Shaheer Ahmad Khan, Muhammad Usamah Shahid, Muddassar Farooq
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.
Although food is essential for the survival of organisms, it can also trigger a variety of adverse reactions, ranging from nutrient intolerances to celiac disease and food allergies. Food not only contains essential nutrients but also includes numerous substances that may have positive or negative effects on the consuming organism. To protect against potentially harmful components, all animals have evolved defense mechanisms, which are similar to antimicrobial defenses but often come at the cost of the organism’s health. When these defensive responses are exaggerated or misdirected, they can lead to adverse food reactions, where the costs outweigh the benefits. Furthermore, due to the persistent toxicity of harmful food components, the failure of defense mechanisms can also result in pathological effects triggered by food. This article review presents a food quality control framework that aims to clarify how these reactions relate to normal physiological processes. Organisms utilize several systems to coexist with symbiotic microbes, regulate them, and concurrently avoid, expel, or neutralize harmful pathogens. Similarly, food quality control systems allow organisms to absorb necessary nutrients while defending against low-quality or harmful components in food. Although many microbes are lethal in the absence of antimicrobial defenses, diseases related to microbiome dysregulation, such as inflammatory bowel disease, have significantly increased. Antitoxin defenses also come with costs and may fail due to insufficiencies, exaggerations, or misdirected actions, ultimately leading to adverse food reactions. With the changes in human diet and lifestyle, the failure of defense mechanisms has contributed to the rising incidence of food intolerances. This review explores the mechanisms of antitoxin defenses and analyzes how their failure can lead to adverse food reactions, emphasizing the importance of a comprehensive understanding of food quality control mechanisms for developing more effective treatments for food-triggered diseases.
Nikolaos Giannelos, Desmond Curran, Sean Matthews
et al.
Abstract Introduction Herpes zoster (HZ, Shingles) is a vaccine-preventable viral disease impacting patients’ quality of life owing to pain and rash. An estimated 15 million HZ cases occur annually in individuals aged ≥ 50 years worldwide. Recombinant zoster vaccine (RZV) is effective in protecting against HZ. This is the first study evaluating the potential incremental public health benefits in terms of HZ cases averted worldwide by vaccinating adults aged ≥ 50 years with RZV. Methods A previously published static multi-cohort Markov model with an annual cycle length and lifetime horizon was used for all analyses. Demographic data depicting populations on 31 December 2023, and age–sex specific mortality rates by region were sourced from United Nations (2022). HZ incidence rates were informed from a recent meta-regression analysis of global HZ burden (Asia, Europe, Northern America, Oceania, and worldwide). RZV efficacy and waning modelling was based on 11-year clinical trial follow-up data [NCT02723773]. Results Assuming 70% second-dose compliance in the general population aged ≥ 50 years worldwide, increased RZV uptake by 5% reduced the number of expected HZ cases by > 10 million over the vaccinated cohort’s remaining lifetime. More than 5 million of the averted cases were among the cohort vaccinated at ages 50–59 years. Numbers needed to vaccinate (NNV) to avert one HZ case worldwide ranged from 9 at 50–59 years to 18 at ≥ 80 years-of-age, with an overall NNV of 10 for the entire cohort aged ≥ 50 years. Variations observed by region and vaccination age reflected varying inputs, i.e., population counts, HZ incidence rates, mortality rates, and vaccine efficacy waning by age. Conclusions A modest (5%) increase in absolute RZV uptake worldwide was estimated to avert millions of additional HZ cases. Lower NNVs were observed in younger vaccinated cohorts irrespective of region, outlining the merits of long-term protection afforded by RZV, and suggesting that earlier vaccination with RZV may be a more effective public health policy against HZ. Greater numbers of averted HZ cases and lower NNVs estimated at ideal second dose compliance demonstrated the importance of timely series completion.
