Colloid, interface, and foam properties of water-soluble polyglycerol esters solutions.

HYPOTHESIS: Polyglycerol esters of fatty acids are generated via the esterification of a polydisperse mixture of polyglycerol with naturally derived fatty acids. The polymerization process of polyglycerol results in the production of various oligomers, ranging from di-, tri-, and higher-order forms, which contribute to the complexity of final products. The combination of complementary experimental techniques and adequate theoretical interpretations can reveal the wide variety of their physicochemical properties. EXPERIMENTS: The colloid and interface properties of polyglyceryl mono-laurate, mono-stearate, mono-oleate, and a mixture of mono-caprylate and mono-caprate esters solutions were characterized by measurements of the electrolytic conductivity, static and dynamic surface tension, aggregate and micelle sizes and distributions, thin liquid film stability and stratification, and solubility in aqueous and in oil phases. The formation, stability, and bubble size distribution of foams generated from polyglycerol esters aqueous solutions were systematically investigated. FINDINGS: The low concentrations of double-tail molecules and fatty acids in polyglycerol esters affect considerably their micellar, aggregation, and vesicle formations in aqueous solutions. The theoretical data interpretation of polyglycerol esters isotherms and thin liquid films data provide information on the adsorption energies, excluded areas per molecule, interaction parameters of molecules at interfaces, surface electrostatic potential, and the size of micelles. Polyglyceryl mono-oleate exhibits spontaneous emulsification properties. Short chain length polyglycerol esters have excellent foaming ability but relatively low foam stability. The optimal weight fractions of the short-chain polyglyceryl esters and polyglyceryl mono-stearate mixtures with respect to good foaminess and foam stability upon Ostwald ripening are obtained. The reported physicochemical characterization of the water-soluble polyglycerol esters could be of interest to increase the range of their applicability in practice.

Lower social vulnerability is associated with a higher prevalence of social media-involved violent crimes in Prince George's County, Maryland, 2018-2023.

BACKGROUND: Social vulnerability may play a role in social media-involved crime, but few studies have investigated this issue. We investigated associations between social vulnerability and social media-involved violent crimes. METHODS: We analyzed 22,801 violent crimes occurring between 2018 and 2023 in Prince George's County, Maryland. Social media involvement was obtained from crime reports at the Prince George's County Police Department. Social media application types included social networking, advertising/selling, ridesharing, dating, image/video hosting, mobile payment, instant messaging/Voice over Internet Protocol, and other. We used the Centers for Disease Control and Prevention's Social Vulnerability Index to assess socioeconomic status (SES), household characteristics, racial and ethnic minority status, housing type and transportation, and overall vulnerability. Modified Poisson models estimated adjusted prevalence ratios (aPRs) among the overall sample and stratified by crime type (assault and homicide, robbery, and sexual offense). Covariates included year and crime type. RESULTS: Relative to high tertile areas, we observed a higher prevalence of social media-involved violent crimes in areas with low SES vulnerability (aPR: 1.82, 95% CI: 1.37-2.43), low housing type and transportation vulnerability (aPR: 1.53, 95% CI: 1.17-2.02), and low overall vulnerability (aPR: 1.63, 95% CI: 1.23-2.17). Low SES vulnerability areas were significantly associated with higher prevalences of social media-involved assaults and homicides (aPR: 1.64, 95% CI: 1.02-2.62), robberies (aPR: 2.00, 95% CI: 1.28-3.12), and sexual offenses (aPR: 2.07, 95% CI: 1.02-4.19) compared to high SES vulnerability areas. Low housing type and transportation vulnerability (vs. high) was significantly associated with a higher prevalence of social media-involved robberies (aPR: 1.54, 95% CI:1.01-2.37). Modified Poisson models also indicated that low overall vulnerability areas had higher prevalences of social media-involved robberies (aPR: 1.71, 95% CI: 1.10-2.67) and sexual offenses (aPR: 2.14, 95% CI: 1.05-4.39) than high overall vulnerability areas. CONCLUSIONS: We quantified the prevalence of social media-involved violent crimes across social vulnerability levels. These insights underscore the need for collecting incident-based social media involvement in crime reports among law enforcement agencies across the United States and internationally. Comprehensive data collection at the national and international levels provides the capacity to elucidate the relationships between neighborhoods, social media, and population health.

