Hasil untuk "Infectious and parasitic diseases"

Hanna-Leena Melender, Elina Koota, Katariina Kainulainen et al.

Summary: Background: Patient education regarding hand hygiene (HH) and the correct use of non-sterile clinical gloves (NSCGs) are important parts of infection prevention and control. Unnecessary use of NSCGs can be harmful, has associated financial costs, and harms the environment. This study aimed to explore healthcare workers' (HCWs) adherence to patient education regarding HH and the correct use of NSCGs in an observation unit. Methods: Data in this observational descriptive cross-sectional study were collected from patients using a questionnaire. The questionnaire asked about the patient education received and the use of NSCGs by HCWs. The correctness of NSCG use was determined by the investigators based on standard precautions on infection prevention and control. Statistical analysis and qualitative content analysis were performed. Results: The convenience sample consisted of 174 patients in an observation unit at Helsinki University Hospital, and 600 care, examination or test procedures conducted for patients. The response rate was 87%. Of the participating patients, 8.6% reported that they had received patient education on HH. Eighteen different procedures were conducted for the study patients. The use of NSCGs was always correct for six procedures. Unnecessary use of NSCGs was found (to varying degrees) for nine procedures, and insufficient use of NSCGs was found for three procedures. An association was found between a procedure/procedure type conducted for a patient and the correct use of NSCGs (P<0.001). Conclusions: Deviations from the standard precautions existed. Interventions for HCWs are needed to support routine patient education on HH and evidence-based use of NSCGs.

Bimandra A. Djaafara, Verry Adrian, Etrina Eriawati et al.

Diphtheria has resurged globally, including in Indonesia, despite widespread vaccination since the 1970s. Knowledge gaps persist in understanding contemporary transmission drivers and effective outbreak control, especially in densely populated areas like Jakarta. We analyzed the 2017 Jakarta outbreak data and developed a compartmental model incorporating estimates of population susceptibility and asymptomatic carriers. Key epidemiological parameters were estimated, and various control measures were simulated. Our study found overall diphtheria susceptibility at 12.9 % (95 % CrI: 8.6 %–19.0 %) and 28.0 % (95 % CrI: 20.5 %–36.0 %) in children under 5 under different modeling scenarios, which were below the 'herd immunity threshold'. We estimated asymptomatic carriers to be highly prevalent, substantially contributing to the reproduction number. The model indicated that contact tracing and treating suspected cases and their contacts were more effective in preventing new cases than catch-up vaccination alone. These findings provide valuable insights for future outbreak management strategies in similar settings.

Isadonna F. Tengganu, Ke Hu

Abstract Chromera velia is a photosynthetic, free-living alga that is closely related to the apicomplexans, a phylum of intracellular parasites responsible for many devastating diseases, including malaria, cryptosporidiosis, and toxoplasmosis. With molecular and cellular landmarks that are clearly related to but distinguishable from those found in apicomplexan parasites, Chromera provides a fantastic opportunity to investigate the evolutionary origin of the structures and processes needed for intracellular parasitism. However, tools for defining localization and functions of gene products do not exist for Chromera , which creates a major bottleneck for exploring its biology. Here we report two major advances in exploring the cell biology of this free-living relative of a large group of intracellular parasites: 1) successful cell transformation and 2) the implementation of expansion microscopy. The initial analysis enabled by these tools generated new insights into subcellular organization in different life stages of Chromera. These new developments boost the potential of Chromera as a model system for understanding the evolution of parasitism in apicomplexans.

Lyubomira Boyanova, Zoia Ivanova

Coccidioidomycosis is a systemic fungal infection with no local distribution in Europe , associated with travelling to endemic regions of the world. The disease is defined as local but of global importance due to the increasing number of travellers to endemic areas. We present a rare case of coccidioidomycosis in a 52-year-old woman living in the state of Arizona, USA. The patient had no disease symptoms, but computed axial tomography performed at the annual follow-up showed a small dense mass in the right lung. Positron emission tomography was also performed for suspicion of neoplasia, without conclusive evidence of malignancy. On her annual holiday in Bulgaria, the woman decided to consult a microbiologist at the National Centre of Infectious and Parasitic Diseases in Sofia, based on her information about the disease and the regions at risk of infection. The patient was referred to a consultation with a pulmonologist. As recommended by the pulmonologist, the lung nodule was surgically removed and subsequent histological and microbiological studies (Sabouraud agar medium culture) confirmed the diagnosis of coccidioidomycosis. This is only the second case of this systemic mycosis registered in Bulgaria, showing that the diagnosis is difficult due to the lack of specific symptoms. A multidisciplinary approach is essential for the rapid diagnosis and timely treatment, which in turn is a prerequisite for a favorable outcome of the disease.

