Vital Signs Research Articles

Assessment of Wine Quality, Traceability and Detection of Grapes Wine, Detection of Harmful Substances in Alcohol and Liquor Composition Analysis

Abstract: Wine production is the result of the interaction between various strains and grapes, and its good quality is also affected by many factors. Aureobasidium, Cladosporium, Candida, Filobasidium, Hanseniaspora, Hannaella, Saccharomyces, Wickerhamomyce, Alternaria, Starmerella, Acetobacter, Papiliotrema, Bradyrhizobium, Leuconostoclia, Gluconobacter, Comamonas, and Massilia, are significantly correlated with changes of physiological properties and volatile compounds. Phenolic compounds, shortened as phenolics, are a vital parameter to the quality of wine, and wine phenolics include two main families: non-flavonoids, which consist of hydroxybenzoic acids (HBAs), hydroxycinnamic acids (HCAs), and stilbenes, and flavonoids, comprising flavonols, flavan-3-ols, and anthocyanins. Wine quality is determined by either sensory tests or physicochemical tests, and the latter analyse the wine’s chemical parameters such as sugar, pH, and alcohol level. The most important constituents found in wine are Terpenes; Aldehydes, Pyrazines, Esters, Ketones and diketones, Mercaptans, and Lactones. In wine quality analysis, the most chief variables are volatile acidity, alcohol, sulphates, citric acid, density, total sulfur dioxide, chlorides, pH, fixed acidity, free sulfur dioxide, and residual sugar. Some classifiers utilized for wine quality prediction in machine learning are: k-Nearest Neighbor (KNN), Random Forest, Decision Tree, Support Vector Machines, Linear Regression, Stochastic Gradient Descent, Artificial Neural Networks (ANN), and Naive Bayes. This article is aimed to review wine quality parameters, detection and traceability of wine, and detection of harmful substances in alcohol and liquor composition analysis.

Tumor formation at ileocecal junction associated with interleukin-1β upregulation in aryl hydrocarbon receptor-deficient mouse.

Aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor. We previously reported spontaneous ileocecal tumorigenesis in AhR-deficient mice after the age of 10 weeks, which originated in the confined area between ileum and cecum. This study aimed to investigate the underlying mechanism that causes tumor development at this particular location. To observe mucosal architecture in detail, tissues of ileocecal region were stained with methylene blue. Gene expression profile in the ileocecal tissue was compared with cecum. Immunohistochemical analysis was performed with ileocecal tissues using antibodies against ileum-specific Reg3β or cecum-specific Pitx2. In AhR+/+ mice and AhR+/- mice, that do not develop lesions, methylene blue staining revealed the gradually changing shape and arrangement of villi from ileum to cecum. It was also observed in AhR-deficient mice before developing lesions. Microarray-based analysis revealed abundant antimicrobial genes, such as Reg3, in the ileocecal tissue while FGFR2 and Pitx2 were specific to cecum. Immunohistochemical analysis of AhR-deficient mice indicated that lesions originated from the ileocecal junction, a boundary area between different epithelial types. Site-specific gene expression analysis revealed higher expression of IL-1β at the ileocecal junction compared with the ileum or cecum of 9-11-week-old AhR-deficient mice. These findings indicate that AhR plays a vital function in the ileocecal junction. Regulating AhR activity can potentially manage the stability of ileocecal tissue possessing cancer-prone characteristics. This investigation contributes to understanding homeostasis in different epithelial transitional tissues, frequently associated with pathological states.

