Quantifying holistic capacity response and healthcare resilience in tackling COVID-19: Assessment of country capacity by MCDM.

The resilience of a country during the COVID-19 pandemic was determined based in whether it was holistically prepared and responsive. This resilience can only be identified through systematic data collection and analysis. Historical evidence-based response indicators have been proven to mitigate pandemics like COVID-19. However, most databases are outdated, requiring updating, derivation, and explicit interpretation to gain insight into the impact of COVID-19. Outdated databases do not show a country's true preparedness and response capacity, therefore, it undermines pandemic threat. This study uses up-to-date evidence-based pandemic indictors to run a cross-country comparative analysis of COVID-19 preparedness, response capacity, and healthcare resilience. PROMETHEE-a multicriteria decision making (MCDM) technique-is used to quantify the strengths (positive) and weaknesses (negative) of each country's COVID-19 responses, with full ranking (net) from best to least responsive. From 22 countries, South Korea obtained the highest net outranking value of 0.1945, indicating that it was the most resilient, while Mexico had the lowest (-0.1428). Although countries were underprepared, there was a robust response to the pandemic, especially in developing countries. This study demonstrates the performance and response capacity of 22 key countries to resist COVID-19, from which other countries can compare their statutory capacity ranking in order to learn/adopt the evidence-based responses of better performing countries to improve their resilience.

Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest.

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models-such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier-is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification.

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

Diffusion Tensor Imaging Along Perivascular Spaces (DTI-ALPS) to Assess Effects of Age, Sex, and Head Size on Interstitial Fluid Dynamics in Healthy Subjects.

Diffusion tensor imaging along perivascular spaces (DTI-ALPS) is a novel MRI method for assessing brain interstitial fluid dynamics, potentially indexing glymphatic function. Failed glymphatic clearance is implicated in Alzheimer's disease (AD) pathophysiology. We assessed the contribution of age and female sex (strong AD risk factors) to DTI-ALPS index in healthy subjects. We also for the first time assessed the effect of head size. In accord with prior studies, we show reduced DTI-ALPS index with aging, and in men compared to women. However, head size may be a major contributing factor to this counterintuitive sex difference.

Unveiling diagnostic prowess: a comparative study of exercise treadmill test and CT coronary in coronary artery disease detection in Pakistan (2021-2023).

Coronary Artery Disease (CAD) is a global health concern, with diagnostic modalities and risk factors that exhibit regional variations. This study, conducted at the Islamabad Diagnostic Center, Pakistan, aimed to provide a comprehensive assessment of CAD prevalence, severity, and associated risk factors, while also evaluating the diagnostic accuracy of Computed Tomography Coronary Test (CTT) and Exercise Treadmill Test (ETT) in a cohort of 2909 patients. Among the patients assessed via CT Coronary scans, CAD was universally observed, presenting with varying degrees of severity. Our findings indicated that 24.5% of patients had mild CAD, 28.6% exhibited mild to moderate CAD, 16.3% were diagnosed with moderate CAD, 18.4% demonstrated moderate to severe CAD, and 20.4% displayed severe CAD. This spectrum underscores the diverse nature of CAD within the study population. In addition to CTT, we conducted a detailed evaluation of ETT results in 49 patients. These results revealed that 55.1% of patients tested positive for ischemia during the exercise test, emphasizing the prevalence of cardiac stress and underlying CAD. Conversely, 32.7% of patients exhibited negative ETT results, indicating favorable cardiac tolerance during physical activity. A subset of patients yielded non-diagnostic or inconclusive results, necessitating further clinical assessment. Disease history analysis showed a dichotomy within the cohort, with 20.4% having a known medical history and 79.6% possessing an unknown disease history, highlighting the importance of comprehensive medical records in clinical practice. Hypertension, a critical cardiovascular risk factor, was identified in 87.8% of patients, underscoring its significance. Smoking history displayed notable variation, with 69.4% categorized as smokers, 14.3% as ex-smokers, and 10.2% as non-smokers. Lipid profile analysis indicated that 69.4% of patients had abnormal lipid levels. To assess the diagnostic accuracy of CTT and ETT, we calculated Positive Predictive Values (PPV) and Negative Predictive Values (NPV). CTT exhibited a PPV of approximately 5.99% and an NPV of approximately 4.40%, whereas ETT displayed a higher PPV of around 26.44% and a substantially higher NPV of about 49.24%. This study offers valuable insights into CAD prevalence, severity, and associated risk factors in a Pakistani cohort, emphasizing the importance of holistic risk assessment and tailored interventions in clinical practice. Our findings also highlight the diagnostic utility of ETT in CAD assessment.

Reduction in Constitutively Activated Auditory Brainstem Microglia in Aging and Alzheimer's Disease.

Background: Alzheimer's disease (AD) pathology is considered to begin in the brainstem, and cerebral microglia are known to play a critical role in AD pathogenesis, yet little is known about brainstem microglia in AD. Translocator protein (TSPO) PET, sensitive to activated microglia, shows high signal in dorsal brainstem in humans, but the precise location and clinical correlates of this signal are unknown. Objective: To define age and AD associations of brainstem TSPO PET signal in humans. Methods: We applied new probabilistic maps of brainstem nuclei to quantify PET-measured TSPO expression over the whole brain including brainstem in 71 subjects (43 controls scanned using 11C-PK11195; 20 controls and 8 AD subjects scanned using 11C-PBR28). We focused on inferior colliculi (IC) because of visually-obvious high signal in this region, and potential relevance to auditory dysfunction in AD. We also assessed bilateral cortex. Results: TSPO expression was normally high in IC and other brainstem regions. IC TSPO was decreased with aging (p = 0.001) and in AD subjects versus controls (p = 0.004). In cortex, TSPO expression was increased with aging (p = 0.030) and AD (p = 0.033). Conclusions: Decreased IC TSPO expression with aging and AD-an opposite pattern than in cortex-highlights underappreciated regional heterogeneity in microglia phenotype, and implicates IC in a biological explanation for strong links between hearing loss and AD. Unlike in cerebrum, where TSPO expression is considered pathological, activated microglia in IC and other brainstem nuclei may play a beneficial, homeostatic role. Additional study of brainstem microglia in aging and AD is needed.