BackgroundMalaria is the most important parasitic illness causing morbidity and mortality with high prevalence in tropical regions.ObjectiveThis study was aimed at evaluating the 7-year malaria trend and community awareness at Jawi Health Center and primary Hospital in Northwest Ethiopia.MethodsA retrospective and cross-sectional or prospective design were used for the study. The data was analyzed using SPSS version 22 software. The findings were considered significant at P < 0.05.ResultsAmong 62,624 blood films between 2015 and 2021 at Jawi Health Center, 40.9% were positive. Plasmodium falciparum accounted for 85.8%. Women had more mixed infections (P. falciparum and P. vivax) (X2 = 8.9, df = 2, P = 0.011) than men. A greater proportion (20.6%) of malaria cases was observed within the under 5 years age group and the number of malaria cases was higher in September, October, and June. The overall prevalence of malaria was found to be 25.2% and June had the highest proportion (75.6%). In total, 335 (80.9%) respondents recognized mosquito bites as the cause and fever (50%) as a clinical symptom of malaria. More than half of the respondents (60.1%) never sleep under mosquito nets.ConclusionThus, these findings have substantial implications for the trend of malaria prevalence and patient awareness of the disease which support the existing malaria control efforts.
Noer Farakhin, Atik Choirul Hidajah, Retna Gumilang
et al.
Background: The SARS-CoV-2 mutation in the bodies of COVID-19 patients is a critical factor for health. Notably, in October 2022, the disease recovery rate in Sidoarjo (96.05%) was lower than the national rate (97.30%). Purpose: This study aims to identify the recovery time and its influencing factors in COVID-19 patients in Sidoarjo. Methods: The life-table method was used to conduct a survival analysis on data from the NAR COVID-19 dashboard in Sidoarjo from January 3 to August 4, 2022, which involved 20,662 respondents. Age group, sex, treatment status, and testing purpose were analyzed. Results: The majority of COVID-19 patients were females (50.30%) aged 26-<46 years (46.24%). Most patients (64.74%) chose to self-isolate, and 63.34% were screened as COVID-19 positive. On average, patients recovered within seven days of diagnosis. The Wilcoxon (Gehan) statistical test yielded a p-value of less than 0.001 for all factors at a significance level of 0.05, indicating a significant difference in the survival time distribution. The age group with the shortest median recovery time was 6-<12 years at 7.03 days, while males had a median recovery time of at 7.66 days. Patients who self-isolated had a median recovery time of 7.16 days, and those who were tested for close contact purposes had a median recovery time of 7.65 days. Conclusion: The median recovery time for COVID-19 patients post-diagnosis was seven days. There was a significant difference in recovery time among the COVID-19 patients based on age group, sex, treatment status, and testing purpose.
Public aspects of medicine, Infectious and parasitic diseases
Yao-Ming Huang, Li-Jane Shih, Teng-Wei Hsieh
et al.
The THαβ host immunological pathway contributes to the response to infectious particles (viruses and prions). Furthermore, there is increasing evidence for associations between autoimmune diseases, and particularly type 2 hypersensitivity disorders, and the THαβ immune response. For example, patients with systemic lupus erythematosus often produce anti-double stranded DNA antibodies and anti-nuclear antibodies and show elevated levels of type 1 interferons, type 3 interferons, interleukin-10, IgG1, and IgA1 throughout the disease course. These cytokines and antibody isotypes are associated with the THαβ host immunological pathway. Similarly, the type 2 hypersensitivity disorders myasthenia gravis, Graves’ disease, graft-versus-host disease, autoimmune hemolytic anemia, immune thrombocytopenia, dermatomyositis, and Sjögren’s syndrome have also been linked to the THαβ pathway. Considering the potential associations between these diseases and dysregulated THαβ immune responses, therapeutic strategies such as anti-interleukin-10 or anti-interferon α/β could be explored for effective management.
Temesgen Ashine, Adane Eyasu, Yehenew Asmamaw
et al.