안마의자를 활용하는 마사지가 성인 남녀의 자율신경균형도에 미치는 즉각적 영향: 전향적 관찰연구

Objectives The aim of this study was to examine the immediate impact of a massage chair intervention on homeostasis and the balance of the autonomic nervous system in individuals aged 20 to 64. Methods This study was a single-center, prospective, single-arm, investigator-initiated observational study. Non-underweight adults without significant health concerns underwent a 40-minute massage chair intervention. Measurements of heart rate variability, functional near-infrared spectroscopy, and vital signs were conducted before and after the intervention. Additionally, participants completed the Mibyeong questionnaire and a satisfaction survey. Results A 40-minute massage chair session led to a notable decrease in average heart rate and low frequency to high frequency (LF/HF) ratio, coupled with an increase in root mean square of the successive differences (RMSSD) and standard deviation of NN interval (SDNN). No adverse reactions were noted. Subjects with elevated sympathetic nerve activity exhibited a reduction in LF/HF ratio, whereas those with heightened parasympathetic activity demonstrated an increase in LF/HF ratio post-intervention. Conclusions The findings indicate that the massage chair intervention has the potential to activate the parasympathetic nervous system, promote blood pressure homeostasis and autonomic nervous system balance. However, further research over a more extended period is crucial to validate these conclusions.

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

Sepsis Prediction

Abstract: Sepsis manifests as a life-threatening condition wherein the body's response to infection triggers organ dysfunction. Typically, this immune response inadvertently damages the body's tissues while combating the infection. Within the overall range of sepsis, septic shock represents a critical subset characterized by notable circulatory, cellular, and metabolic abnormalities, resulting in a increased mortality rate than standard sepsis. Noteworthy is the consensus from various studies highlighting the crucial role of early intervention in enhancing the survival chances of sepsis patients. Given the urgency of timely recognition and treatment, there is a heightened focus on sepsis prediction. Traditional scoring systems such as APACHE, SAPS, and SOFA provide insights into disease severity and prognosis but lack the capacity for early sepsis detection. This study uses ML techniques and analyzes extensive patient data, which includes vital signs and medical history, to identify important indicators and prognostic patterns that find the occurrence of sepsis. By applying advanced algorithms like neural networks and ensemble methods, the model shows impressive accuracy in predicting the progression of sepsis. In addition, detailed analysis of trait significance helps to understand key prognostic markers, allowing clinicians to strategically prioritize interventions. Successful integration of this prognostic tool into the clinical setting offers a promising opportunity for preventive management of sepsis, which can reduce mortality and improve patient outcomes. We built a (LSTM) network to distinguish the internal association between different. indicators in clinical data. Analysis includes use of electronic health records, multicenter collaboration, and challenge development. Our goal is to support the research of accurate and timely sepsis predictors, ultimately improving patient outcomes.

Comparative Evaluation of Mortality Predictors in Trauma Patients: A Prospective Single-center Observational Study Assessing Injury Severity Score Revised Trauma Score Trauma and Injury Severity Score and Acute Physiology and Chronic Health Evaluation II Scores.

This prospective cohort study aimed to compare the predictive accuracy of outcome (survival/death) among trauma patients using various prognostic scores. Over 3 months, 240 trauma patients in a tertiary care hospital were assessed for demographic details, trauma characteristics, vital signs, Glasgow coma scale, arterial blood gas values, and lab markers. Injury severity score (ISS), revised trauma score (RTS), trauma and injury severity score (TRISS), and acute physiology and chronic health evaluation II (APACHE II) were applied at admission, 24 hours, and 48 hours post-admission. Road traffic accidents (55.83%) were the primary cause of trauma, followed by falls (33.75%) and violence (10.41%). The all-cause mortality rate was 23.33%, with 34.16% requiring ICU admission. Head injuries (65.83%) were both the most frequent injury site and cause of mortality. Analysis indicated that APACHE II outperformed other scores in predicting outcomes, with ISS following closely. The study concludes that trauma severity correlates with ICU admission and mortality, emphasizing APACHE II as a superior predictor, particularly for traumatic brain injuries leading to ICU admission and mortality. This study contributes to the existing body of knowledge by addressing the gap in comparing prognostic abilities among scoring systems for trauma patients. The unexpected superiority of APACHE II suggests its potential as a valuable tool in predicting outcomes in this specific patient population. Gupta J, Kshirsagar S, Naik S, Pande A. Comparative Evaluation of Mortality Predictors in Trauma Patients: A Prospective Single-center Observational Study Assessing Injury Severity Score Revised Trauma Score Trauma and Injury Severity Score and Acute Physiology and Chronic Health Evaluation II Scores. Indian J Crit Care Med 2024;28(5):475-482.