Xianhua Gui, Xiaohua Qiu, Yujuan Gao et al.

Acute fibrinous and organizing pneumonia (AFOP) is a rare type of lung injury, and while Chlamydia psittaci pneumonia is a zoonotic disease, secondary AFOP has not been previously reported. We present a 53-year-old female with a 13-day history of cough, fever, and shortness of breath. High-resolution computed tomography (HRCT) showed multiple bilateral patchy shadows and consolidations in the left lung lower lobe. Empirical treatment was ineffective, and lung lesions worsened. Metagenomic next-generation sequencing (mNGS) confirmed Chlamydia psittaci infection. After minocycline treatment, the patient’s fever improved, but shortness of breath persisted. CT-guided lung biopsy revealed “fibrin balls” in the alveolar space and interstitial inflammatory infiltrates. Shortness of breath improved after glucocorticoid therapy, with significant lesion absorption noted on follow-up chest CT. This case suggests a possible association between AFOP and C. psittaci infection, supporting the use of combined antibiotic and glucocorticoid therapy.

Gemma Moncunill, Eldo Elobolobo, Carlos Chaccour et al.

Introduction Noma is a rapidly progressing, disfiguring orofacial necrotising infection that primarily affects children living in poverty. To date, there are no primary data reporting noma in Mozambique. Our aim was to collect empirical evidence on the ongoing presence of noma in Zambezia Province, Mozambique, for the first time.Methods We used a passive case search approach at the maxillofacial and paediatric wards of the reference hospital to identify acute noma cases. To find noma survivors, we conducted a community-based active case search, showing posters of noma sequelae to crowds of potential informants. We visited 12 of the 22 districts in the province and administered a questionnaire to each confirmed noma case.Results Over a 5-week period, two acute noma cases and 21 survivors having had noma between 1971 and 2015 were identified. Using a cohort-estimated healthcare-seeking proportion of 18.75% and assuming a survival rate of 10%, the annual incidence in rural areas of Zambezia was estimated at 13.7 per 100 000 children under the age of nine years, suggesting that at least 213 noma cases occur yearly in the region.Conclusion The total lack of data does not mean noma is non-existent in Mozambique. This study provides a simple methodology to rapidly identify noma cases in high-risk areas and populations. Noma is likely present wherever there is poverty. Increased awareness, reporting and public health interventions are urgently needed worldwide to stop the consequences of this preventable and treatable disease.

Shimaa A. E-S. El-Sayed, Mohamed A. Rizk, Hang Li et al.

Due to the lack of efficacy of the currently used chemical drugs, poor tick control, and lack of effective vaccines against Babesia, novel control strategies are urgently needed. In this regard, searching for anti-Babesia gene therapy may facilitate the control of this infection. Following this pattern, small interfering RNAs (siRNAs) are widely used to study gene function and hence open the way to control the parasite. However, the primary constraint of this approach is the lack of Babesia to RNA-induced silencing complex (RISC) enzymes, making siRNA impractical. In this study, we preassembled complexes with the human enzyme argonaute 2 (hAgo2) and a small interfering RNA (siRNA)/single-stranded RNA (ssRNA) against B. gibsoni and B. microti metabolite transporters. The assembled complexes were generated by developing a gene delivery system with chitosan dehydroascorbic acid nanoparticles. The delivery system effectively protected the loaded RNAi and targeted Babesia-infected RBCs with a relatively high internalization rate. The assembled complexes were successfully transfected into live parasites for specific slicing of Babesia targets. We demonstrated a reduction in the expression of target genes at the mRNA level. Furthermore, this silencing inhibited Babesia growth in vitro and in vivo. For the first time, we used this method to confirm the role of the assembled complexes in manipulating the noncanonical pathway of RNAi in Babesia parasites. This novel method provides a means of silencing Babesia genes to study their role in host–parasite interactions and as potential targets for gene therapy and control.

