South Carolina is home to millions of residents and is renowned for its subtropical climate and beautiful beaches. Nevertheless, the state\'s healthcare system faces significant challenges, with a ranking in the bottom 50% of all states, a statistic that warrants serious attention. One of the most pressing healthcare issues in South Carolina is accessibility. The state currently has a higher percentage of its population that is medically uninsured compared to the national average in the United States (18% vs 14%, respectively). Consequently, a lower proportion of South Carolinians enjoy access to healthcare services when compared to residents of other states. Unfortunately, recent efforts to expand coverage for more of its residents via the Medicaid expansion by the Affordable Care Act (ACA) have been opposed by legislation. Lack of accessible healthcare is a significant issue in this state and the state legislature should increase access, especially to low-income vulnerable populations.

A 63-year-old male patient recovering from dengue came to our department for contrast-enhanced computed tomography (CECT) for the evaluation of abdominal pain. An ultrasound performed in the periphery diagnosed him with a rectus abscess. The CECT evaluation done in our department clarified that the collection in his rectus sheath was hemorrhagic and not infective, as previously thought. The patient was managed conservatively and recovered without complications. Like most infectious diseases, dengue is a disease of tropical countries. System-wise data collection processes are inadequate in many developing countries, which means complications and adverse effects of common diseases are not adequately captured. Furthermore, resource limitations restrict the availability of more expensive diagnostic tests to central locations. Peripherally located regions with lower purchasing capacity have greater access to relatively inexpensive tests. This causes deficits in the management of some common disease entities, like dengue. Considering these issues, it is important to optimize healthcare testing for low-resource settings. This can only be achieved with adequate sensitization of healthcare providers in diagnosis and management.

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Enterococcus faecium is a Gram-positive flora bacterium home to the gastrointestinal tracts of humans. A true ubiquitous pathogen and a member of the intestinal microbiome, formerly known as group D streptococci, this pathogen has been around for over 10 centuries. Enterococcus faecium thrives in the presence of stool and sewage. The ability to cause endocarditis and urinary tract infections (UTIs) has led to morbidity and mortality in the adult population. We report a case of an elderly woman who presented with multiple falls to our trauma bay area. She was initially managed as trauma during multiple visits with superficial scalp lacerations. However, with multiple falls, she was subsequently transferred to medicine to rule out cardiogenic versus neurogenic syncope. She was admitted to the telemetry unit, and a cardiologist was consulted. Orthostatic vitals were negative, and she had no fever or leukocytosis. Abdominal computed tomography (CT) done as part of the standard trauma workup revealed an interesting finding of multiple bladder diverticula.

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

UNASSIGNED: In India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India.
UNASSIGNED: A total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet.
UNASSIGNED: ML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http://das.southeastasia.cloudapp.azure.com/predict/.
UNASSIGNED: ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected.

Stress reduction through contact with nature is well established, but far less is known about the contribution of contact parameters - duration, frequency, and nature quality. This study describes the relationship between duration of a nature experience (NE), and changes in two physiological biomarkers of stress - salivary cortisol and alpha-amylase. It is the first study to employ long-term, repeated-measure assessment and the first evaluation wherein study participants are free to choose the time of day, duration, and the place of a NE in response to personal preference and changing daily schedules. During an 8-week study period, 36 urban dwellers were asked to have a NE, defined as spending time in an outdoor place that brings a sense of contact with nature, at least three times a week for a duration of 10 min or more. Their goal was compliance within the context of unpredictable opportunity for taking a nature pill. Participants provided saliva samples before and after a NE at four points over the study period. Before-NE samples established the diurnal trajectory of each stress indicator and these were in line with published outcomes of more closely controlled experiments. For salivary cortisol, an NE produced a 21.3%/hour drop beyond that of the hormone\'s 11.7% diurnal drop. The efficiency of a nature pill per time expended was greatest between 20 and 30 min, after which benefits continued to accrue, but at a reduced rate. For salivary alpha-amylase, there was a 28.1%/h drop after adjusting for its diurnal rise of 3.5%/h, but only for participants that were least active sitting or sitting with some walking. Activity type did not influence cortisol response. The methods for this adaptive management study of nature-based restoration break new ground in addressing some complexities of measuring an effective nature dose in the context of normal daily life, while bypassing the limitations of a clinical pharmacology dose-response study. The results provide a validated starting point for healthcare practitioners prescribing a nature pill to those in their care. This line of inquiry is timely in light of expanding urbanization and rising healthcare costs.

A robust and numerically-efficient method based on two moving average filters, followed by a dynamic event-related threshold, has been developed to detect P and T waves in electrocardiogram (ECG) signals as a proof-of-concept. Detection of P and T waves is affected by the quality and abnormalities in ECG recordings; the proposed method can detect P and T waves simultaneously through a unique algorithm despite these challenges. The algorithm was tested on arrhythmic ECG signals extracted from the MIT-BIH arrhythmia database with 21,702 beats. These signals typically suffer from: (1) non-stationary effects; (2) low signal-to-noise ratio; (3) premature atrial complexes; (4) premature ventricular complexes; (5) left bundle branch blocks; and (6) right bundle branch blocks. Interestingly, our algorithm obtained a sensitivity of 98.05% and a positive predictivity of 97.11% for P waves, and a sensitivity of 99.86% and a positive predictivity of 99.65% for T waves. These results, combined with the simplicity of the method, demonstrate that an efficient and simple algorithm can suit portable, wearable, and battery-operated ECG devices.

A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non-stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave-one-out cross-validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F 1 scores of 86.0%, 87.2%, and 79.1%, respectively.

There are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG). We investigate the PPG signal and its derivatives for heat stress assessment using Welch (non-parametric) and autoregressive (parametric) spectral estimation methods. The preliminary results of this study indicate that applying the first and second derivatives to PPG waveforms is useful for determining heat stress level using 20-s recordings. Interestingly, Welch\'s and Yule-Walker\'s methods in agreement that the second derivative is an improved detector for heat stress. In fact, both spectral estimation methods showed a clear separation in the frequency domain between measurements before and after simulated heat-stress induction when the second derivative is applied. Moreover, the results demonstrate superior performance of the Welch\'s method over the Yule-Walker\'s method in separating before and after the three simulated heat-stress inductions.