Health monitoring system using ECG and PPG techniques

The demand for portable health monitoring systems has grown rapidly in recent years due to the increasing prevalence of chronic diseases and the need for continuous health monitoring. This article introduces the development and implementation of a portable health monitoring system using photo plethysmography (PPG) and electrocardiogram (ECG) technologies. The system integrates the Node MCU ESP8266 microcontroller, AD8232 ECG sensor, and MAX30100 sensor to measure vital signs such as heart rate and blood oxygen saturation level. The proposed system provides a cost-effective and efficient solution for real-time health monitoring, enabling early detection of abnormalities and timely intervention. The feasibility and performance of the system were proven through experimental tests.

Real-time Continuous Glucose Monitoring in the Intensive Care Unit: The Fifth Vital Sign.

Real-time Continuous Glucose Monitoring in the Intensive Care Unit: The Fifth Vital Sign.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

PHARMACOLOGICAL EFFECTS OF MELATONIN AND ITS POTENTIAL APPLICATION IN THERAPY OF GOUT AND ASSOCIATED DISEASES

Introduction. The disturbance in biosynthetic activity of pinealocytes of the pineal gland and subsequent melatonin deficiency in the body are linked to the progression of certain chronic diseases. This has prompted research into correcting chrono-biological dysfunctions, particularly in conditions such as gout and its comorbidities. This study aims to analyze the potential use of melatonin in correcting metabolic disorders in gout and to justify its use as a pharmacotherapeutic agent for treating metabolic and cardiovascular diseases. Materials and methods. We conducted a comprehensive analysis of modern scientific literature from specialized domestic and foreign sources. Emphasis was placed on studies exploring the pharmacological and pharmacotherapeutic aspects of melatonin use in both experimental and clinical settings, particularly those conducted in the last 5-6 years. Results. The research data revealed three main aspects of the problem that must be addressed to understand the experimental, biological, pharmacological, and pharmacotherapeutic foundations for the long-term use of melatonin in clinical practice. The first part of the review highlighted the significant impact of melatonin homeostasis disruption on the pathogenesis of gout and its progression in comorbidity with other conditions such as hypertension, obesity, insulin resistance, metabolic syndrome, and type 2 diabetes. The role of chronobiological disorders and desynchronosis in the progression of these diseases was also established. Pharmacological studies demonstrated the pivotal role of melatonin, as an important factor in neuroendocrine regulation, in organizing the circadian rhythm of the body's vital functions under normal and pathological conditions. Melatonin was found to normalize the pathological processes underlying gout development and comorbid diseases, providing a scientific basis for its use in the treatment of gout, cardiovascular diseases, disturbed metabolic processes, and normalization of circadian rhythms in patients with these conditions. Conclusions. This paper presents data on the causes of gout development and progression, as well as some comorbid diseases, from the perspective of chronobiological disruptions in the body's functions. The review sections delve into the pharmacological and pharmacotherapeutic aspects of melatonin's therapeutic action. The findings confirm the importance of melatonin in effectively treating gout and other comorbid diseases and metabolic disorders.

Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest

Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG sensors are catching huge research attention in different applications for measurement and monitoring of physiological parameters. In this paper, we report a simple technique for remote monitoring of sleep disorder using a smart vest implemented with four FBG stress sensors located at different sides of the vest and free space optics (FSO) transmission system. The sleep disorder of the patient is monitored in real time through shifts in the original Bragg wavelengths of sensors by stress loading during random changes in patient’s sleeping postures. The reflected wavelength from a stress loaded sensor at a certain posture is transmitted over 0.5 km long FSO channel towards remote medical center, photodetected, and then can be processed in a PC to record the restlessness in a certain time interval in terms of total number of times sleeping postures are changed, total time spent at a certain posture etc. To correctly detect the stress loaded FBG sensor at the medical center, various parameters of FBG sensors and demultiplexer are carefully adjusted to minimize the power leakages from unloaded sensors that may result into errors in the detection. Maximum dynamic range around 45 dB has been achieved ensuring accurate detection. This study not only provides a cost-efficient and non-intrusive solution for monitoring the sleep disorder of patients but also can be used for real-time monitoring of various other ailments, such as lung, brain, and cardiac diseases in future.