Austin Polanco, M. E. J. Newman

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.

L Cif, D Demailly, JP Lin et al.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012).

Guanchu Wang, Junhao Ran, Ruixiang Tang et al.

Despite the impressive capabilities of Large Language Models (LLMs) in general medical domains, questions remain about their performance in diagnosing rare diseases. To answer this question, we aim to assess the diagnostic performance of LLMs in rare diseases, and explore methods to enhance their effectiveness in this area. In this work, we introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of LLMs in diagnosing rare diseases. Specifically, we collected 1360 high-quality question-answer pairs within the ReDis-QA dataset, covering 205 rare diseases. Additionally, we annotated meta-data for each question, facilitating the extraction of subsets specific to any given disease and its property. Based on the ReDis-QA dataset, we benchmarked several open-source LLMs, revealing that diagnosing rare diseases remains a significant challenge for these models. To facilitate retrieval augmentation generation for rare disease diagnosis, we collect the first rare diseases corpus (ReCOP), sourced from the National Organization for Rare Disorders (NORD) database. Specifically, we split the report of each rare disease into multiple chunks, each representing a different property of the disease, including their overview, symptoms, causes, effects, related disorders, diagnosis, and standard therapies. This structure ensures that the information within each chunk aligns consistently with a question. Experiment results demonstrate that ReCOP can effectively improve the accuracy of LLMs on the ReDis-QA dataset by an average of 8%. Moreover, it significantly guides LLMs to generate trustworthy answers and explanations that can be traced back to existing literature.

Xuanzhong Chen, Ye Jin, Xiaohao Mao et al.

Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience, make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents powered by large language models (LLMs) have demonstrated notable applications across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical examinations. However, current agent frameworks are not well-adapted to real-world clinical scenarios, especially those involving the complex demands of rare diseases. To bridge this gap, we introduce RareAgents, the first LLM-driven multi-disciplinary team decision-support tool designed specifically for the complex clinical context of rare diseases. RareAgents integrates advanced Multidisciplinary Team (MDT) coordination, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents outperforms state-of-the-art domain-specific models, GPT-4o, and current agent frameworks in diagnosis and treatment for rare diseases. Furthermore, we contribute a novel rare disease dataset, MIMIC-IV-Ext-Rare, to facilitate further research in this field.

Ethan Kane Waters, Carla Chia-ming Chen, Mostafa Rahimi Azghadi

Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management. This study employed various machine learning techniques to detect the presence of RSD in different sugarcane varieties, using vegetation indices derived from freely available satellite-based spectral data. Our results show that the Support Vector Machine with a Radial Basis Function Kernel (SVM-RBF) was the most effective algorithm, achieving classification accuracy between 85.64% and 96.55%, depending on the variety. Gradient Boosting and Random Forest also demonstrated high performance achieving accuracy between 83.33% to 96.55%, while Logistic Regression and Quadratic Discriminant Analysis showed variable results across different varieties. The inclusion of sugarcane variety and vegetation indices was important in the detection of RSD. This agreed with what was identified in the current literature. Our study highlights the potential of satellite-based remote sensing as a cost-effective and efficient method for large-scale sugarcane disease detection alternative to traditional manual laboratory testing methods.

Abdurezak Kemal, Kenzudin Assfa, Bisrat Zeleke et al.

