Previous
Aim:
Low cardiac output syndrome (LCOS) may be improvable; hence, timely detection and intervention are essential. However, no model has been established for the prediction of LCOS onset post non-isolated coronary artery bypass grafting (CABG) surgery. Therefore, this study aimed to develop a machine-learning-based model to predict LCOS after non-isolated CABG.
Methods:
A total of 378 patients who underwent non-isolated CABG at Nanjing First Hospital, China, were retrospectively assessed. Five algorithms [L2 regularized logistic regression (LR), random forest (RF) classifier, extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and support vector machine (SVM)] were employed. Model performance and clinical utility were evaluated using area under the curve (AUC), 10-fold cross-validation, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to assess the model’s interpretability. A web calculator was developed.
Results:
XGB showed superior performance and calibration (AUC: 0.933, 95% CI: 0.903–0.962; Brier score of 0.107), with excellent specificity (0.865), accuracy (0.860), and precision (0.753). In testing, XGB maintained excellent discrimination (AUC: 0.868, 95% CI: 0.799–0.936), best specificity (0.785), accuracy (0.781), and precision (0.614). DCA confirmed clinical usefulness. SHAP analysis identified the ejection fraction, left ventricular end-systolic diameter, and lactate levels as the most influential predictors. The web calculator is accessible via https://lcos-cabg-xgb-model.streamlit.app/
Conclusions:
The developed web-based XGB model effectively predicts LCOS after non-isolated CABG, aiding early risk stratification and detection.
Aim:
Low cardiac output syndrome (LCOS) may be improvable; hence, timely detection and intervention are essential. However, no model has been established for the prediction of LCOS onset post non-isolated coronary artery bypass grafting (CABG) surgery. Therefore, this study aimed to develop a machine-learning-based model to predict LCOS after non-isolated CABG.
Methods:
A total of 378 patients who underwent non-isolated CABG at Nanjing First Hospital, China, were retrospectively assessed. Five algorithms [L2 regularized logistic regression (LR), random forest (RF) classifier, extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and support vector machine (SVM)] were employed. Model performance and clinical utility were evaluated using area under the curve (AUC), 10-fold cross-validation, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to assess the model’s interpretability. A web calculator was developed.
Results:
XGB showed superior performance and calibration (AUC: 0.933, 95% CI: 0.903–0.962; Brier score of 0.107), with excellent specificity (0.865), accuracy (0.860), and precision (0.753). In testing, XGB maintained excellent discrimination (AUC: 0.868, 95% CI: 0.799–0.936), best specificity (0.785), accuracy (0.781), and precision (0.614). DCA confirmed clinical usefulness. SHAP analysis identified the ejection fraction, left ventricular end-systolic diameter, and lactate levels as the most influential predictors. The web calculator is accessible via https://lcos-cabg-xgb-model.streamlit.app/
Conclusions:
The developed web-based XGB model effectively predicts LCOS after non-isolated CABG, aiding early risk stratification and detection.
DOI: https://doi.org/10.37349/ec.2026.1012111
Aim:
This study aimed to evaluate the expression of ectonucleotidases cluster of differentiation 39 (ecto-adenosine triphosphate diphosphohydrolase) (CD39) and cluster of differentiation 73 (ecto-5’-nucleotidase) (CD73) and determine the serum levels of their soluble forms (sCD39 and sCD73) in patients with bronchial asthma compared to healthy controls.
Methods:
A case-control study was conducted, including asthmatic patients and age-matched healthy controls. The expression levels of CD39 and CD73 were assessed using quantitative real-time PCR (qRT-PCR), while the serum levels of their soluble forms were measured using enzyme-linked immunosorbent assay (ELISA). The diagnostic performance of these biomarkers was evaluated using receiver operating characteristic (ROC) curve analysis.
Results:
Compared to controls, asthmatic patients showed significantly lower levels of CD39 expression and its sCD39, whereas CD73 expression and its sCD73 levels were significantly higher. All four biomarkers demonstrated good diagnostic performance in ROC curve with area under the curve (AUC) values greater than 0.8.
Conclusions:
The dysregulation of CD39 and CD73 expressions and their soluble forms is associated with bronchial asthma. These biomarkers may serve as potential diagnostic indicators, with sCD39 showing the best overall diagnostic performance due to its high sensitivity and specificity.