Efficacy of inflammation-based stratification for add-on celecoxib or minocycline in major depressive disorder: Protocol of the INSTA-MD double-blind placebo-controlled randomised clinical trial.

Introduction: Different lines of evidence confirm the involvement of the immune system in the pathophysiology of major depressive disorder. Up to 30% of depressed patients present with an immune-mediated subtype, characterized by peripheral inflammation (high-sensitive C-reactive protein (hsCRP) ≥ 3 mg/l) and an atypical symptom profile with fatigue, anhedonia, increased appetite, and hypersomnia. This immune-mediated subtype of MDD is associated with poorer response to first-line antidepressant treatment. Consequently, strategies for immune-targeted augmentation should be prioritised towards patients with this subtype. Meta-analyses have shown modest but heterogeneous treatment effects with immune-targeted augmentation in unstratified MDD cohorts, with celecoxib and minocycline as most promising first-line treatment options. However, no study has prospectively evaluated the effectiveness of a priori stratification by baseline inflammation levels for add-on celecoxib or minocycline in MDD. Methods: The INSTA-MD trial is a multicentre, 12-week, randomised, double-blind, placebo-controlled, parallel-group stratified clinical trial of adjunctive minocycline or celecoxib to treatment-as-usual for patients with MDD. Two hundred forty adult patients with Major Depressive Disorder who failed to remit with one or two trials of antidepressant treatment will be enrolled and allocated to high-hsCRP (hsCRP ≥3 mg/L) or low-hsCRP (hsCRP <3 mg/L) strata, where disproportional stratified sampling will ensure equally sized strata. Participants in each hsCRP stratum will be randomised to augment their ongoing antidepressant treatment with either adjunctive minocycline, celecoxib or placebo for a duration of 12 weeks, resulting in six treatment arms of each 40 participants. The primary objective is to evaluate the efficacy of immune-targeted augmentation with minocycline or celecoxib versus placebo, and the use of baseline hsCRP stratification to predict treatment response. Additionally, we will perform a head-to-head analysis between the two active compounds. The primary outcome measure is change in the Hamilton Depression Rating Scale (HDRS-17) total score. Secondary outcome measures will be response and remission rates, and change in inflammation-specific symptoms, adverse events and therapy acceptability (adherence). Further exploratory analyses will be performed with an array of peripheral inflammatory biomarkers, metabolic outcomes and physiological data. Expected impact: The aim of INSTA-MD is to advance the use of immune-targeted precision psychiatry, by supporting the implementation of targeted hsCRP screening and treatment of immune-mediated MDD as a cost-effective intervention in primary care settings. Based on previous studies, we expect immune-targeted augmentation with minocycline or celecoxib to yield a superior remission rate of 15-30% compared to treatment as usual for immune-mediated cases of MDD. By treating immune-related depression early in the treatment algorithm with repurposed first-line anti-inflammatory treatments, we can significantly improve the outcomes of these patients, and reduce the global societal and economic burden of depression. Ethics and dissemination: This protocol has been approved by the Medical Ethics Review Board (CTR - 04/08/2023). Registration details: Trial registration number NCT05644301 (Clinical trial.gov), EU-CT 2022-501692-35-00.

Integrating oxidative-stress biomarkers into a precision oncology risk-stratification model for bladder cancer prognosis and therapy.

Introduction: Bladder cancer is a common malignant tumor with significant heterogeneity, making personalized risk stratification crucial for optimizing treatment and prognosis. This study aimed to develop a prognostic model based on oxidative stress-related genes to guide risk assessment in bladder cancer. Methods: Differentially expressed oxidative stress-related genes were identified using the GEO database. Functional enrichment and survival analyses were performed on these genes. A risk-scoring model was built and tested for prognostic value and therapeutic response prediction. Expression of key genes was validated by qRT-PCR in samples from two muscle-invasive and two non-muscle-invasive bladder cancer patients. Results: Several oxidative stress-related genes were identified as significantly associated with survival. The risk-scoring model stratified patients into high- and low-risk groups, accurately predicting prognosis and therapeutic responses. qRT-PCR confirmed the differential expression of key genes in patient samples. Discussion: The study provides a concise risk stratification model based on oxidative stress-related genes, offering a practical tool for improving personalized treatment in bladder cancer. Further validation is required for broader clinical application.

Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) vary widely in prognosis, and traditional pathological assessments often lack precision in risk stratification. Advanced imaging techniques, especially magnetic resonance imaging (MRI), offer potential improvements. This study investigates how MRI imagomics can enhance risk assessment and support personalized treatment for GIST patients. AIM: To assess the effectiveness of MRI imagomics in improving GIST risk stratification, addressing the limitations of traditional pathological assessments. METHODS: Analyzed clinical and MRI data from 132 GIST patients, categorizing them by tumor specifics and dividing into risk groups. Employed dimension reduction for optimal imagomics feature selection from diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), and contrast enhanced T1WI with fat saturation (CE-T1WI) fat suppress (fs) sequences. RESULTS: Age, lesion diameter, and mitotic figures significantly correlated with GIST risk, with DWI sequence features like sphericity and regional entropy showing high predictive accuracy. The combined T1WI and CE-T1WI fs model had the best predictive efficacy. In the test group, the DWI sequence model demonstrated an area under the curve (AUC) value of 0.960 with a sensitivity of 80.0% and a specificity of 100.0%. On the other hand, the combined performance of the T1WI and CE-T1WI fs models in the test group was the most robust, exhibiting an AUC value of 0.834, a sensitivity of 70.4%, and a specificity of 85.2%. CONCLUSION: MRI imagomics, particularly DWI and combined T1WI/CE-T1WI fs models, significantly enhance GIST risk stratification, supporting precise preoperative patient assessment and personalized treatment plans. The clinical implications are profound, enabling more accurate surgical strategy formulation and optimized treatment selection, thereby improving patient outcomes. Future research should focus on multicenter studies to validate these findings, integrate advanced imaging technologies like PET/MRI, and incorporate genetic factors to achieve a more comprehensive risk assessment.

Preoperative preparation of children with upper respiratory tract infection: a focussed narrative review.

This review summarises the current evidence for the perioperative preparation in children with upper respiratory tract infections (URTI), including COVID-19 infection. URTI, including COVID-19 infection, are common and frequent in children who present for elective surgery. Children with URTI are at increased risk of perioperative respiratory adverse events. Perioperative respiratory adverse events are among the most serious and impactful consequences of paediatric anaesthesia, including cardiorespiratory arrest, and therefore present a significant challenge for the paediatric anaesthetist. This review addresses the pathophysiology and time course of URTI, including COVID-19. The evidence-based patient, anaesthetic, and surgical risk factors for perioperative respiratory adverse events are summarised. These risk factors work synergistically to determine individual patient risk and allow for risk stratification both clinically and with validated scoring systems. Evidence-based optimisation of modifiable respiratory risk factors can reduce the risk of perioperative bronchospasm. The evidence for the anaesthesia management options, including the timing and setting of surgery, experience of the paediatric anaesthetist, premedication, choice of airway device, choice of agent for induction and maintenance of anaesthesia, and deep vs awake tracheal extubation techniques along with a risk stratification framework are discussed.

High-Risk HLA-DQ Mismatches Are Associated With Adverse Outcomes After Lung Transplantation.

