-
BMC Public Health Jun 2024Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is...
BACKGROUND
Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.
METHODS
Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).
RESULTS
The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.
CONCLUSION
Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
Topics: Humans; COVID-19; Machine Learning; Ethiopia; Male; Female; Middle Aged; Adult; Algorithms; Aged; SARS-CoV-2; Hospitalization; Electronic Health Records; Young Adult; Adolescent
PubMed: 38943093
DOI: 10.1186/s12889-024-19196-0 -
Nature Communications Jun 2024Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic...
Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.
Topics: Animals; Grasshoppers; Odorants; Bayes Theorem; Social Behavior; Smell; Behavior, Animal; Crowding; Feeding Behavior; Olfactory Perception; Male; Female; Cues
PubMed: 38942759
DOI: 10.1038/s41467-024-49719-7 -
JMIR Medical Informatics Jun 2024The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a...
The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals.
PubMed: 38941141
DOI: 10.2196/58491 -
PLoS Biology Jun 2024Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these...
Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.
Topics: Caenorhabditis elegans; Animals; Chemotaxis; High-Throughput Screening Assays; Smell; Behavior, Animal; Software
PubMed: 38935621
DOI: 10.1371/journal.pbio.3002672 -
Neural Regeneration Research Jun 2024Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb,...
Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover, the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.
PubMed: 38934393
DOI: 10.4103/NRR.NRR-D-24-00312 -
Frontiers in Nutrition 2024Rhubarb is a popular food that relieves constipation and aids with weight loss. The traditional method of preparation, includes steaming and sun-drying rhubarb nine...
INTRODUCTION
Rhubarb is a popular food that relieves constipation and aids with weight loss. The traditional method of preparation, includes steaming and sun-drying rhubarb nine times (SDR-9) to reduce its toxicity and increase efficacy.
METHODS
Flavor analysis includes odor analysis by gas chromatography-ion mobility spectrometry and taste characterization using an electronic tongue.
RESULTS
Odor analysis of the samples prepared through SDR-9 identified 61 volatile compounds, including aldehydes, esters, alcohols, ketones, acids, alkenes, and furans. Of these, 13 volatile components were the key substances associated with odor. This enabled the process to be divided into two stages: 1-5 times of steaming and sun-drying and 6-9 times. In the second stage, SDR-6 and SDR-9 were grouped together in terms of odor. Analysis using electronic tongue revealed that the most prominent taste was bitterness. A radar map indicated that the bitterness response was the highest for raw rhubarb, whereas that for processed (steamed and sun-dried) rhubarb decreased. Orthogonal partial least squares discriminant analysis (OPLS-DA) clustering results for SDR-6 and SDR-9 samples indicated that their tastes were similar. Anthraquinones were analyzed via high-performance liquid chromatography; moreover, analysis of the taste and components of the SDR samples revealed a significant correlation.
DISCUSSION
These results indicate that there are similarities between SDR-6 and SDR-9 in terms of smell, taste, and composition, indicating that the steaming and sun-drying cycles can be conducted six times instead of nine.
PubMed: 38933883
DOI: 10.3389/fnut.2024.1406430 -
Vaccines Jun 2024SARS-CoV-2 infections elicit antibodies against the viral spike (S) and nucleocapsid (N) proteins; COVID-19 vaccines against the S-protein only. The BCG-Corona trial,...
SARS-CoV-2 infections elicit antibodies against the viral spike (S) and nucleocapsid (N) proteins; COVID-19 vaccines against the S-protein only. The BCG-Corona trial, initiated in March 2020 in SARS-CoV-2-naïve Dutch healthcare workers, captured several epidemic peaks and the introduction of COVID-19 vaccines during the one-year follow-up. We assessed determinants of systemic anti-S1 and anti-N immunoglobulin type G (IgG) responses using trial data. Participants were randomised to BCG or placebo vaccination, reported daily symptoms, SARS-CoV-2 test results, and COVID-19 vaccinations, and donated blood for SARS-CoV-2 serology at two time points. In the 970 participants, anti-S1 geometric mean antibody concentrations (GMCs) were much higher than anti-N GMCs. Anti-S1 GMCs significantly increased with increasing number of immune events (SARS-CoV-2 infection or COVID-19 vaccination): 104.7 international units (IU)/mL, 955.0 IU/mL, and 2290.9 IU/mL for one, two, and three immune events, respectively ( < 0.001). In adjusted multivariable linear regression models, anti-S1 and anti-N log concentrations were significantly associated with infection severity, and anti-S1 log concentration with COVID-19 vaccine type/dose. In univariable models, anti-N log concentration was also significantly associated with acute infection duration, and severity and duration of individual symptoms. Antibody concentrations were not associated with long COVID or long-term loss of smell/taste.