Aim:
This study aimed to evaluate the expression of ectonucleotidases cluster of differentiation 39 (ecto-adenosine triphosphate diphosphohydrolase) (CD39) and cluster of differentiation 73 (ecto-5’-nucleotidase) (CD73) and determine the serum levels of their soluble forms (sCD39 and sCD73) in patients with bronchial asthma compared to healthy controls.
Methods:
A case-control study was conducted, including asthmatic patients and age-matched healthy controls. The expression levels of CD39 and CD73 were assessed using quantitative real-time PCR (qRT-PCR), while the serum levels of their soluble forms were measured using enzyme-linked immunosorbent assay (ELISA). The diagnostic performance of these biomarkers was evaluated using receiver operating characteristic (ROC) curve analysis.
Results:
Compared to controls, asthmatic patients showed significantly lower levels of CD39 expression and its sCD39, whereas CD73 expression and its sCD73 levels were significantly higher. All four biomarkers demonstrated good diagnostic performance in ROC curve with area under the curve (AUC) values greater than 0.8.
Conclusions:
The dysregulation of CD39 and CD73 expressions and their soluble forms is associated with bronchial asthma. These biomarkers may serve as potential diagnostic indicators, with sCD39 showing the best overall diagnostic performance due to its high sensitivity and specificity.
DOI: https://doi.org/10.37349/ei.2026.1003256
Aim:
Cerebral palsy (CP) is one of the most common motor neurodevelopmental disorders, affecting approximately three in every thousand live births in North America. The study aims to investigate and identify the factors influencing manual dexterity performance among children with CP and typically developing (TD) children according to the Manual Ability Classification System (MACS) levels.
Methods:
A total of 100 children aged 4 to 12 years were enrolled, including 50 diagnosed with CP and 50 TD children. Manual dexterity performance was assessed across MACS levels. A Bayesian seemingly unrelated regression (BayesSUR) framework was applied to identify influential factors, explicitly accounting for interrelationships among multiple response variables. This probabilistic approach allowed for robust estimation under uncertainty while incorporating correlations across outcomes.
Results:
The BayesSUR analysis revealed distinct factor influences MACS levels. For children with mild CP (MACS level 1), object type had the strongest effect on response time. For moderately affected children (MACS level 2), direction most strongly influenced movement error, while age impacted both error and success rate. Among severely affected children (MACS level 3) and TD children, gender emerged as the dominant factor influencing response time. However, the low inclusion probabilities of other factors suggest that additional data and validation are warranted.
Conclusions:
The findings highlight the importance of considering both individual characteristics and task-specific factors when designing interventions to improve manual dexterity in children with CP. These results contribute to a better understanding of the key determinants influencing motor performance and may guide the development of more effective therapeutic and rehabilitation strategies. The Trial Registration Number: CTRI/2018/07/014900.
Aim:
Cerebral palsy (CP) is one of the most common motor neurodevelopmental disorders, affecting approximately three in every thousand live births in North America. The study aims to investigate and identify the factors influencing manual dexterity performance among children with CP and typically developing (TD) children according to the Manual Ability Classification System (MACS) levels.
Methods:
A total of 100 children aged 4 to 12 years were enrolled, including 50 diagnosed with CP and 50 TD children. Manual dexterity performance was assessed across MACS levels. A Bayesian seemingly unrelated regression (BayesSUR) framework was applied to identify influential factors, explicitly accounting for interrelationships among multiple response variables. This probabilistic approach allowed for robust estimation under uncertainty while incorporating correlations across outcomes.
Results:
The BayesSUR analysis revealed distinct factor influences MACS levels. For children with mild CP (MACS level 1), object type had the strongest effect on response time. For moderately affected children (MACS level 2), direction most strongly influenced movement error, while age impacted both error and success rate. Among severely affected children (MACS level 3) and TD children, gender emerged as the dominant factor influencing response time. However, the low inclusion probabilities of other factors suggest that additional data and validation are warranted.
Conclusions:
The findings highlight the importance of considering both individual characteristics and task-specific factors when designing interventions to improve manual dexterity in children with CP. These results contribute to a better understanding of the key determinants influencing motor performance and may guide the development of more effective therapeutic and rehabilitation strategies. The Trial Registration Number: CTRI/2018/07/014900.
DOI: https://doi.org/10.37349/edht.2026.101195
Aim:
To examine the behavioral signature of the “Algorithmic Self,” characterizing how users adapt their identity and behaviors in response to algorithmic reinforcement among active digital media users in Pakistan.