Human leukocyte antigen (HLA) mismatches (MM) between donor and recipient lead to eplet MM (epMM) in lung transplantation (LTX), which can induce the development of de-novo donor-specific HLA-antibodies (dnDSA), particularly HLA-DQ-dnDSA. Aim of our study was to identify risk factors for HLA-DQ-dnDSA development. We included all patients undergoing LTX between 2012 and 2020. All recipients/donors were typed for HLA 11-loci. Development of dnDSA was monitored 1-year post-LTX. EpMM were calculated using HLAMatchmaker. Differences in proportions and means were compared using Chi2-test and Students' t-test. We used Kaplan-Meier curves with LogRank test and multivariate Cox regression to compare acute cellular rejection (ACR), chronic lung allograft dysfunction (CLAD) and survival. Out of 183 patients, 22.9% patients developed HLA-DQ-dnDSA. HLA-DQ-homozygous patients were more likely to develop HLA-DQ-dnDSA than HLA-DQ-heterozygous patients (p = 0.03). Patients homozygous for HLA-DQ1 appeared to have a higher risk of developing HLA-DQ-dnDSA if they received a donor with HLA-DQB1*03:01. Several DQ-eplets were significantly associated with HLA-DQ-dnDSA development. In the multivariate analysis HLA-DQ-dnDSA was significantly associated with ACR (p = 0.03) and CLAD (p = 0.01). HLA-DQ-homozygosity, several high-risk DQ combinations and high-risk epMM result in a higher risk for HLA-DQ-dnDSA development which negatively impact clinical outcomes. Implementation in clinical practice could improve immunological compatibility and graft outcomes.

Spatiotemporal insights of phytoplankton dynamics in a northern, rural-urban lake using a 3D water quality model.

Lake St. Charles, located north of Quebec City, Canada, is a shallow fluvial lake with two distinct basins bridging rural and urban landscapes. Mainly used as a source of drinking water for 300,000 residents, the lake has faced a steady degradation in water quality due to urbanization and the discharge of domestic wastewater. This study introduces a 3D hydrodynamics and water quality model using the Environmental Fluid Dynamics Code to enhance our understanding of algal bloom dynamics in Lake St. Charles. More specifically, we ran simulations for eight years (i.e., a three-year period for calibration, 2015 to 2017; and a five-year period for validation, 2018 to 2022) to reproduce the complex circulation patterns and dynamics of water quality within the system. The simulation results for chlorophyll-a demonstrate seasonal fluctuations in phytoplankton biomass, closely aligning with in situ observations and achieving Relative Root Mean Square Error (RRMSE) values below 50%. (i) In spring, runoff from snowmelt brought phosphorus into the lake, triggering primary production. Diatom growth was initially predominant in the shallow southern basin, then spread to the deeper northern basin due to favorable environmental conditions, including flow- and wind-induced currents, warmer water temperatures and nutrient availability. (ii) In summer, warm water temperatures stimulated biological activity, leading to the growth of cyanobacteria at the expense of diatoms, as well as a drop in phosphorus. (iii) The cyanobacteria persisted into the fall but began to decline in mid-November. (iv) Winter conditions, including the presence of an ice cover, limited the input of phosphorus and minimized phytoplankton production, but diatoms were observed in low concentrations near Des Hurons River inflow. Overall, during the open-water period, the lake-maintained chlorophyll-a concentrations indicative of mesotrophic conditions, with occasional periods when the biomass increased above the eutrophic threshold. Temperature, nutrient levels, and the fluvial dynamics of the lake are the primary factors influencing phytoplankton formation and distribution in lake St. Charles.

A multiparametric heart failure score at baseline is associated with long-term outcome in patients with remotely monitored implantable cardioverter-defibrillators: A pooled analysis of nine clinical trials.

BACKGROUND: To predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a Heart Failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). OBJECTIVE: We studied the association of the baseline HF Score (BHFScore), determined at algorithm activation, with long-term patient outcomes. METHODS: Data from nine clinical trials were pooled, including 1,841 ICD patients with a pre-implant ejection fraction ≤35%, NYHA class II/III, and no long-standing atrial fibrillation. Primary endpoint was a composite of death or WHFH. RESULTS: After a median follow-up of 631 days (interquartile range, 385-865), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. Primary endpoint occurred in 265 patients (14.4%). A multivariable time-to-first event analysis showed that a high BHFScore (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of primary endpoint (adjusted hazard ratio [HR], 2.05; 95%-confidence interval [CI], 1.54-2.71; p<0.0001), all-cause death (HR, 2.37; CI, 1.56-3.58; p<0.0001), cardiovascular death (HR, 2.19; CI, 1.14-4.22; p=0.019), and WHFH (HR, 1.91; CI, 1.35-2.71; p=0.0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated-event outcomes, the win-ratio was 2.47 (CI, 1.89-3.24; p<0.0001). CONCLUSIONS: Based on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.