Review on Pregnancy Progress Monitoring

This project outlines the creation of an innovative pregnancy progress monitoring system leveraging cutting-edge sensor technology and artificial intelligence (AI) models. This project presents an integrated system designed to revolutionize the monitoring of pregnancy progression through a sophisticated fusion of sensor technology and artificial intelligence (AI). The collaborative functionality of DS18B20 temperature sensors, heart beat sensor, and GSM modules establishes a seamless data collection and transmission framework. These sensors continuously capture vital parameters including body temperature, heart rate, and blood oxygen saturation, while the GSM module ensures secure and real-time transmission of this data to designated servers or caregivers’ devices. Leveraging AI-driven algorithms, the system interprets these multifaceted datasets, employing pattern recognition and anomaly detection to discern potential health irregularities. The intuitive user interface grants caregivers comprehensive access to visualized data, facilitating proactive monitoring and swift response through customizable alerts for critical health indicators. This innovative solution undergoes iterative development and validation, seeking to redefine prenatal care by enabling early identification of deviations and fostering informed decision-making among medical practitioners and expectant parents. By amalgamating sensor technology with AI capabilities, this system aspires to enhance the safety and well-being of both mother and fetus, marking a paradigm shift in prenatal health monitoring and intervention strategies.

Analysis of clinical outcomes in older individuals who received pharmaceutical care and posthospital discharge follow-up.

Previous evidence underscores the need to assess potential clinical outcomes resulting from pharmaceutical care interventions and to monitor patient's progress to evaluate their clinical evolution, which is crucial for bolstering the relevance of implementing pharmaceutical care in healthcare services. To conduct an in-depth analysis of pharmaceutical care practices in a geriatric ward and monitor the clinical outcomes of older people served. This interventionist study was conducted in the geriatrics ward of a Brazilian university hospital. The research intervention occurred between January and May 2022, with a follow-up conducted for up to 90 days after patients' hospital discharge. Older patients in the geriatrics ward received pharmaceutical care, including medication reconciliation, medication review,and pharmacotherapeutic follow-up, aimed at identifying and resolving drug-related problems (DRPs). The clinical relevance of DRPs and pharmaceutical recommendations was evaluated. Additionally, analyses were conducted on mortality and rehospitalization outcomes in older patients at 30, 60,and 90 days following initial hospital discharge. Of the patients evaluated, a significant 88.3% exhibited at least one DRP (with an average of 2.6 ± 1.9 DRPs per patient), with the majority classified as need/indication problems (38.9%). The acceptance rate of pharmaceutical recommendations was 80.9%, with the majority categorized as very significant relevance (60.4%). DRPs were predominantly of serious clinical relevance (50.9%). In patients whose clinical indicators could be monitored, 95.5% showed some clinical response (in vital signs, laboratory testsand/or clinical status evolution) potentially related to resolved DRPs. Association analysis revealed that a higher number of medications in use before hospitalization correlated with a greater identification of DRPs during hospitalization (p = 0.03). At hospital discharge, 23.6% of patients were no longer using polypharmacy. In total, 16 patients (26.7%) died during the study period. Among patients who did not die during hospitalization (n = 54), 20 patients (37%) experienced rehospitalizations within 90 days following discharge. This study facilitated the consolidation of pharmaceutical care implementation in a geriatric ward. We conducted identification, evaluation,and proposed evidence-based solutions, as well as monitored cases for outcome analysis. It is anticipated that this methodology will inspire future research and the implementation of pharmaceutical care-related services.

Quantitative blood loss measurement methods for early detection of primary postpartum haemorrhage following vaginal birth: A scoping review.

