Background A major development in noninvasive imaging modalities, computed tomographic enterography (CTE) has a number of benefits over conventional computed tomography (CT) and capsule endoscopy. Through the utilization of multidetector computed tomography (MDCT) technology, CTE expedites the assessment of small bowel diseases, especially in those segments that are not accessible through traditional endoscopy. This study\'s main goal is to thoroughly evaluate CTE\'s diagnostic accuracy for a range of small intestinal conditions. Methodology In this investigation, which is a prospective observational study, 40 patients, 25 men and 15 women, with suspected small intestinal disorders and ages ranging from 10 to 70 underwent CTE. To evaluate diagnosis accuracy, a combination of clinical symptoms, imaging data, and histopathological/ultrasonography findings were evaluated. Throughout the research procedure, ethical issues and statistical analysis were incorporated to guarantee validity and adherence to ethical norms. Results The most frequent findings on CTE were bowel thickening and mucosal hyperenhancement, which were seen in 25 (62.5%) and 20 (50%) of the patients, respectively. The majority of patients (65%) exhibited both the ileal and jejunal loops to be adequately distended in grade III. In 35% of the patients, grade II distensibility of the ileal and jejunal loops was seen. Conclusion When it comes to accurately detecting small intestinal disorders, CTE is superior. It evaluates extraintestinal, mural, and intraluminal diseases with efficacy, particularly in places that are difficult to reach. It is essential for directing clinical decisions because of its capacity to assess disease activity prior to endoscopy and see consequences.

[This corrects the article DOI: 10.3389/fmed.2024.1354363.].

For many years, noninvasive methods to measure intracranial pressure (ICP) have been unsuccessful. However, such methods are crucial for the assessment of patients with nonpenetrating traumatic brain injuries (TBIs) who are unconscious. In this study, we explored the use of transcranial transmission ultrasound (TTUS) to gather experimental data through brain pulsatility, assessing its effectiveness in detecting high ICP using machine learning analysis. We included patients with severe TBI under invasive ICP monitoring in our intensive care unit. During periods of both normal and elevated ICP, we simultaneously recorded ICP, arterial blood pressure, heart rate, and TTUS measurements. Our classification model was based on data from 9 patients, encompassing 387 instances of elevated ICP (>15 mmHg) and 345 instances of normal ICP (<10 mmHg), and validated through a leave-one-subject-out method. The study, conducted from October 2021 to October 2022, involved 25 patients with an average age of 61.6 ± 17.6 years, producing 279 datasets with an average ICP of 11.3 mmHg (1st quartile 6.1 mmHg; 3rd quartile 14.8 mmHg). The automated TTUS analysis effectively identified ICP values over 15 mmHg with 100% sensitivity and 47% specificity. It achieved a 100% negative predictive value and a 14% positive predictive value. This suggests that TTUS can accurately rule out high ICP above 15 mmHg in TBI patients, indicating patients who may need immediate imaging or intervention. These promising results, if confirmed and expanded in larger studies, could lead to the first reliable, noninvasive screening tool for detecting elevated ICP.