Cost-effective identification of Barrett's esophagus in the community: A first step towards screening.

BACKGROUND AND AIM: The first step towards developing a screening strategy for Barrett's esophagus (BE) is the identification of individuals in the community. Currently available tools include endoscopy, less-invasive non-endoscopic devices, and non-invasive risk stratification models. We evaluated the cost of potential strategies for identification of BE as a first step towards screening. METHODS: Two hypothetical cohorts of the general population aged ≥ 50 years with BE prevalence rates of 1.9% and 6.8% were modeled. Four potential screening tools were evaluated: (i) risk stratification based on non-weighted clinical factors according to US/European guidelines, (ii) weighted risk stratification using algorithmic models, (iii) less-invasive devices such as Cytosponge + trefoil factor 3 (TFF3), and (iv) endoscopy. Using a decision-analytic model, the cost per BE case identified and the cost-effectiveness were compared for six potential BE screening strategies based on combinations of the four screening tools; (i) + (iv), (ii) + (iv), (iii) + (iv), (i) + (iii) + (iv), (ii) + (iii) + (iv), and only (iv). RESULTS: The cost per BE case identified was lowest for the weighted risk stratification followed by Cytosponge-TFF3 then endoscopy strategy at both 1.9% and 6.8% BE prevalences (US$9282 and US$3406, respectively) although it was sensitive to the cost of less-invasive devices. This strategy was also most cost-effective for a BE prevalence of 1.9%. At BE prevalence of 6.8%, the Cytosponge-TFF3 followed by endoscopy strategy was most cost-effective. CONCLUSIONS: Incorporating weighted risk stratification and less-invasive devices such as Cytosponge-TFF3 into BE screening strategies has a potential to cost-effectively identify BE in the community although device cost and the community prevalence of BE will impact the optimal strategy.

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development.

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

Diagnostic utility of coronary artery calcium score percentiles and categories to exclude abnormal scans and relevant ischemia in rubidium positron emission tomography.

Background: Despite clinical suspicion, most non-invasive ischemia tests for coronary artery disease (CAD) reveal unremarkable results. Patients with a coronary artery calcium score (CACS) of zero rarely have an abnormal positron emission tomography (PET) and could be deferred from further testing. However, most patients have some extent of coronary calcification. Objectives: CACS percentiles could be useful to exclude abnormal perfusion in patients with CACS >0, but data from patients with 82Rb PET are lacking. The aim of this study was to assess the diagnostic utility of CACS percentiles in comparison to zero calcium and absolute CACS classes. Methods: Consecutive patients with suspected CAD (n = 1,792) referred for 82Rb PET were included and analyzed for abnormal PET (SSS ≥4) and relevant ischemia (>10% myocardium). Test characteristics were calculated. Results: The mean age was 65 ± 11 years, 43% were female, and typical angina was reported in 21%. Abnormal PET/relevant ischemia (>10%) were observed in 19.8%/9.3%. Overall, the sensitivity/negative predictive value (NPV) of a <25th percentile CACS to rule out abnormal PET and relevant ischemia were 93.0%/95.7% and 98.2%/99.5%, respectively. The sensitivity/NPV of CACS 1-9 to rule out abnormal PET and relevant ischemia were 96.0%/91.8% and 97.6%/97.6%, respectively. Except for patients <50 years old, sensitivity for abnormal PET was >90.9% in all age groups. Conclusion: In patients >50 years, the <25th percentile and CACS 1-9 had good test characteristics to rule out abnormal PET and relevant ischemia (>10%). They could be used to extend the scope of application of CACS 0 by 8%-10% to 32%-34% overall of patients who could be deferred from further testing.

Preoperative prognostic stratification and prediction of long-term outcomes after pancreatoduodenectomy for distal cholangiocarcinoma.

BACKGROUND: Patients with distal cholangiocarcinoma (DCC) frequently receive adjuvant chemotherapy in preoperative and postoperative settings, but prediction of prognostic risk at the time of treatment selection remains challenging. METHODS: This single-center retrospective study enrolled DCC patients who underwent initial pancreatoduodenectomy (PD) between 2009 and 2022. Preoperative clinical parameters were collected, and Cox regression analysis was used to identify risk factors for overall survival (OS). RESULTS: Among 170 patients examined, the median tumor depth was 10 mm, and 37 % of the patients were diagnosed with pT3. Overall, 46 % of patients had lymph node metastasis. The median and 5-year OS was 58.2 months and 50 %, respectively. Multivariate analysis revealed tumor size on computed tomography (CT) ≥15 mm and main pancreatic duct (MPD) dilatation (≥3 mm) as independent risk factors for OS among various preoperative parameters; the prognosis was stratified based on these two parameters. Patients with one risk factor had similar outcomes (5-year OS: 39 %) to pStage IIB DCC (pT2N1 or pT3), while those with two risk factors had a prognosis akin to pStage IIIA (pN2), with a high early recurrence rate of 64 % (5-year OS: 8 %). Among non-risk group patients with low carbohydrate antigen (CA)19-9 levels (<37 U/mL), the prognosis was comparable (5-year OS: 72 %) to those with pStage I DCC. CONCLUSION: A simple stratification approach was developed to predict long-term postoperative outcomes. To improve poor prognosis, intensive therapy, including neoadjuvant chemotherapy, should be considered for patients with two risk factors.

A simplified and robust risk stratification model for stem cell transplantation in pediatric acute myeloid leukemia.

The efficacy of stem cell transplantation (SCT) in pediatric acute myeloid leukemia (pAML) remains unsatisfactory due to the limitations of existing prognostic models in predicting efficacy and selecting suitable candidates. This study aims to develop a cytomolecular risk stratification-independent prognostic model for SCT in pAML patients at CR1 stage. The pAML SCT model, based on age, KMT2A rearrangement (KMT2A-r), and minimal residual disease at end of course 1 (MRD1), effectively classifies patients into low-, intermediate-, and high-risk groups. We validate the effectiveness in an internal validation cohort and in four external validation cohorts, consisting of different graft sources and donors. Moreover, by incorporating the FMS-like tyrosine kinase 3/internal tandem duplication (FLT3/ITD) allelic ratio, the pAML SCT model is refined, enhancing its ability to effectively select suitable candidates. We develop a simple and robust risk stratification model for pAML patients undergoing SCT, to aid in risk stratification and inform pretransplant decision-making at CR1 stage.

Fragmented QRS in patients with preeclampsia.

PURPOSE: This study aims to investigate the prevalence, clinical correlates, and prognostic implications of fragmented QRS complexes (fQRS) in pregnant women with preeclampsia (PE), shedding light on the potential role of electrocardiographic markers in identifying cardiac involvement in hypertensive disorders of pregnancy. METHODS: Patients with PE and age-matched low-risk control patients were recruited at a tertiary hospital between January 2015 and January 2023. A comprehensive assessment, including heart rate, PR duration, QRS duration, corrected QT duration, and fragmented QRS, was conducted by 12-lead electrocardiography. Baseline clinical characteristics, laboratory parameters, and electrocardiographic findings were compared between the study groups. RESULT: 128 preeclampsia patients and 122 age- and comorbidity-matched controls were included in the study. The prevalence of fQRS was significantly higher in preeclamptic women compared to normotensive controls (14.1% vs. 3.3%, p = 0.04). ALT levels of pregnant women with preeclampsia and without preeclampsia groups were 43,77 (35.25-48.22) and 23,18 (13.75-33.00) (p: 0.038), respectively. In univariate regression analyses, Na and fragmented QRS were found to be associated with preeclampsia. (p: 0.016 and 0.009, respectively). After multivariable adjustment for variables, Na and fragmented QRS remained strongly associated with preeclampsia (OR: 4.787 (1.556-14.720), p: 0.06; 0.941 (0.893-0.992), p: 0.023, respectively). CONCLUSION: This study provides compelling evidence of an association between preeclampsia and fragmented QRS complexes, implicating electrolyte imbalances and hemodynamic stress as potential contributors to myocardial electrical instability in hypertensive disorders of pregnancy. Further research is warranted to validate these findings and improve risk stratification and clinical outcomes in affected women. Number: 2023/4705 Retrospectively Registered.

Is artificial intelligence prepared for the 24-h shifts in the ICU?

Integrating machine learning (ML) into intensive care units (ICUs) can significantly enhance patient care and operational efficiency. ML algorithms can analyze vast amounts of data from electronic health records, physiological monitoring systems, and other medical devices, providing real-time insights and predictive analytics to assist clinicians in decision-making. ML has shown promising results in predictive modeling for patient outcomes, early detection of sepsis, optimizing ventilator settings, and resource allocation. For instance, predictive algorithms have demonstrated high accuracy in forecasting patient deterioration, enabling timely interventions and reducing mortality rates. Despite these advancements, challenges such as data heterogeneity, integration with existing clinical workflows, and the need for transparency and interpretability of ML models persist. The deployment of ML in ICUs also raises ethical and legal considerations regarding patient privacy and the potential for algorithmic biases. For clinicians interested in the early embracing of AI-driven changes in clinical practice, in this review, we discuss the challenges of integrating AI and ML tools in the ICU environment in several steps and issues: 1. Main categories of ML algorithms; 2. From data enabling to ML development; 3. Decision-support systems that will allow patient stratification, accelerating the foresight of adequate individual care; 4. Improving patient outcomes and healthcare efficiency, with positive society and research implications; 5. Risks and barriers to AI-ML application to the healthcare system, including transparency, privacy, and ethical concerns.

Prognostic factors and risk-stratification model of recurrent or metastatic head and neck squamous cell carcinoma treated with cetuximab containing regimen.

BACKGROUND: In recent years, the addition of cetuximab to chemotherapy has improved treatment outcomes for patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). In this study, we present the real-world survival data of R/M HNSCC patients who received cetuximab-containing regimens from thirteen medical centers in Taiwan, as well as a three-level risk stratification model for this patient population. METHODS: This study enrolled R/M HNSCC patients from thirteen medical centers in Taiwan who received cetuximab-containing regimens from January 1, 2017 to June 6, 2022. The cases were divided into a training cohort and a validation cohort based on the start of treatment. Overall survival (OS) was evaluated in both cohorts and exploratory analysis was performed to identify associated adverse clinical and laboratory factors. The results of the exploratory analysis were used to construct a three-level risk stratification prediction model for OS. RESULTS: A total of 1434 patients with R/M HNSCC were enrolled in this study and received cetuximab-containing regimens. The overall population had a median OS of 8.57 months (95% CI: 8.07 - 9.08). Multivariate analysis of the training cohort identified poor ECOG performance status, heavy alcohol consumption, and prior adjuvant CCRT or lack of prior RT as adverse prognostic factors. Comparison of laboratory data between patients with OS≦6 and OS > 6 also revealed unfavorable factors, including increased white blood cell count, decreased hemoglobin level, increased platelet count, increased absolute neutrophil count, decreased absolute lymphocyte count, and increased neutrophil-to-lymphocyte ratio. Using forward prediction, a three-level risk stratification prediction model was constructed using the variables of ECOG performance status, alcohol consumption, skin metastasis, modality of radiation therapy, hemoglobin level, and neutrophil-to-lymphocyte ratio. The median OS in the low-risk, intermediate-risk, and high-risk groups were 12.02 months (95% CI 10.44-13.61), 7.5 months (95% CI 7.33-8.17), and 4.01 months (95% CI 3.94-4.08), respectively, with a log-rank test p-value < 0.001. CONCLUSION: This study presents a three-level risk stratification model with strong prediction ability for OS in R/M HNSCC patients who received cetuximab-containing regimens. The results are based on real-world data and may provide valuable information for clinicians in treatment planning and future drug development.

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Background: Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. Methods: We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. Results: The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. Conclusions: A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.