a focused beam. Yet the trapping capabilities of focused beams have so-far only been partially explored. In this paper, we explore numerically with an angular spectrum code the trapping capabilities of focused beams on a wide range of parameters (size over wavelength ratio and type of particles). We demonstrate (i) that 3D trapping of particles, droplets and microorganisms more compressible than the surrounding fluid is possible in and beyond Rayleigh regime (e.g. polydimethylsiloxane, olive oil, benzene, and lipid sphere) and (ii) that 2D trapping (without axial trap) of particles with positive contrast factor can be achieved by using the particles resonances.
Porcine epidemic diarrhoea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae. It causes acute watery diarrhoea and vomiting in piglets with high a mortality rate. Currently, the GII genotype, PEDV, possesses a high separation rate in wild strains and is usually reported in immunity failure cases, which indicates a need for a portable and sensitive detection method. Here, reverse transcription–recombinase aided amplification (RT-RAA) was combined with the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas12a system to establish a multiplexable, rapid and portable detection platform for PEDV. The CRISPR RNA (crRNA) against Spike (S) gene of GII PEDV specifically were added into the protocol. This system is suitable for different experimental conditions, including ultra-sensitive fluorescence, visual, UV light, or flow strip detection. Moreover, it exhibits high sensitivity and specificity and can detect at least 100 copies of the target gene in each reaction. The CRISPR/Cas12a detection platform requires less time and represents a rapid, reliable and practical tool for the rapid diagnosis of GII genotype PEDV.
Purpose: To investigate the clinical features and risk factors for methicillin-resistant Staphylococcus aureus (MRSA) pneumonia (MP) with secondary MRSA bloodstream infections (MRSA-BSI) (termed MP-BSI) compared with MP alone and to study the incidence of MP-BSI among patients with MP. Methods: This was a retrospective, single-center study with clinical data derived from previous medical records. The cases were divided into groups: MP alone and MP-BSI. The determination of independent risk factors for MP-BSI relied on logistic regression analysis. Additionally, the crude outcomes were compared. Results: A total of 435 patients with MP were recruited, with 18.9% (82/435) having MP-BSI. The median age was 62 (interquartile range, 51,72) years, and 74.5% of the patients were male. Multivariate analysis revealed that immunosuppression, community-acquired MP (CA-MP), time from initial to targeted antibiotic use, high Sequential Organ Failure Assessment (SOFA) score, increased respiratory rate, and elevated γ-GT level (all p < 0.05) were independent risk factors for MP-BSI, while targeted treatment with linezolid was a protective factor. Patients with MP-BSI had a longer duration of hospitalization (median days, 27.5 vs. 19, p = 0.001), a higher 28-day mortality rate (24.4% vs. 11.0%, p = 0.001), and a higher in-hospital mortality rate (26.8% vs. 14.7%, p = 0.009) than those with MP alone. Conclusion: Secondary MRSA-BSI among patients with MP is not rare. Immunosuppression, CA-MP, time from initial to targeted antibiotic use, high SOFA score, increased respiratory rate and elevated γ-GT level are all independent risk factors for MP-BSI; however, linezolid, as a targeted antibiotic, is a protective factor. Moreover, patients with MP may have worse clinical outcomes when they develop MRSA-BSI.
Farah Yusuf Mohamud M, Jeele MOO, Cetinkaya O
et al.
Mohamed Farah Yusuf Mohamud,1 Mohamed Osman Omar Jeele,1 Osman Cetinkaya,1 Senai Goitom Sereke,2 Felix Bongomin,3 Mohamed AM Ahmed4– 6 1Mogadishu Somali Turkish Training and Research Hospital, Mogadishu, Somalia; 2School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda; 3Department of Medical Microbiology and Immunology, Faculty of Medicine, Gulu University, Gulu, Uganda; 4Department of Paediatrics, Faculty of Medicine and Surgery, Mogadishu University, Mogadishu, Somalia; 5Department of Paediatric Cardiology, Uganda Heart Institution, Kampala, Uganda; 6Basaksehir Cam ve Sakura Sehir Hospital, University of Health Sciences, Istanbul, TurkeyCorrespondence: Mohamed Farah Yusuf Mohamud, Mogadishu Somali Turkey Education and Research Hospital, Thirty Street, Alikamin, Wartanabada District, Mogadishu, Somalia, Tel +252615591689, Email m.qadar59@gmail.comIntroduction: Heart failure (HF) is a heterogeneous syndrome and growing global epidemic estimated to affect over 26-million people worldwide. The aim of this study is to investigate baseline characteristics, risk factors, and etiology of HF among patients admitted to a tertiary hospital in Somalia.Methods: Between May and October 2021, data on epidemiological characteristics, risk factors, and etiology of HF among patients admitted to an emergency unit of a teaching hospital in Mogadishu, Somalia were retrospectively collected and analyzed.Results: A total of 155 patients were disgnosed with HF, their mean age was 65.9 ± 14 years, 46.5% (n = 72) were aged 65 years or older, and 58.7% (n=91) were female. The prevalence of HF was 3.4% (n = 155); 47.7% (n = 74) had HF with preserved ejection fraction (HFpEF), 34.9% HF with reduced ejection fraction (HFrEF), and 17.4% HF with mildly reduced ejection fraction (HFmrEF). More females than males had HFrEF and HFmrEF (77.8% vs 59.3%, p < 0.01). In contrast, HFpEF was more frequent in males (55.4% vs 44.6%, p < 0.01). Regarding cardiovascular disease risk factors, patients with HFpEF had advanced age (≥ 65), were male, and had co-morbid hypertensive heart disease (HHD) and thyroid disease. On the other hand, patients with HFrEF were more in older age (50– 64 years), were women, and frequently had ischemic heart disease and diabetes mellitus compared to patients with HFpEF and HFmrEF. Among participants with HFpEF, HHD (58.1%) was the most common etiologies of HF, whereas in HFrEF, ischemic heart disease (38.9%) was prominent.Conclusion: HF in Somalia, different risk factors and etiologies were found in three groups of HF patients. Hypertension plays a predominant role both in its risk factors and the underlying cause of HF. We recommend implementation of specialized cardiac centers for the adult patients with cardiovascular diseases and promotion of the awareness of cardiovascular risk factors.Keywords: heart failure, ejection fraction, hypertension, diabetes, emergency unit, Somalia
Diseases of the circulatory (Cardiovascular) system
Mohammad Pousti, MirPouyan Zarabadi, Mehran Abbaszadeh Amirdehi
et al.
Bacterial biofilms are among the oldest and most prevalent multicellular life forms on Earth and are increasingly relevant in research areas related to industrial fouling, medicine and biotechnology. The main hurdles to obtaining definitive experimental results include time-varying biofilm properties, structural and chemical heterogeneity, and especially their strong sensitivity to environmental cues. Therefore, in addition to judicious choice of measurement tools, a well-designed biofilm study requires strict control over experimental conditions, more so than most chemical studies. Due to excellent control over a host of physiochemical parameters, microfluidic flow cells have become indispensable in microbiological studies. Not surprisingly, the number of lab-on-chip studies focusing on biofilms and other microbiological systems with expanded analytical capabilities has expanded rapidly in the past decade. In this paper, we comprehensively review the current state of microfluidic bioanalytical research applied to bacterial biofilms and offer a perspective on new approaches that are expected to drive continued advances in this field.