PubMed: 38932420
DOI: 10.3390/vaccines12060691 -
Nutrients Jun 2024Taste disorders (TDs) are common among systemically treated cancer patients and negatively impact their nutritional status and quality of life. The novel food approved... (Randomized Controlled Trial)
Randomized Controlled Trial
Taste disorders (TDs) are common among systemically treated cancer patients and negatively impact their nutritional status and quality of life. The novel food approved by the European Commission (EFSA), dried miracle berries (DMB), contains the natural taste-modifying protein miraculin. DMB, also available as a supplement, has emerged as a possible alternative treatment for TDs. The present study aimed to evaluate the efficacy and safety of habitual DMB consumption in malnourished cancer patients undergoing active treatment. An exploratory clinical trial was carried out in which 31 cancer patients were randomized into three arms [standard dose of DMB (150 mg DMB/tablet), high dose of DMB (300 mg DMB/tablet) or placebo (300 mg freeze-dried strawberry)] for three months. Patients consumed a DMB tablet or placebo daily before each main meal (breakfast, lunch, and dinner). Throughout the five main visits, electrochemical taste perception, nutritional status, dietary intake, quality of life and the fatty acid profile of erythrocytes were evaluated. Patients consuming a standard dose of DMB exhibited improved taste acuity over time (% change right/left side: -52.8 ± 38.5/-58.7 ± 69.2%) and salty taste perception (2.29 ± 1.25 vs. high dose: 2.17 ± 1.84 vs. placebo: 1.57 ± 1.51 points, < 0.05). They also had higher energy intake ( = 0.075) and covered better energy expenditure (107 ± 19%). The quality of life evaluated by symptom scales improved in patients receiving the standard dose of DMB (constipation, = 0.048). The levels of arachidonic (13.1 ± 1.8; 14.0 ± 2.8, 12.0 ± 2.0%; = 0.004) and docosahexaenoic (4.4 ± 1.7; 4.1 ± 1.0; 3.9 ± 1.6%; = 0.014) acids in erythrocytes increased over time after DMB intake. The standard dose of DMB increased fat-free mass vs. placebo (47.4 ± 9.3 vs. 44.1 ± 4.7 kg, = 0.007). Importantly, habitual patients with DMB did not experience any adverse events, and metabolic parameters remained stable and within normal ranges. In conclusion, habitual consumption of a standard 150 mg dose of DMB improves electrochemical food perception, nutritional status (energy intake, fat quantity and quality, fat-free mass), and quality of life in malnourished cancer patients receiving antineoplastic treatment. Additionally, DMB consumption appears to be safe, with no changes in major biochemical parameters associated with health status. Clinical trial registered (NCT05486260).
Topics: Humans; Male; Female; Pilot Projects; Neoplasms; Middle Aged; Malnutrition; Dietary Supplements; Quality of Life; Aged; Nutritional Status; Treatment Outcome; Taste Perception; Adult
PubMed: 38931260
DOI: 10.3390/nu16121905 -
Journal of Clinical Medicine Jun 2024: The introduction of biological drugs in the management of chronic rhinosinusitis with nasal polyps (CRSwNP) is allowing new and increasingly promising therapeutic...
: The introduction of biological drugs in the management of chronic rhinosinusitis with nasal polyps (CRSwNP) is allowing new and increasingly promising therapeutic options. This manuscript aims to provide a multicenter trial in a real-life setting on Mepolizumab treatment for severe uncontrolled CRSwNP with or without comorbid asthma. : A retrospective data analysis was jointly conducted at the Otolaryngology-Head and Neck Surgery departments of La Sapienza University and San Camillo Forlanini Hospital in Rome. Both institutions participated by sharing clinical information on patients with CRSwNP treated with Mepolizumab. Patients were evaluated before starting Mepolizumab, at six months and at twelve months from the first drug administration. During follow-up visits, patients underwent endoscopic evaluation, quality of life assessment, nasal symptoms assessment, and blood tests to monitor mainly neutrophils, basophils, eosinophils, and IgG, IgA, and IgE assay. : Twenty patients affected by CRSwNP and treated with Mepolizumab were enrolled (12 females and 8 males with a mean age of 63.7 years). Sixteen patients (80%) had concomitant asthma. During follow-up, a gradual improvement in nasal polyp score, quality of life and nasal symptoms, assessed by SNOT-22 and VAS and loss of smell measured by olfactory VAS, was found. Regarding blood tests, eosinophils decreased gradually, while other blood parameters showed no statistically significant changes. : Mepolizumab has been shown to be effective in the therapeutic management of patients with CRSwNP. Further studies are needed to support our findings and better understand the underlying immune pathways to predict patients' response to biological treatment in CRSwNP.
PubMed: 38930104
DOI: 10.3390/jcm13123575 -
Journal of Clinical Medicine Jun 2024SARS-CoV-2 continually mutates, with five identified variants. Many neurological manifestations were observed during the COVID-19 pandemic, with differences between...
SARS-CoV-2 continually mutates, with five identified variants. Many neurological manifestations were observed during the COVID-19 pandemic, with differences between virus variants. The aim of this study is to assess the frequency and characteristics of neurological manifestations during COVID-19 in hospitalized patients over three waves in Poland with comparison and analysis correlation with the course of infection. This retrospective single-center study included 600 consecutive adults with confirmed COVID-19, hospitalized during 3 waves (pre-Delta, Delta and Omicron) in Poland. Demographic and clinical information and neurological manifestations were collected and compared across three periods. The median age of the study group was 68, lower during the Delta wave. In the Omicron period, the disease severity at admission and inflammatory markers concentration were the lowest. Neurological manifestations were observed in 49%. The most common were altered mentation, headache, myalgia, mood disorder, ischemic stroke and encephalopathy. Smell and taste disturbances (STDs) were less frequent in the Omicron period. Neurological complications were predominant in the pre-Delta and Omicron periods. Ischemic stroke was observed more often in pre-Delta period. Altered mentation was related to higher severity at admission, worse lab test results, higher admission to ICU and mortality, while headache reduced mortality. Pre-existing dementia was related to higher mortality. Neurological manifestations of COVID-19 are frequent, with a lower rate of STDs in the Omicron period and more often cerebrovascular diseases in the pre-Delta period. Headache improves the course of COVID-19, while altered mentation, stroke and neurological comorbidities increase severity and mortality.
PubMed: 38930003
DOI: 10.3390/jcm13123477