Abstract Introduction The World Health Organization declared COVID-19 is a pandemic disease. Countries should take standard measures and responses to battle the effects of the viruses. However, little is known in Ethiopia regarding the recommended preventive behavioral messages responses. Therefore, the study aimed to assess the response to COVID-19 recommended preventive behavioral messages. Methods Community-based cross-sectional study design was carried out from 1 to 20, July 2020. We recruited 634 respondents by using a systematic sampling method. Data were analyzed using Statistical Package Software for Social Sciences version 23. Association between variables were explored using a bivariable and multi variable logistic regression model. The strength of the association is presented using odds ratio and regression coefficient with 95% confidence interval. A p-value of less than 0.05 was declared statistically significant. Results: Three hundred thirty-six (53.1%) of respondents had good response to recommended preventive behavioral messages. The general precise rate of the knowledge questionnaire was 92.21%. The study showed that merchant was 1.86 (p ≈ 0.01) times more likely respond to COVID-19 recommended preventive behavioral messages than government-employed. Respondents who scored one unit increase for self-efficacy and response-efficacy, the odds of responding to COVID-19 recommended preventive behavioral messages were increased by 1.22 (p < 0.001), and 1.05 times (p = 0.002) respectively. Respondents who scored one unit increase to cues to action, the odds of responding to COVID-19 recommended preventive behavioral messages were 43% (p < 0.001) less likely. Conclusion Even though respondents were highly knowledgeable about COVID-19, there is a lower level of applying response to recommended preventive behavioral messages. Merchant, self-efficacy, response efficacy, and cues to action were significantly associated with response to recommended preventive behavioral messages. Like merchants, government employer should be applying preventive behavioral messages and also, participants’ self and response efficacy should be strengthened to improve the response. In addition, we should be changed or modified the way how-to deliver relevant information, promoting awareness, and also using appropriate reminder systems to preventive behavioral messages.

Timmer, Antje, Neuser, Johanna, Uslar, Verena et al.

Introduction: According to the Master Plan 2020, science education will play a critical role in future medical curricula. Science modules have already been implemented at many locations. Other medical faculties will follow in the next few years, as legislation is expected to make recommendations of the national competence-based learning objectives curriculum for medicine (NKLM) mandatory. This article aims to present an implementation example from epidemiology and biometry as a contribution to the didactic discussions within the data sciences in medicine. Project description: We report on our experiences with a data analysis project for second-year medical students, which has been compulsory at the Faculty of Medicine and Health Sciences since 2019. The project is intended to train the scientific skills required from the subjects of epidemiology and biometry for student research projects. Emphasis is placed on responsible data handling, transparency, and reproducibility. For example, the writing of a statistical analysis plan is required prior to data access. Improved standardization of materials, optional use of the English language, and digital support will be implemented to help manage the project when student numbers increase. Discussion: The experience from five years is very positive, although a formal evaluation of the learning success is still pending. Current challenges concern staffing, additional time and supervision requirements for those students who do statistical programming with R, and improved integration into the medical curriculum.

Zenin Easa Panthakkalakath, Neeraj, Jimson Mathew

The challenges posed by epidemics and pandemics are immense, especially if the causes are novel. This article introduces a versatile open-source simulation framework designed to model intricate dynamics of infectious diseases across diverse population centres. Taking inspiration from historical precedents such as the Spanish flu and COVID-19, and geographical economic theories such as Central place theory, the simulation integrates agent-based modelling to depict the movement and interactions of individuals within different settlement hierarchies. Additionally, the framework provides a tool for decision-makers to assess and strategize optimal distribution plans for limited resources like vaccines or cures as well as to impose mobility restrictions.

Sayantan Nag Chowdhury, Jeet Banerjee, Matjaž Perc et al.

Predator prey interactions are one of ecology's central research themes, but with many interdisciplinary implications across the social and natural sciences. Here we consider an often-overlooked species in these interactions, namely parasites. We first show that a simple predator prey parasite model, inspired by the classical Lotka Volterra equations, fails to produce a stable coexistence of all three species, thus failing to provide a biologically realistic outcome. To improve this, we introduce free space as a relevant eco-evolutionary component in a new mathematical model that uses a game-theoretical payoff matrix to describe a more realistic setup. We then show that the consideration of free space stabilizes the dynamics by means of cyclic dominance that emerges between the three species. We determine the parameter regions of coexistence as well as the types of bifurcations leading to it by means of analytical derivations as well as by means of numerical simulations. We conclude that the consideration of free space as a finite resource reveals the limits of biodiversity in predator prey parasite interactions, and it may also help us in the determination of factors that promote a healthy biota.

Konrad Furmańczyk, Wojciech Niemiro, Mariola Chrzanowska et al.

We propose a new graphical model to describe the comorbidity of allergic diseases. We present our model in two versions. First, we introduce a generative model that correctly reflects the variables' causal relationship. Then we propose an approximation of the generative model by another misspecified model that is computationally more efficient and easily interpretable. We will focus on the misspecified version, which we consider more practical. We include in the model two directed graphs, one graph of known dependency between the main binary variables (diseases), and a second graph of the dependence between the occurrence of the diseases and their symptoms. In the model, we also consider additional auxiliary variables. The proposed model is evaluated on a cross-sectional multicentre study in Poland on the ECAP database (www.ecap.pl). An assessment of the stability of the proposed model was obtained using bootstrap and jackknife techniques.