To map the commonly used quantitative blood loss measurement methods in clinical practice and provide a solid foundation for future studies. This study adhered to the JBI methodology for scoping reviews and preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews. We conducted a literature search using five databases to retrieve articles published between January 2012 and September 2022. The search was repeated on 29 February 2024. Data extraction and verification were carried out by two independent researchers using a self-designed data extraction form. Ultimately, 26 studies published between 2012 and 2024 were considered eligible for inclusion. Six categories of methods were identified from the 26 articles. Among the included studies, only two involved randomized controlled trials, with the majority being observational studies. The World Health Organization (2012) version of the postpartum haemorrhage diagnostic criteria was predominantly used in most studies. Gravimetric and volumetric methods emerged as the most commonly used methods for quantifying postpartum haemorrhages. The timing of blood collection was inconsistent among the included studies. Only 12 studies mentioned measures for the management of amniotic fluid. This scoping review supports the replacement of the visual estimation of blood loss with quantitative assessment methods. Supporting a specific assessment approach is not feasible due to the variability of the study. Future research should focus on establishing the best practices for specific quantitative methods to standardize the management of postpartum haemorrhage and reduce the incidence of postpartum haemorrhage-related adverse outcomes. Healthcare professionals need to acknowledge the low accuracy of visual estimation methods and implement quantitative methods to assess postpartum blood loss. Given the limitations inherent in each assessment method, quantification of blood loss should be combined with assessment of maternal vital signs, physiologic indicators and other factors.

Development and validation of the tic score for early detection of traumatic coagulopathy upon hospital admission: a cohort study

BackgroundCritically injured patients need rapid and appropriate hemostatic treatment, which requires prompt identification of trauma-induced coagulopathy (TIC) upon hospital admission. We developed and validated the performance of a clinical score based on prehospital resuscitation parameters and vital signs at hospital admission for early diagnosis of TIC.MethodsThe score was derived from a level-1 trauma center registry (training set). It was then validated on data from two other level-1 trauma centers: first on a trauma registry (retrospective validation set), and then on a prospective cohort (prospective validation set). TIC was defined as a PTratio > 1.2 at hospital admission. Prehospital (vital signs and resuscitation care) and admission data (vital signs and laboratory parameters) were collected. We considered parameters independently associated with TIC in the score (binomial logistic regression). We estimated the score’s performance for the prediction of TIC.ResultsA total of 3489 patients were included, and among these a TIC was observed in 22% (95% CI 21–24%) of cases. Five criteria were identified and included in the TIC Score: Glasgow coma scale < 9, Shock Index > 0.9, hemoglobin < 11 g.dL−1, prehospital fluid volume > 1000 ml, and prehospital use of norepinephrine (yes/no). The score, ranging from 0 and 9 points, had good performance for the identification of TIC (AUC: 0.82, 95% CI: 0.81–0.84) without differences between the three sets used. A score value < 2 had a negative predictive value of 93% and was selected to rule-out TIC. Conversely, a score value ≥ 6 had a positive predictive value of 92% and was selected to indicate TIC.ConclusionThe TIC Score is quick and easy to calculate and can accurately identify patients with TIC upon hospital admission.

The effect of deep and awake extubation on emergence agitation after nasal surgery: a randomized controlled trial

BackgroundPost-anesthetic emergence agitation is common after general anesthesia and may cause adverse consequences, such as injury as well as respiratory and circulatory complications. Emergence agitation after general anesthesia occurs more frequently in nasal surgery than in other surgical procedures. This study aimed to assess the occurrence of emergence agitation in patients undergoing nasal surgery who were extubated under deep anesthesia or when fully awake.MethodsA total of 202 patients (18–60 years, American Society of Anesthesiologists classification: I–II) undergoing nasal surgery under general anesthesia were randomized 1:1 into two groups: a deep extubation group (group D) and an awake extubation group (group A). The primary outcome was the incidence of emergence agitation. The secondary outcomes included number of emergence agitations, sedation score, vital signs, and incidence of adverse events.ResultsThe incidence of emergence agitation was lower in group D than in group A (34.7% vs. 72.8%; p < 0.001). Compared to group A, patients in group D had lower Richmond Agitation-Sedation Scale scores, higher Ramsay sedation scores, fewer agitation episodes, and lower mean arterial pressure when extubated and 30 min after surgery, whereas these indicators did not differ 90 min after surgery. There was no difference in the incidence of adverse events between the two groups.ConclusionsExtubation under deep anesthesia can significantly reduce emergence agitation after nasal surgery under general anesthesia without increasing the incidence of adverse events.Trial registrationRegistered in Clinicaltrials.gov (NCT04844333) on 14/04/2021.

Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

An essential part of healthcare is disease prediction, which seeks to identify people who are at risk of getting certain diseases. Because of their superior capacity to sift through massive datasets in search of intricate patterns, machine learning algorithms have recently become useful instruments in the fight against illness prediction. The goal of this project is to make it easier for people to diagnose their own health problems using just their symptoms and precise vital signs. Due to excessive medical expenditures, many people put off taking care of their health, which can result in worsening symptoms or even death. Medical expenses can be overwhelming for people without health insurance. Using machine learning methods like ExtRa Trees, the suggested system provides a general illness forecast based on patients' symptoms. The algorithm provides a possible diagnosis based on the user's age, gender, and symptoms, suggesting that the user may be experiencing a certain illness. The system also suggests healthy eating and exercise routines to help lessen the impact of the condition, depending on how bad it is. Lastly, this article has shown a comparison examination of the suggested system using several algorithms including logistic regression, decision tree, and Naïve Bayes. The efficiency and accuracy of illness prediction are both enhanced by the suggested model.

A clinical evaluation and acceptability study of the innovative SurePulse VS wireless heart rate monitor across the neonatal journey

BackgroundThe SurePulse vital signs (VS) device is an innovative wireless heart rate monitor designed for neonatal patients. This study evaluates the application of SurePulse VS technology in clinical practice.MethodsData were collected about the quantitative metrics of the device itself when deployed on real infants and qualitative feedback from perinatal professionals and parents regarding their experiences using this novel technology.ResultsThis study recruited 101 infants and achieved target completion rates of 101 healthcare professional (HCP) and 51 parent questionnaires over the seven-month study period. The SurePulse device was deployed across a range of gestational ages (34–39 weeks) and birth weights (1.8–3.5 kg). Device deployment was performed across a range of clinical environments, with 51% of deployments at delivery and 47% within the neonatal unit. The data show clinically acceptable timings from device deployment to heart rate signal acquisition [median 20 s (IQR 15–76 s)]. HCP feedback rated SurePulse monitoring as “Always” or “Mostly” reliable in 80% of cases. Parental feedback reported that having the SurePulse device was reassuring, convenient and beneficial to them. These positive comments were reflected across device deployment in the delivery room and within the neonatal unit.ConclusionsThe study findings show that the SurePulse device has potential to be a significant advancement in the way neonatal patients are monitored in a variety of post-delivery circumstances. This study has demonstrated that the SurePulse device has utility throughout the neonatal journey, enabling accurate heart rate monitoring in a manner that promotes parent-infant contact and bonding.

Assessing the clinical utility of abdominal computed tomography in sepsis patients with unknown origin: A retrospective cohort study.

Early identification of the sources of infection in emergency department (ED) patients of sepsis remains challenging. Computed tomography (CT) has the potential to identify sources of infection. This retrospective study aimed to investigate the role of CT in identifying sources of infection in patients with sepsis without obvious infection foci in the ED. A retrospective chart review was conducted on patients with fever and sepsis visiting the ED of Linkou Chang Gung Memorial Hospital between July 1, 2020 and June 30, 2021. Data on patient demographics, vital signs, clinical symptoms, underlying medical conditions, laboratory results, administered interventions, length of hospital stay, and mortality outcomes were collected and analyzed. Of 218 patients included in the study, 139 (63.8%) had positive CT findings. The most common sources of infection detected by CT included liver abscesses, acute pyelonephritis, and cholangitis. Laboratory results showed that patients with positive CT findings had higher white blood cell and absolute neutrophil counts and lower hemoglobin levels. Positive blood culture results were more common in patients with positive CT findings. Additionally, the length of hospital stay was longer in the group with positive CT findings. Multivariate logistic regression analysis revealed that hemoglobin levels and positive blood culture results independently predicted positive CT findings in patients with fever or sepsis without an obvious source of infection. In patients with sepsis with an undetermined infection focus, those presenting with leukocytosis, anemia, and elevated absolute neutrophil counts tended to have positive findings on abdominal CT scans. These patients had high rates of bacteremia and longer lengths of stay. Abdominal CT remains a valuable diagnostic tool for identifying infection sources in carefully selected patients with sepsis of undetermined infection origins.

Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows.