Early Identification Research Articles

The impact of implementing a virtual interdisciplinary dysphagia clinic in independent sector care homes - Early identification and management of dysphagia and malnutrition is vital

The impact of implementing a virtual interdisciplinary dysphagia clinic in independent sector care homes - Early identification and management of dysphagia and malnutrition is vital

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Comparing long-term outcomes of children treated with new-onset type 2 diabetes in an outpatient versus inpatient setting: A retrospective chart review.

Early identification and management of pediatric type 2 diabetes mellitus (T2DM) is crucial for improving long-term outcomes. This study aimed to assess if the severity of T2DM at presentation, inferred by the location of treatment initiation (inpatient or outpatient), influences long-term clinical outcomes. A retrospective chart review was conducted on 116 pediatric T2DM patients. Data on treatment initiation location, initial and subsequent glycated hemoglobin (HbA1c) levels, prescribed insulin, and body mass index were collected from electronic medical records. Of the 116 patients, 69 were initially treated in an inpatient setting, and 47 received outpatient treatment. At treatment initiation, the inpatient group had significantly higher HbA1c levels compared to the outpatient group (p < .001), but 3 years after treatment initiation, no significant difference in HbA1c was observed between the two groups (p = .057). Prescribed insulin dosages were higher in the inpatient group at treatment initiation (p < .001) and remained higher after 3 years (p < 0.003) compared to the outpatient group. Pediatric patients initially treated in an inpatient setting had poorer glycemic control and higher prescribed insulin dosing at baseline. After 3 years, there was no significant difference in HbA1c levels, but patients treated as inpatients continued to have higher prescribed insulin. These findings suggest that the severity of diabetes at initial presentation may affect long-term clinical outcomes in children with T2DM.

Skin cancer diagnosis using the deep learning advancements: a technical review

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.

Association of Anxiety and Depressive Symptoms with Thyroid Hormone Concentrations in Patients with Primary Bone Tumors.

Timely identification and intervention of psychological disorders bear significant import in ameliorating the ensuing therapeutic trajectories in primary bone tumor patients. Moreover, perturbations in thyroxine and thyroid-stimulating hormone (TSH) levels have been linked to manifestations of depressive and anxiety-related symptoms. However, the precise interplay governing the nexus of anxiety, depression, and the levels of thyroxine and TSH within the context of primary bone tumor patients remains presently unexplored. The objective of this study is to investigate the potential correlation between the hypothalamus- pituitary-thyroxine (HPT) axis and the depressive as well as anxious states observed in patients afflicted with bone tumors. Patients with primary bone tumors were required to accept the assessments of anxiety and depressive symptoms as well as thyroid axis hormone concentrations. The depressive and anxiety symptoms were assessed using the Hamilton Depression Rating Scale (HAMD) and the Hamilton Anxiety Scale (HAMA) score. During each follow-up, peripheral venous blood samples were collected for subsequent analysis using radioimmunoassay methods to measure serum- free T3, free T4, and TSH levels, with the calculated free T3 to free T4 ratio indicating peripheral free T4 to free T3 conversion. Tests for trend were conducted to assess thyroid axis hormone concentrations, HAMA scores, and HAMD scores, while the correlation between HAMA or HAMD scores and thyroid axis hormone concentrations was examined through univariate regression analyses. The study included 30 primary bone tumor patients. Initial high HAMA and HAMD scores decreased over a year after surgery (P < 0.05), reflecting diminishing anxiety and depression. TSH levels reduced postoperatively, contrasting with increased free-T3 and free-T4 levels (p < 0.01). Multivariate analysis affirmed that positive correlations were noted between TSH and anxiety/depression scores, while free-T3 correlated negatively, adjusted for demographic factors (p < 0.05). No significant associations emerged between HAMA/HAMD scores and free-T4 or free-T3 to free-T4 ratio (p > 0.05). The early identification of the low T3 syndrome could prove instrumental in both intervening and preventing adverse emotional states associated with primary bone tumors.

Exploration and Validation of Lead Molecules against Yellow Fever through High Throughput Virtual Screening and Molecular Dynamics Simulation

Background: Yellow fever (YF) is a mosquito-borne flaviviral hemorrhagic fever (VHF) that causes severe hepatitis, renal failure, bleeding, and quick terminal events such as shock and multi-organ failure. There are currently no particular anti-viral medications for the management of the YF virus (YFV). Despite the availability of a commercial YFV vaccination, there are roughly 30,000 fatalities globally each year, with instances rising over the previous 20 years. After being translocated into the endoplasmic reticulum lumen, glycosylated NS1 resides as a membrane-associated dimer, where it is required for viral genome replication. The secreted hexamer NS1 has a role in immune evasion and pathogenesis and has been discovered as a possible diagnostic marker for the early identification of viral infections. Objective: The main aim of this study is to analyze the small molecule as a potent drug candidate against the target NS1 protein. Methods: In this study, Computational approaches, including high throughput virtual screening, molecular docking, and dynamics simulation, were carried out against the target NS1 protein using three different chemical libraries Enamine, Asinex, and NCI. The selected lead compounds were validated through HOMO-LUMO analysis, ADME prediction, and Toxicity parameters to analyze the biological and pharmacological properties of the lead small molecules. Results: From the result, it was concluded that the leads possessed the highest docking scores, interacting with the binding residues, and were stable in the simulation period. Conclusion: Overall findings revealed that the lead three small molecules could act as the potential drug candidate for the target NS1 protein to inhibit the diseasing efficacy of Yellow fever.

Psychological outcomes in patients with rare cancers: a systematic review and meta-analysis

Rare cancers are those that exhibit an incidence of less than six per 100,000 in a year. On average, the five-year relative survival for patients with rare cancers is worse than those with common cancers. The traumatic experience of cancer can be further intensified in patients with rare cancers due to the limited clinical evidence and the lack of empirical evidence for informed decision-making. With rare cancers cumulatively accounting for up to 25% of all cancers, coupled with the rising burden of rare cancers on societies globally, it is necessary to determine the psychological outcomes of patients with rare cancers. This PRISMA-adherent systematic review (PROSPERO: CRD42023475748) involved a systematic search of PubMed, Embase, Cochrane and PsycINFO for all peer-reviewed English language studies published since 2000 to 30th January 2024 that evaluated the prevalence, incidence and risk of depression, anxiety, suicide, and post-traumatic stress disorder (PTSD) in patients with rare cancers. Two independent reviewers appraised and extracted the summary data from published studies. Random effects meta-analyses and meta-regression were used for primary analysis. We included 32 studies with 57,470 patients with rare cancers. Meta-analyses indicated a statistically significant increased risk-ratio (RR) of depression (RR=2.61, 95% CI: 1.43-4.77, I2=97%) and anxiety (RR=2.66, 95% CI: 1.27-5.55, I2=92%) in patients with rare cancers compared to healthy controls. We identified a high suicide incidence (315 per 100,000 person-years, 95% CI: 162-609, I2=95%), prevalence of depression (17%, 95% CI: 14-22, I2=88%), anxiety (20%, 95% CI: 15-25, I2=96%) and PTSD (18%, 95% CI: 9-32, I2=25%). When compared to patients with common cancer types, suicide incidence, and PTSD prevalence were significantly higher in patients with rare cancers. Systematic review found that having advanced disease, chemotherapy treatment, lower income, and social status were risk factors for negative psychological outcomes. We highlight the need for early identification of psychological maladjustment in patients with rare cancers. Additionally, studies to identify effective interventions are imperative. This study was supported by the National Medical Research Council Transition Award, SingHealth Duke-NUS Oncology Academic Clinical Programme, the Khoo Pilot Collaborative Award, the National Medical Research Council Clinician Scientist-Individual Research Grant-New Investigator Grant, the Terry Fox Grant and the Khoo Bridge Funding Award.

Isolated injury, Charlson Comorbidity Index, and transfer from another facility are associated with delay in antibiotic administration: a retrospective study of 963 patients with open fractures.

To identify factors associated with delays in administration and pharmacy and nursing preparation of antibiotics for patients with open fractures. Retrospective review. Level I trauma center. Nine hundred sixty-three adults with open fractures administered antibiotics. Delay in antibiotic administration greater than 66 minutes from arrival and significant pharmacy-related and nursing-related delay. Isolated injury, Charlson Comorbidity Index, and transfer from another facility were associated with delay in antibiotic administration greater than 66 minutes. Injury Severity Score, transfer, and trauma team activation were associated with pharmacy-related or nursing-related delay. Interventions to reduce antibiotic administration time for open fractures should focus on early identification of open fractures and standardization of antibiotic protocols to ensure timely administration even in complex or resource-scarce care situations. Prognostic level III.

Dental Carries Prediction and Prevention

Abstract: A large percentage of people worldwide suffer from dental cavities, which are prevalent disorders. They caused by bacterial activity, can lead to permanent damage to teeth. They pose a risk to tooth health without timely intervention. In order to stop additional harm, early identification and treatment are essential. Traditionally, x-rays or a direct visual inspection are done by dentists to detect cavities. On the other hand, automated systems that employ image processing methods including region recognition, binary image synthesis, and RGB to Gray conversion can help locate cavities in x-ray pictures. Automated tools that utilize ordinary cameras for dental image analysis are also being explored, showing promising results.

Performance of Malayalam Speaking Children Fitted with Hearing Aids on PEACH Scale

Abstract: Parental questionnaires impart a better understanding of hearing technology utilization by children with Hearing loss(HL). They provide insights into the functional abilities and accomplishments of children equipped with Hearing Aids (HA). The present study aimed to translate and adapt the PEACH questionnaire into Malayalam language and evaluating its effectiveness in discriminating the functional hearing abilities of Malayalam speaking children with HL fitted with HAs at various stages of life. The Malayalam PEACH questionnaire was aPdministered to parents/caregivers of 40 Malayalamspeaking children aged 3 to 6, divided into two groups based on whether they received HA before or after the age of 2. Results revealed that children fitted with HA before 2 years of age demonstrated superior performance in quiet, noisy, and overall situations compared to those fitted after 2 years of age highlighting the importance of early identification of HL in children. Additionally, the questionnaire exhibited high reliability upon retesting across all of the three hearing situations (quiet,noise and overall). Therefore, the Malayalam PEACH questionnaire emerges as a dependable and effective tool for evaluating and monitoring the functional hearing abilities and communication skills of children fitted with HA or Cochlear Implants (CI) in their everyday lives

A comprehensive study of machine learning-based methods to predict epileptic seizures

People with epilepsy have many difficulties as a result of this complicated brain condition, which is typified by frequent convulsions. Symptoms of these seizures include bizarre behaviors, odd sensations, and in extreme cases, loss of awareness. These seizures appear as episodes of aberrant electrical impulses in the central nervous system. Successful epilepsy management depends on early seizure detection and identification, which allows for appropriate intervention to minimize risks and improve patient outcomes. Two major reasons have contributed to the extraordinary advancements in the area of epilepsy investigations in the last few years: the explosive development of machine learning techniques and the decreasing cost of non-invasive electroencephalography (EEG) apparatus. The availability of low-cost EEG equipment has made it easier to gather information on brain activity, which has opened up new avenues for monitoring and analyzing episodes of epileptic seizures away from conventional medical settings. The abundance of data and the advancement of machine learning methods have created new opportunities for the early identification and forecasting of seizures. Machine learning algorithms can predict seizures based on EEG data, providing patients with epilepsy with more control and informed decision-making. This paper offers a current review of current methods for treating epileptic seizures. The feature extraction techniques and classification algorithms receive particular focus. The most popular EEG datasets and their accessibility are listed. The approaches that are examined range from those that use more established machine learning techniques, such as naive Bayes models, Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), to those that take advantage of more recent deep learning techniques, like (Long-Short Term Memory, or LSTM), and deep Convolutional-Neural-Networks (CNN).

The thin fat paradox: Understanding normal weight obesity and its clinical relevance

Obesity, defined by the World Health Organization (WHO) as an excess accumulation of body fat or body fat percentage (BFP) associated with clear health risks, presents a significant global challenge. While Body Mass Index (BMI) serves as a conventional metric for identifying obesity, it fails to account for body composition variations, such as distinguishing between fat-free mass and adipose tissue. Consequently, individuals with normal BMI may exhibit excessive body fat (BFP≥30%), termed Normal Weight Obesity (NWO). In contrast, others with normal BMI and normal body fat are termed Normal Weight Lean (NWL). NWO significantly elevates the risks of various metabolic disorders, including hyperglycemia, diabetes, hypertension, dyslipidemia, and reduced HDL levels. Moreover, NWO is associated with an increased risk of cardiovascular diseases (CVD), hypertension, low HDL, central obesity, and muscle weakness. Individuals with NWO face a heightened risk of developing cardiometabolic diseases, including atherosclerosis, coronary artery disease, stroke, and heart failure, due to the adverse effects of excess body fat on cardiovascular health. Furthermore, NWO is associated with insulin resistance, inflammation, and endothelial dysfunction, further exacerbating the risk of metabolic disorders and cardiovascular complications. Early identification of NWO is crucial for implementing preventive measures and lifestyle interventions to mitigate these health risks. Integrating body fat percentage measurements into nutritional screening protocols in healthcare facilities is essential for identifying individuals at risk of NWO and initiating appropriate interventions to prevent the onset of cardiometabolic diseases. This study aims to provide comprehensive insights into Normal-Weight Obesity (NWO) and its potential health implications.

Gaining Insight into Updated MR Imaging for Quantitative Assessment of Cartilage Injury in Knee Osteoarthritis.

Knee Osteoarthritis (KOA) entails progressive cartilage degradation, reviewed via MRI for morphology, biochemical composition, and microtissue alterations, discussing clinical advantages, limitations, and research applicability. Compositional MRI, like T2/T2* mapping, T1rho mapping, gagCEST, dGEMRIC, sodium imaging, diffusion-weighted imaging, and diffusion-tensor imaging, provide insights into cartilage injury in KOA. These methods quantitatively measure collagen, glycosaminoglycans, and water content, revealing important information about biochemical compositional and microstructural alterations. Innovative techniques like hybrid multi-dimensional MRI and diffusion-relaxation correlation spectrum imaging show potential in depicting initial cartilage changes at a sub-voxel level. Integration of automated image analysis tools addressed limitations in manual cartilage segmentation, ensuring robust and reproducible assessments of KOA cartilage. Compositional MRI techniques reveal microstructural changes in cartilage. Multi-dimensional MR imaging assesses biochemical alterations in KOA-afflicted cartilage, aiding early degeneration identification. Integrating artificial intelligence enhances cartilage analysis, optimal diagnostic accuracy for early KOA detection and monitoring.

Construction of a Near-Infrared Fluorescent Probe for Dynamic Monitoring and Early Diagnosis of Heart Failure.

Cardiac hypertrophy characterized by abnormal cardiomyocyte viscosity is a typical sign of heart failure (HF) with vital importance for early diagnosis. However, current biochemical and imaging diagnostic methods are unable to detect this subclinical manifestation. In this work, we developed a series of NIR-I fluorescence probes for detecting myocardial viscosity based on the pyridazinone scaffold. The probes showed weak fluorescence due to free intramolecular rotation under low-viscosity conditions, while they displayed strong fluorescence with limited intramolecular rotation in response to a high-viscosity environment. Among them, CarVis2 exhibited higher stability and photobleaching resistance than commercial dyes. Its specific response to viscosity was not influenced by the pH and biological species. Furthermore, CarVis2 showed rapid and accurate responses to the viscosity of isoproterenol (ISO)-treated H9C2 cardiomyocytes with good biocompatibility. More importantly, CarVis2 demonstrated excellent sensitivity in monitoring myocardial viscosity variation in HF mice in vivo, potentially enabling earlier noninvasive identification of myocardial abnormalities compared to traditional clinical imaging and biomarkers. These findings revealed that CarVis2 can serve as a powerful tool to monitor myocardial viscosity, providing the potential to advance insights into a pathophysiological mechanism and offering a new reference strategy for early visual diagnosis of HF.

Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study

BackgroundResearch has demonstrated that sarcopenia and visceral obesity are significant risk factors for chronic disease in middle-aged and older adults. However, the relationship between sarcopenia, the cardiac metabolic index (CMI), a novel measure of visceral obesity, and cardiometabolic multimorbidity (CMM) remains unclear. In this study, data from the China Longitudinal Study of Health and Retirement (CHARLS) were analyzed to investigate the association between sarcopenia and CMI with CMM in the middle-aged and older adult population.MethodsThe study included 4,959 participants aged 45 and over. Sarcopenia was defined using the criteria of the Asian Sarcopenia Working Group 2019. CMM is defined as having two or more of the following conditions: physician-diagnosed heart disease, diabetes, stroke, and/or hypertension. CMI was calculated using the formula: CMI = (TG/HDL-C) × WHtR. To explore the association between CMI and sarcopenia and CMM, cox proportional risk regression models were used.ResultsThe median age of all participants was 57 years, with 47.1% being male. Over the 8-year follow-up, 1,362 individuals developed CMM. The incidence of CMM was 8.7/1,000 person-years in the group without sarcopenia or high CMI, 17.37/1,000 person-years in those with high CMI, 14.22/1,000 person-years in the sarcopenia group, and 22.34/1,000 person-years in the group with both conditions. After adjusting for covariates, the group with both sarcopenia and high CMI had a significantly increased risk of CMM (HR 2.48, 95% CI 1.12-5.51) and heart disease (HR 2.04, 95% CI 1.05-3.98). Among those over 65 years, sarcopenia was discovered to be associated with an increased risk of CMM [HR (95% CI: 4.83 (1.22, 19.06)]. The risk of CMM was further increased to 7.31-fold (95% CI:1.72, 31.15) when combined with high CMI.ConclusionsThe combination of sarcopenia and high CMI is associated with an increased risk of developing CMM. Early identification and intervention of sarcopenia and CMI not only enable the development of targeted therapeutic strategies but also provide potential opportunities to reduce the morbidity and mortality of CMM.

Evaluation of an artificial intelligence model for identification of mass effect and vasogenic edema on computed tomography of the head.

Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) to consensus neuroradiologist interpretations in detecting mass effect and vasogenic edema. A retrospective standalone performance assessment was conducted on datasets of non-contrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients aged 18 years or older from five hospitals in the United States. The positive cases were selected consecutively based on the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up to three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the AI model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The AI model separately received thin (≤1.5mm) and/or thick (>1.5 and ≤5mm) axial series. The two cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The AI model identified mass effect with sensitivity 96.6% (95% CI, 94.9-98.2) and specificity 89.8% (95% CI, 84.7-94.2) for the thin series, and 95.3% (95% CI, 93.5-96.8) and 93.1% (95% CI, 89.1-96.6) for the thick series. It identified vasogenic edema with sensitivity 90.2% (95% CI, 82.0-96.7) and specificity 93.5% (95% CI, 88.9-97.2) for the thin series, and 90.0% (95% CI, 84.0-96.0) and 95.5% (95% CI, 92.5-98.0) for the thick series. The corresponding areas under the curve were at least 0.980. The assessed AI model accurately identified mass effect and vasogenic edema in this CT dataset. It could assist the clinical workflow by prioritizing interpretation of abnormal cases, which could benefit patients through earlier identification and subsequent treatment. AI = artificial intelligence; AUC = area under the curve; CADt = computer assisted triage devices; FDA = Food and Drug Administration; NPV = negative predictive value; PPV = positive predictive value; SD = standard deviation.

A novel in vitro high-content imaging assay for the prediction of drug-induced lung toxicity.

The development of inhaled drugs for respiratory diseases is frequently impacted by lung pathology in non-clinical safety studies. To enable design of novel candidate drugs with the right safety profile, predictive in vitro lung toxicity assays are required that can be applied during drug discovery for early hazard identification and mitigation. Here, we describe a novel high-content imaging-based screening assay that allows for quantification of the tight junction protein occludin in A549 cells, as a model for lung epithelial barrier integrity. We assessed a set of compounds with a known lung safety profile, defined by clinical safety or non-clinical in vivo toxicology data, and were able to correctly identify 9 of 10 compounds with a respiratory safety risk and 9 of 9 compounds without a respiratory safety risk (90% sensitivity, 100% specificity). The assay was sensitive at relevant compound concentrations to influence medicinal chemistry optimization programs and, with an accessible cell model in a 96-well plate format, short protocol and application of automated imaging analysis algorithms, this assay can be readily integrated in routine discovery safety screening to identify and mitigate respiratory toxicity early during drug discovery. Interestingly, when we applied physiologically-based pharmacokinetic (PBPK) modelling to predict epithelial lining fluid exposures of the respiratory tract after inhalation, we found a robust correlation between in vitro occludin assay data and lung pathology in vivo, suggesting the assay can inform translational risk assessment for inhaled small molecules.

Extravasated contrast volumetric assessment on computed tomography angiography in gastrointestinal bleeding: A useful predictor of positive angiographic findings

BACKGROUND Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening condition, especially in cases of delayed treatment. Computed tomography angiography (CTA) plays a pivotal role in the early identification of upper and lower GIB and in the prompt treatment of the haemorrhage. AIM To determine whether a volumetric estimation of the extravasated contrast at CTA in GIB may be a predictor of subsequent positive angiographic findings. METHODS In this retrospective single-centre study, 35 patients (22 men; median age 69 years; range 16-92 years) admitted to our institution for active GIB detected at CTA and further submitted to catheter angiography between January 2018 and February 2022 were enrolled. Twenty-three (65.7%) patients underwent endoscopy before CTA. Bleeding volumetry was evaluated in both arterial and venous phases via a semi-automated dedicated software. Bleeding rate was obtained from volume change between the two phases and standardised for unit time. Patients were divided into two groups, according to the angiographic signs and their concordance with CTA. RESULTS Upper bleeding accounted for 42.9% and lower GIB for 57.1%. Mean haemoglobin value at the admission was 7.7 g/dL. A concordance between positive CTA and direct angiographic bleeding signs was found in 19 (54.3%) cases. Despite no significant differences in terms of bleeding volume in the arterial phase (0.55 mL vs 0.33 mL, P = 0.35), a statistically significant volume increase in the venous phase was identified in the group of patients with positive angiography (2.06 mL vs 0.9 mL, P = 0.02). In the latter patient group, a significant increase in bleeding rate was also detected (2.18 mL/min vs 0.19 mL/min, P = 0.02). CONCLUSION In GIB of any origin, extravasated contrast volumetric analysis at CTA could be a predictor of positive angiography and may help in avoiding further unnecessary procedures.

Self-Reported Sleep Duration Is a Useful Tool to Predict Sarcopenia in Chilean Older Adults: Evidence from the ALEXANDROS Longitudinal Study

Age-related sleep disorders share common pathways with sarcopenia. Prospective data from Latin American populations are scarce, and the association between sleep disorders and sarcopenia in Chileans remains unknown. Thus, we aimed to study the longitudinal association between sleep disorders and sarcopenia in a cohort study of 1116 community-dwelling Chilean older people ≥60 years old from the ALEXANDROS cohorts. After the exclusion criteria, 318 subjects were followed. Sociodemographic data, self-reported chronic diseases, sedentarism, sleep characteristics, anthropometric measurements, handgrip strength, and muscle performance were assessed. Results indicated that at baseline, the prevalence of sarcopenia was 24.10% without gender differences, and the prevalence of self-reported sleep problems was 23.3%, higher in women (26.46% versus 17.15% in men). The adjusted Cox regression models for sarcopenia showed an association between sarcopenia, sleep disorders (HR = 2.08, 95% IC 1.14–3.80), and long sleep duration (HR = 2.42, 95% IC 1.20–4.91). After 8.24 years of follow-up, there were 2.2 cases of sarcopenia per 100 person-years. This study demonstrates that sleep disorders are an independent risk factor for sarcopenia in Chilean older people. The identification of sleep disorders through self-reported data provides an opportunity for early identification of risk and cost-effective sarcopenia prevention.