Yousuf Rayhan Emon, Md Golam Rabbani, Md. Taimur Ahad et al.

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies

Cici Apriza Yanti, Mila Sari, Ane Triana

ABSTRACT Self-protection against the bite of the Aedes Aegypty mosquito is very necessary today. Especially to avoid the occurrence of vector-borne diseases, one of which is Dengue Hemorrhagic Fever (DHF). Dengue fever is an infectious disease that is a health problem in the world, especially developing countries. For this reason, it is necessary to make efforts to protect oneself from the bite of the Aedes aegypti mosquito vector. One of them is by using a natural repellent, namely citronella (Cymbopogon Winterianus Jowitt). Lemongrass plants can be used to repel mosquitoes because they contain substances such as geraniol, metal heptenon from Cymbopogon winterianus Jowitt   oil so that it can be used as a repellent. This study aims to analyze the protective power of citronella by using citronella extract to protect against the bite of the Aedes aegypti mosquito. This research is experimental with the independent variable concentration of citronella extract (75%, 60%, 45%, 30%, 15%) and the dependent variable is mosquito bite protection. The design of this study was a posttest only control group design which was statistically analyzed using Analysis of Variance (ANOVA). The results showed that the most effective concentration of citronella extract was a concentration of 75%. This can be seen from the number of mosquitoes that landed on the hands that had been smeared with citronella extract (Cymbopogon winterianus Jowitt) at a concentration of 75% with a total of 8 tails at 5 hours with a protective power of 92.26%. The results of the study concluded that the extract of citronella (Cymbopogon winterianus Jowitt) was effective against the protective power of Aedes aegypti mosquito bites. The results of this study are expected to be socialized and developed so that it can be used by the community so that it can reduce the incidence of DHF.  ABSTRAK Perlindungan diri terhadap gigitan nyamuk Aedes aegypti sangat diperlukan dewasa ini. Terutama untuk menghindari terjadinya penyakit bawaan vektor yang salah satunya adalah  Demam Berdarah Dengue (DBD). Penyakit demam berdarah ini merupakan penyakit menular yang menjadi masalah kesehatan di dunia terutama negara berkembang. Untuk itu perlu dilakukan usaha untuk melindungi diri dari gigitan vektor nyamuk  Aedes aegypti. Salah satunya dengan menggunakan repellent berbahan alami yaitu serai wangi (Cymbopogon winterianus Jowitt). Tanaman sereh dapat dimanfaatkan untuk mengusir nyamuk karena mngandung zat-zat seperti geraniol, metal heptenon dari mintak atsiri sereh sehingga bisa digunakan sebagai repellent.  Penelitian ini bertujuan untuk menganalisa seberapa besar daya proteksi serai wangi dengan menggunakan ekstrak citronelle untuk melindungi dari gigitan nyamuk Aedes aegypti. Penelitian ini bersifat eksperimental dengan variabel independen konsentrasi ekstrak serai wangi (konsentrasi 75%, 60%, 45%, 30%, 15%) dan variabel dependen adalah daya proteksi gigitan nyamuk. Rancangan penelitian ini adalah  posttest only control group design dianalisis secara statistik menggunakan Analisis Varians (ANOVA). Hasil penelitian menunjukkan konsentrasi ekstrak serai wangi yang paling efektif adalah konsentrasi 75%. Ini dapat dilihat dari jumlah nyamuk yang hinggap ditangan yang telah diolesi ekstrak serai wangi (Cymbopogon winterianus Jowitt) pada konsentrasi 75 % dengan jumlah 8 ekor pada jam ke-5 dengan daya proteksi 92,26%. Hasil Penelitian dapat disimpulkan bahwa ekstrak serai wangi (Cymbopogon winterianus Jowitt) efektif terhadap daya proteksi gigitan nyamuk Aedes aegypti. Hasil penelitian ini diharapkan dapat  disosialisasikan dan dikembangkan agar dapat menekan angka kejadian penyakit DBD.