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PloS One 2024More than a year after recovering from COVID-19, a large proportion of individuals, many of whom work in the healthcare sector, still report olfactory dysfunctions....
BACKGROUND
More than a year after recovering from COVID-19, a large proportion of individuals, many of whom work in the healthcare sector, still report olfactory dysfunctions. However, olfactory dysfunction was common already before the COVID-19 pandemic, making it necessary to also consider the existing baseline prevalence of olfactory dysfunction. To establish the adjusted prevalence of COVID-19 related olfactory dysfunction, we assessed smell function in healthcare workers who had contracted COVID-19 during the first wave of the pandemic using psychophysical testing.
METHODS
Participants were continuously tested for SARS-CoV-2 IgG antibodies since the beginning of the pandemic. To assess the baseline rate of olfactory dysfunction in the population and to control for the possibility of skewed recruitment of individuals with prior olfactory dysfunction, consistent SARS-CoV-2 IgG naïve individuals were tested as a control group.
RESULTS
Fifteen months after contracting COVID-19, 37% of healthcare workers demonstrated a quantitative reduction in their sense of smell, compared to only 20% of the individuals in the control group. Fifty-one percent of COVID-19-recovered individuals reported qualitative symptoms, compared to only 5% in the control group. In a follow-up study 2.6 years after COVID-19 diagnosis, 24% of all tested recovered individuals still experienced parosmia.
CONCLUSIONS
In summary, 65% of healthcare workers experienced parosmia/hyposmia 15 months after contracting COVID-19. When compared to a control group, the prevalence of olfactory dysfunction in the population increased by 41 percentage points. Parosmia symptoms were still lingering two-and-a half years later in 24% of SARS-CoV-2 infected individuals. Given the amount of time between infection and testing, it is possible that the olfactory problems may not be fully reversible in a plurality of individuals.
Topics: Humans; COVID-19; Health Personnel; Male; Female; Olfaction Disorders; Adult; Prevalence; Case-Control Studies; Middle Aged; SARS-CoV-2; Smell
PubMed: 38950019
DOI: 10.1371/journal.pone.0306290 -
Rhinology Jul 2024Chronic rhinosinusitis with nasal polyps (CRSwNP) frequently leads to olfactory dysfunction. This study aimed to assess the impact of dupilumab on CRSwNP patients,...
BACKGROUND
Chronic rhinosinusitis with nasal polyps (CRSwNP) frequently leads to olfactory dysfunction. This study aimed to assess the impact of dupilumab on CRSwNP patients, focusing on olfactory outcomes and potential correlations with other clinical factors.
METHODS
CRSwNP patients eligible for dupilumab therapy received subcutaneous Dupixent® injections every two weeks (300mg/2ml dupilumab). The 12-item Sniffin' Sticks Test (SST-12), fractional exhaled nitric oxide (FeNO) and Nasal Polyp Score (NPS) were assessed at baseline and after one, three, and six months. Patients also completed the Sino-Nasal Outcome Test (SNOT-22) weekly.
RESULTS
26 CRSwNP patients were included. After one month, dupilumab led to substantial reductions in FeNO, SNOT scores, andNPS, whereas SST-12 scores improved significantly only after three months. A shift toward normosmia occurred, with 81% achieving normosmia after six months, and a drop in anosmia prevalence to 9.5%. Significant negative correlations between olfaction (SST-12) and polyp severity (NPS) at baseline and after six months were found, while no significant correlations were observed between SST-12 and FeNO or SNOT scores. Age did not correlate with olfaction.
CONCLUSIONS
Dupilumab demonstrated efficacy in restoring olfaction in CRSwNP patients. Reaching normosmia in over 80% ofpatients after six months of treatment underscores the drug's effectiveness in managing this challenging symptom.
PubMed: 38949841
DOI: 10.4193/Rhin23.476 -
International Forum of Allergy &... Jun 2024This is the first systematic review and meta-analysis to investigate the effectiveness of the nasal airflow-inducing maneuver (NAIM) in olfactory rehabilitation for...
INTRODUCTION
This is the first systematic review and meta-analysis to investigate the effectiveness of the nasal airflow-inducing maneuver (NAIM) in olfactory rehabilitation for total laryngectomy (TL) patients.
METHODS
We conducted a systematic literature search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The inclusion criteria required that patients must have undergone a TL with subsequent NAIM training for at least 2 weeks and olfactory evaluation. The impact of NAIM on olfactory outcomes compared to that at baseline was measured. Olfactory measures included the Sniffin' Sticks Test, Smell Disk Test, Scandinavian Odor Identification Test, and Quick Odor Detection Test. The primary outcome measures were the proportion of patients with normosmia at baseline and after intervention.
RESULTS
Seven studies from 2000 to 2023 comprising a total of 290 TL patients met the inclusion criteria. The meta-analysis revealed that prior to intervention, the pooled proportion of patients with normosmia was 0.16 (95% confidence interval [CI]: 0.09‒0.27, p = 0.01). After intervention, the same proportion increased to 0.55 (95% CI: 0.45‒0.68, p = 0.001). Among the included patients, 88.3% were initially anosmic or hyposmic, which was reduced to 48.9% after NAIM practice, with 51.1% achieving normosmia. The percent improvement was not found to be significantly associated with the timing of intervention post-TL (p = 0.18).
CONCLUSIONS
NAIM increased the proportion of patients who achieved normosmia in TL patients. NAIM stands out as a safe, easily teachable maneuver with promising results. Further efforts are warranted to provide specific recommendations and guidelines for the use of NAIM in clinical practice.