Abstract Background Malaria is a major public health concern in Ethiopia, and its incidence could worsen with the spread of the invasive mosquito species Anopheles stephensi in the country. This study aimed to provide updates on the distribution of An. stephensi and likely household exposure in Ethiopia. Methods Entomological surveillance was performed in 26 urban settings in Ethiopia from 2021 to 2023. A kilometer-by-kilometer quadrant was established per town, and approximately 20 structures per quadrant were surveyed every 3 months. Additional extensive sampling was conducted in 50 randomly selected structures in four urban centers in 2022 and 2023 to assess households’ exposure to An. stephensi. Prokopack aspirators and CDC light traps were used to collect adult mosquitoes, and standard dippers were used to collect immature stages. The collected mosquitoes were identified to species level by morphological keys and molecular methods. PCR assays were used to assess Plasmodium infection and mosquito blood meal source. Results Catches of adult An. stephensi were generally low (mean: 0.15 per trap), with eight positive sites among the 26 surveyed. This mosquito species was reported for the first time in Assosa, western Ethiopia. Anopheles stephensi was the predominant species in four of the eight positive sites, accounting for 75–100% relative abundance of the adult Anopheles catches. Household-level exposure, defined as the percentage of households with a peridomestic presence of An. stephensi, ranged from 18% in Metehara to 30% in Danan. Anopheles arabiensis was the predominant species in 20 of the 26 sites, accounting for 42.9–100% of the Anopheles catches. Bovine blood index, ovine blood index and human blood index values were 69.2%, 32.3% and 24.6%, respectively, for An. stephensi, and 65.4%, 46.7% and 35.8%, respectively, for An. arabiensis. None of the 197 An. stephensi mosquitoes assayed tested positive for Plasmodium sporozoite, while of the 1434 An. arabiensis mosquitoes assayed, 62 were positive for Plasmodium (10 for P. falciparum and 52 for P. vivax). Conclusions This study shows that the geographical range of An. stephensi has expanded to western Ethiopia. Strongly zoophagic behavior coupled with low adult catches might explain the absence of Plasmodium infection. The level of household exposure to An. stephensi in this study varied across positive sites. Further research is needed to better understand the bionomics and contribution of An. stephensi to malaria transmission. Graphical Abstract
Nicoleta-Ana Tomsa, Lorena Elena Melit, Teodora Popescu
et al.
Introduction. Pneumonia is a common infectious disease among children, very familiar to pediatricians and a major cause of hospitalization worldwide. Mycoplasma pneumoniae (M. pneumoniae) - atypical pathogen, is estimated to
be responsible for approximately 30-40% of community-acquired pneumonias.
The aim of the paper was to underline the diagnostic and treatment difficulties in a case of pneumonia with M. pneumoniae in a school-aged boy, with multiple presentations in the Emergency Service for respiratory difficulties. Material and method. We present the case of a 7 years and 10-month-old male patient, admitted to our clinic for wheezing, cough and dyspnea. Results. The clinical exam at admission pointed out influenced general status, wheezing, thoraco-abdominal swing, intercostal draft, sibilant and crackles, oxygen saturation 89%, tachycardia 134 beats/minute. The blood tests revealed mild leukocytosis, with neutrophilia, slightly increased inflammatory biomarkers. Considering the general status and the presence of functional respiratory syndrome, steroid and symptomatic anti-inflammatory treatment is initiated. However, the general condition remains stable, which required the widening of the spectrum of investigations with the performance of a chest CT noticing a pneumonic condensation with an air bronchogram located at the level of the right upper lobe. Considering the stationary respiratory functional syndrome and the radiological appearance of the pneumonia as well as the age, we performed serology for atypical germs and identified a positive titer of Ig M antibodies for M. pneumoniae by instituting Azithromycin po, but after 3 days, the evolution remains stationary, thus we changed the treatment with intravenous Levofloxacin, with a favorable subsequent evolution. Conclusions. Pneumonia with atypical pathogens such as M. pneumoniae is a well-defined and well-known pathology in the literature, but it still remains a condition that imposes multiple difficulties related to the diagnosis and therapeutic management of pediatric cases.
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.
Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model achieving very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain
Adrian Gheorghiu, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel
et al.
The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification.
Currently, many researchers and analysts are working toward medical diagnosis enhancement for various diseases. Heart disease is one of the common diseases that can be considered a significant cause of mortality worldwide. Early detection of heart disease significantly helps in reducing the risk of heart failure. Consequently, the Centers for Disease Control and Prevention (CDC) conducts a health-related telephone survey yearly from over 400,000 participants. However, several concerns arise regarding the reliability of the data in predicting heart disease and whether all of the survey questions are strongly related. This study aims to utilize several machine learning techniques, such as support vector machines and logistic regression, to investigate the accuracy of the CDC's heart disease survey in the United States. Furthermore, we use various feature selection methods to identify the most relevant subset of questions that can be utilized to forecast heart conditions. To reach a robust conclusion, we perform stability analysis by randomly sampling the data 300 times. The experimental results show that the survey data can be useful up to 80% in terms of predicting heart disease, which significantly improves the diagnostic process before bloodwork and tests. In addition, the amount of time spent conducting the survey can be reduced by 77% while maintaining the same level of performance.
Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain
et al.
Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems, drugs interact with protein targets in cells, influencing target activities and subsequently impacting biological pathways to promote healthy functions and treat diseases. Moving beyond binary relationships and exploring tighter triple relationships is essential to understanding drugs' mechanism of action (MoAs). Moreover, identifying the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, is critical to model these complex interactions appropriately. To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}). \texttt{HeTriNet} introduces a novel triplet attention mechanism within this heterogeneous graph structure. Beyond pairwise attention as the importance of an entity for the other one, we define triplet attention to model the importance of pairs for entities in the drug-target-disease triplet prediction problem. Experimental results on real-world datasets show that \texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable proficiency in uncovering novel drug-target-disease relationships.
Lyme disease Borrelia are obligately parasitic, tick- transmitted, invasive, persistent bacterial pathogens that cause disease in humans and non-reservoir vertebrates primarily through the induction of inflammation. During transmission from the infected tick, the bacteria undergo significant changes in gene expression, resulting in adaptation to the mammalian environment. The organisms multiply and spread locally and induce inflammatory responses that, in humans, result in clinical signs and symptoms. Borrelia virulence involves a multiplicity of mechanisms for dissemination and colonization of multiple tissues and evasion of host immune responses. Most of the tissue damage, which is seen in non-reservoir hosts, appears to result from host inflammatory reactions, despite the low numbers of bacteria in affected sites. This host response to the Lyme disease Borrelia can cause neurologic, cardiovascular, arthritic, and dermatologic manifestations during the disseminated and persistent stages of infection. The mechanisms by which a paucity of organisms (in comparison to many other infectious diseases) can cause varied and in some cases profound inflammation and symptoms remains mysterious but are the subjects of diverse ongoing investigations. In this review, we provide an overview of virulence mechanisms and determinants for which roles have been demonstrated in vivo, primarily in mouse models of infection.
Igor Henrique Coelho Fonseca, Mariela Cunha Pires Fiusa, Alfredo Ramon Alfonso Cavalcante Junior
et al.
A associação das infecções causadas pelo Vírus da Imunodeficiência Humana (HIV) e pelo protozoário Leishmania spp. caracteriza a coinfecção Leishmania-HIV. Esta coinfecção é considerada doença emergente de alta gravidade em várias regiões do mundo, e há projeções de seu crescimento contínuo, devido à superposição geográfica das duas infecções, como consequência da urbanização das leishmanioses e da interiorização da infecção por HIV. O objetivo desse trabalho é expor a coinfecção leishmaniose visceral e HIV. É um estudo epidemiológico realizado a partir de dados secundários obtidos através do DATASUS com os descritores leishmaniose, coinfecção leishmaniose-HIV. O período de abrangência teve 3200 casos de leishmaniose sendo desses 178 casos coinfectados com HIV (5,56%) dos quais 80,33% eram do sexo masculino. No Tocantins, a leishmaniose visceral mantém com alta incidência. Dessa forma devido aos preocupantes números apresentados concluímos que são necessárias medidas que diminuam as situações de vulnerabilidade e a falta de informação principalmente de baixa renda, com maior contingente de desfechos negativos.