BACKGROUND: Preeclampsia (PE) is one of the most common hypertensive diseases, affecting 2%-8% of all pregnancies. The high maternal and fetal mortality rates of PE are due to a lack of early identification of affected pregnant women that would have led to closer monitoring and care. Recent data suggest that misfolded proteins might be a promising biomarker for PE prediction, which can be detected in urine samples of pregnant women according to their congophilia (aggregated) characteristic.
OBJECTIVE: The main purpose of this trial is to evaluate the value of the urine congophilia-based detection of misfolded proteins for the imminent prediction of PE in women presenting with suspected PE. The secondary objectives are to demonstrate that the presence of urine misfolded proteins correlates with PE-related maternal or neonatal adverse outcomes, and to establish an accurate PE prediction model by combining misfolded proteins with multiple indicators.
METHODS: At least 300 pregnant women with clinical suspicion of PE will be enrolled in this prospective cohort study. Participants should meet the following inclusion criteria in addition to a suspicion of PE: ≥18 years old, gestational week between 20+0 and 33+6, and single pregnancy. Consecutive urine samples will be collected, blinded, and tested for misfolded proteins and other PE-related biomarkers at enrollment and at 4 follow-up visits. Clinical assessments of PE status and related complications for all participants will be performed at regular intervals using strict diagnostic criteria. Investigators and participants will remain blinded to the results. Follow-up will be performed until 42 days postpartum. Data from medical records, including maternal and fetal outcomes, will be collected. The performance of urine misfolded proteins alone and combined with other biomarkers or clinical variables for the prediction of PE will be statistically analyzed.
RESULTS: Enrollment started in July 2023 and was still open upon manuscript submission. As of March 2024, a total of 251 eligible women have been enrolled in the study and enrollment is expected to continue until August 2024. Results analysis is scheduled to start after all participants reach the follow-up endpoint and complete clinical data are collected.
CONCLUSIONS: Upon completion of the study, we expect to derive an accurate PE prediction model, which will allow for proactive management of pregnant women with clinical suspicion of PE and possibly reduce the associated adverse pregnancy outcomes. The additional prognostic value of misfolded proteins is also expected to be confirmed.
BACKGROUND: Chinese Clinical Trials Registry ChiCTR2300074878; https://www.chictr.org.cn/showproj.html?proj=202096.
UNASSIGNED: PRR1-10.2196/54026.

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.
OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.
RESULTS: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.
CONCLUSIONS: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.
UNASSIGNED: PRR1-10.2196/54388.

Ultrasound has become a routine method for endometrial receptivity (ER) evaluation. However, there is controversy over the independent evaluation values of various ultrasound indicators. Some researchers have designed multi-indicator prediction systems, but their prediction values are uneven. To further our understanding of ER, we conducted this prospective cohort study to estimate ER noninvasively and effectively.
Women who underwent the first frozen-thawed embryo transfer (FET) cycle from April 2019 to July 2021 were included in the study. On the day of transfer, transvaginal three-dimensional ultrasound examination was performed to evaluate ER, including endometrial thickness, morphology, volume, movement, blood flow and flow index. The clinical pregnancy rate was the primary outcome. Based on whether clinical pregnancy was achieved, enrolled patients were divided into pregnant and nonpregnant groups.
This study analyzed 197 FET patients (139 pregnancies in total, 70.5%). The protective factors for clinical pregnancy included primary infertility [adjusted odds ratio (aOR), 1.98; 95% confidence interval (CI), 1.01-3.882; p = 0.047] and more frequent endometrial peristalsis (aOR, 1.33; 95% CI, 1.028-1.722; p = 0.03). Scores of 1-2 were assigned according to the relationship between different ultrasound indicators and the clinical pregnancy rate (CPR). The ER score of the patient was the sum of the scores of the 6 items. The ER score of the pregnant group was significantly higher than that of the nonpregnant group (7.40 ± 1.73 vs. 6.33 ± 1.99, p = 0.001). The CPR increased with an increasing ER score. The CPR in the ER < 6 group was significantly lower than that in the ER >6 group (45.5% vs. 75.6%, p = 0.001).
A noninvasive ultrasound scoring system for ER was proposed. This system may provide a non-invasive guidance perspective, in conjunction with invasive assessments currently used in clinical practice, to achieve more effective embryo transfer.

BACKGROUND: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.
OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model\'s predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