PubMed: 38946145
DOI: 10.1002/alr.23391 -
Journal of Nuclear Cardiology :... Jun 2024
PubMed: 38945427
DOI: 10.1016/j.nuclcard.2024.101904 -
Mymensingh Medical Journal : MMJ Jul 2024Objective of the study was the effect of Covid-19 infection on pregnancy and neonatal outcomes. This prospective cohort study was conducted in Combined Military Hospital... (Comparative Study)
Comparative Study
Objective of the study was the effect of Covid-19 infection on pregnancy and neonatal outcomes. This prospective cohort study was conducted in Combined Military Hospital (CMH) Bogura, Obstetrics and Gynaecology department from June 2020 to October 2020. We have collected and analyzed data of 29 pregnant ladies positive for Covid-19. Control group was Covid-19 negative pregnant patients. Nasopharyngeal swab was taken for real time polymerase chain reaction for detection of Covid-19. We observed symptoms, compared any complication in mother and fetus, mode of termination, and duration of hospital stay. Only six patients were asymptomatic (10.3%). Fifteen (25.9%) had fever, six (6) had weakness (10.3%), 5(8.6%) had sore throat, 3(5.2%) had nausea and 5(8.6%) presented with loss of smell. Among twenty-nine patients, 5(8.6%) delivered normally, 24(41.4%) were delivered through caesarean section which was significantly higher than control group (p value <0.001). No mother became critical or expired, neonatal death was also absent. Mean duration of hospital stay was 14.13±6.192 days in case and 5.18±4.99 in control which was significantly (p value <0.001) higher. Breast feeding was significantly higher in control group (p value <0.001). This study shows feto-maternal outcome of Covid-19 pregnancy is almost same as those of normal pregnancy.
Topics: Humans; Pregnancy; Female; COVID-19; Pregnancy Complications, Infectious; Adult; Prospective Studies; Pregnancy Outcome; Bangladesh; Infant, Newborn; SARS-CoV-2; Length of Stay; Cesarean Section; Young Adult
PubMed: 38944726
DOI: No ID Found -
Journal of the American Geriatrics... Jun 2024Sensory disability in older adults is associated with increased rates of depressive symptoms and loneliness. Here, we examined the impact of hearing, vision, and...
BACKGROUND
Sensory disability in older adults is associated with increased rates of depressive symptoms and loneliness. Here, we examined the impact of hearing, vision, and olfaction disability on mental health outcomes in older US adults.
METHODS
We studied respondents from the first three rounds (2005/6, 2010/11, and 2015/16) of the National Social Life, Health and Aging Project, a nationally representative, longitudinal study of older US adults. Sensory function was assessed by structured interviewer ratings (hearing and vision) and objective assessment (olfaction). Cox proportional hazards models and one degree of freedom tests for trend were utilized to analyze the relationships between sensory disability and self-rated mental health, frequent depressive symptoms, frequent perceived stress, frequent anxiety symptoms, and frequent loneliness symptoms over time, adjusting for demographics, health behaviors, comorbidities, and cognitive function.
RESULTS
We analyzed data from 3940 respondents over 10 years of follow-up. A greater number of sensory disabilities was associated with greater hazard of low self-rated mental health, frequent depressive symptoms, frequent perceived stress, and frequent loneliness symptoms over time (p ≤ 0.003, all). After adjusting for covariates, older adults with a greater number of sensory disabilities had greater hazard of low self-rated mental health (HR = 1.22, CI = [1.08, 1.38], p = 0.002) and loneliness symptoms (HR = 1.13, CI = [1.05, 1.22], p = 0.003) over time in our tests for trend. In our Cox proportional hazards model, older adults with vision disability had greater hazard of low self-rated mental health (HR = 1.34, 95% CI = [1.05, 1.72], p = 0.02) and loneliness symptoms (HR = 1.21, CI = [1.04, 1.41], p = 0.01).
CONCLUSIONS
Older US adults with greater numbers of sensory disabilities face worse subsequent mental health. Future longitudinal studies dissecting the relationship of all five classical senses will be helpful in further understanding how improving sensory function might improve mental health in older adults.
PubMed: 38944677
DOI: 10.1111/jgs.19056 -
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 -
European Archives of... Jun 2024In Japan, two types of tests for diagnosing olfactory disorders, T and T (T&T) olfactometry and intravenous olfactory tests, are covered by insurance and performed on...
PURPOSE
In Japan, two types of tests for diagnosing olfactory disorders, T and T (T&T) olfactometry and intravenous olfactory tests, are covered by insurance and performed on patients with olfactory disorders. This study examined the validity of these olfactory tests and whether psychophysical or morphological tests are more helpful in evaluating olfactory disorders.
METHODS
We evaluated patients who visited our department and underwent two types of olfaction tests and sinus computed tomography (CT). Data regarding the age, sex, peripheral blood eosinophil percentage, presence of bronchial asthma, diagnoses, olfactory symptom score, results of the two olfactory tests, and CT findings in eligible patients were extracted from medical records and retrospectively reviewed.
RESULTS
One hundred and sixty-three patients underwent all tests during the study period. The results of the T&T olfactometry and intravenous olfactory tests were significantly correlated. However, only the results of T&T olfactometry and olfactory cleft opacification on CT were statistically significant predictors of the olfactory symptom scores.
CONCLUSION
T&T olfactometry and CT evaluations of olfactory cleft opacification helped evaluate olfactory dysfunction. It is important to note that intravenous olfactory tests are best performed with careful control and not blindly to assess olfactory disorders.
PubMed: 38940928
DOI: 10.1007/s00405-024-08803-w