BACKGROUND: Systemic lupus erythematosus is a chronic, multisystem, inflammatory disease of autoimmune etiology occurring predominantly in women. A major hurdle to the diagnosis, treatment, and therapeutic advancement of this disease is its heterogeneous nature, which presents as a wide range of symptoms such as fatigue, fever, musculoskeletal involvement, neuropsychiatric disorders, and cardiovascular involvement with varying severity. The current therapeutic approach to this disease includes the administration of immunomodulatory drugs that may produce unfavorable secondary effects.
OBJECTIVE: This study explores the known relationship between the autonomic nervous system and inflammatory pathways to improve patient outcomes by treating autonomic nervous system dysregulation in patients via noninvasive vagus nerve stimulation. In this study, data including biomarkers, physiological signals, patient outcomes, and patient quality of life are being collected and analyzed. After completion of the clinical trial, a computer model will be developed to identify the biomarkers and physiological signals related to lupus activity in order to understand how they change with different noninvasive vagus nerve stimulation frequency parameters. Finally, we propose building a decision support system with integrated noninvasive wearable technologies for continuous cardiovascular and peripheral physiological sensing for adaptive, patient-specific optimization of the noninvasive vagus nerve stimulation frequency parameters in real time.
METHODS: The protocol was designed to evaluate the efficacy and safety of transauricular vagus nerve stimulation in patients with systemic lupus erythematosus. This multicenter, national, randomized, double-blind, parallel-group, placebo-controlled study will recruit a minimum of 18 patients diagnosed with this disease. Evaluation and treatment of patients will be conducted in an outpatient clinic and will include 12 visits. Visit 1 consists of a screening session. Subsequent visits up to visit 6 involve mixing treatment and evaluation sessions. Finally, the remaining visits correspond with early and late posttreatment follow-ups.
RESULTS: On November 2022, data collection was initiated. Of the 10 participants scheduled for their initial appointment, 8 met the inclusion criteria, and 6 successfully completed the entire protocol. Patient enrollment and data collection are currently underway and are expected to be completed in December 2023.
CONCLUSIONS: The results of this study will advance patient-tailored vagus nerve stimulation therapies, providing an adjunctive treatment solution for systemic lupus erythematosus that will foster adoption of technology and, thus, expand the population with systemic lupus erythematosus who can benefit from improved autonomic dysregulation, translating into reduced costs and better quality of life.
BACKGROUND: ClinicalTrials.gov NCT05704153; https://clinicaltrials.gov/study/NCT05704153.
UNASSIGNED: DERR1-10.2196/48387.

Decreased arterial perfusion is a typical condition of patients with peripheral artery disease (PAD), with the microvascular picture particularly present among women. This observational study aimed to detect foot perfusion changes by infrared thermography (IRT) after a home-based exercise program in both sexes. A total of 76 PAD patients with claudication (72 ± 4 years; 52 males) were enrolled in a structured in-home exercise program composed of two daily 8 min interval walking sessions (1:1 walk:rest ratio) with progressively increasing speed. Outcome measures collected at baseline (T0) and at each hospital visit after 5 weeks, 12 weeks and 20 weeks included foot temperature measured by IRT (anterior tibial, posterior tibial, dorsalis pedis and arcuate artery regions), ankle brachial index and the 6 min walking test. After 20 weeks, foot temperature in both limbs showed a significant increasing trend, with a mean variation of 1.3 °C for the more impaired limb and 0.9 °C for the contralateral limb (t = 8.88, p < 0.001 and t = 5.36; p < 0.001, respectively), with significant changes occurring after 5 weeks of training. The sex-oriented analysis did not highlight any significant difference, with an improvement of mean foot temperature of 1.5 ± 0.6 °C in females versus 1.2 ± 0.5 °C in males (p = 0.42). Ankle brachial index and performance also significantly improved over time (p < 0.001) without gender differences. In patients with PAD, a structured low-intensity exercise program significantly improved foot temperature and exercise capacity without any sex-related difference.

BACKGROUND: Cryolipolysis is a noninvasive technique for localized fat reduction, which induces selective apoptosis of the adipocytes using controlled exposure to intense cold. During the past 10 years, this technique has been shown to be safe and effective and its application has significantly increased. In this context, Cooltech® devices have been widely used around the world.
OBJECTIVE: The purpose of this retrospective study was to evaluate the efficacy and safety of the removal of localized fatty tissue in different body areas using the new Cooltech® Define controlled cooling system.
METHODS: This single-center study was carried out on 287 patients, both female and male, treated with Cooltech® Define on different body areas for a total of 1118 procedures. Follow-up evaluations were conducted to estimate efficacy and safety. Efficacy was evaluated through plicometry and photographs. Side effects were also reported.
RESULTS: Fold thickness showed a significant reduction (-69.91 ± 12.55%) after cryolipolysis treatment (fold thickness before: 35.33 ± 8.41 mm; fold thickness after: 10.69 ± 5.27 mm). Few side effects were reported, they included posttreatment pain (1.70%) and one case of paradoxical adipose hyperplasia (0.09%).
CONCLUSIONS: The Cooltech® Define cryolipolysis device is a safe, effective, and well-tolerated nonsurgical procedure for reducing localized fat.