Methods:
A cross-sectional quantitative design was employed with 422 adults aged 18–45 years across five major cities. Participants completed a structured online questionnaire capturing demographic data, digital usage patterns, the Algorithmic Exposure Score (AES), and Algorithmic Self Behavioral Signature Scale (ASBSS). Validated instruments assessed social comparison, Fear of Missing Out (FoMO), self-esteem, and digital stress. Data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression in SPSS version 26, with significance set at p < 0.05.
Results:
Participants demonstrated moderate-to-high levels of Algorithmic Self formation, with 39.8% classified in the high category. Higher daily screen time, greater platform diversity, stronger algorithmic trust, and elevated social comparison were associated with higher Algorithmic Self Scores. In multiple linear regression analysis, daily screen time (β = 0.34), social comparison (β = 0.31), algorithmic trust (β = 0.29), and algorithmic exposure (β = 0.28) emerged as significant predictors of Algorithmic Self formation, while FoMO was not a significant predictor (β = 0.11, p = 0.09). The final model explained 56% of the variance in Algorithmic Self formation (R2 = 0.56, adjusted R2 = 0.54, p < 0.001).
Conclusions:
AI-driven digital environments are associated with self-presentation, identity adaptation, and behavioral regulation among Pakistani users. These findings highlight the importance of enhancing digital literacy, improving awareness of algorithmic influence, and further investigating the psychological and societal implications of Algorithmic Self formation in digitally mediated environments.
Aim:
To examine the behavioral signature of the “Algorithmic Self,” characterizing how users adapt their identity and behaviors in response to algorithmic reinforcement among active digital media users in Pakistan.
Methods:
A cross-sectional quantitative design was employed with 422 adults aged 18–45 years across five major cities. Participants completed a structured online questionnaire capturing demographic data, digital usage patterns, the Algorithmic Exposure Score (AES), and Algorithmic Self Behavioral Signature Scale (ASBSS). Validated instruments assessed social comparison, Fear of Missing Out (FoMO), self-esteem, and digital stress. Data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression in SPSS version 26, with significance set at p < 0.05.
Results:
Participants demonstrated moderate-to-high levels of Algorithmic Self formation, with 39.8% classified in the high category. Higher daily screen time, greater platform diversity, stronger algorithmic trust, and elevated social comparison were associated with higher Algorithmic Self Scores. In multiple linear regression analysis, daily screen time (β = 0.34), social comparison (β = 0.31), algorithmic trust (β = 0.29), and algorithmic exposure (β = 0.28) emerged as significant predictors of Algorithmic Self formation, while FoMO was not a significant predictor (β = 0.11, p = 0.09). The final model explained 56% of the variance in Algorithmic Self formation (R2 = 0.56, adjusted R2 = 0.54, p < 0.001).
Conclusions:
AI-driven digital environments are associated with self-presentation, identity adaptation, and behavioral regulation among Pakistani users. These findings highlight the importance of enhancing digital literacy, improving awareness of algorithmic influence, and further investigating the psychological and societal implications of Algorithmic Self formation in digitally mediated environments.
DOI: https://doi.org/10.37349/edht.2026.101196
Aim:
Malnutrition and micronutrient deficiencies remain major public health concerns, while the rising prevalence of diabetes highlights the need for low glycemic index (GI) foods. Pearl millet has potential for value-added product development but is underutilized. This study aimed to optimize a pearl millet-based savory porridge premix formulation based on sensory attributes using a mixture design.
Methods:
A two-component simplex lattice mixture design was used to optimize the proportions of key ingredients based on sensory evaluation. Data were analyzed using response surface modeling, and the optimized formulation was validated through a house-use test. Additionally, the functional properties, proximate composition, iron and zinc content, and cost analysis were analyzed.
Results:
The optimized product consists of 33.524 g of pearl millet flour and 6.476 g of spice mixture, yielding a desirability index of 0.917. Carr’s index (%) and Hausner ratio of the optimized premix were 20.95 ± 0.19 and 1.2650 ± 0.0031. The fiber, iron, and zinc content of the premix were 3.63 g/100 g, 4.216 mg/100 g, and 1.287 mg/100 g, respectively. The cost analysis of the optimized premix demonstrated that the product is economically viable as it can be manufactured at Indian Rupee (INR) 2.32 per 40 g pouch. The house-use test results indicated that 52.7% of participants reported “like very much”, while 27.1% expressed “like” for the product. However, the strongly purchase intention for the optimized product was reported by only approximately 27.1% of the participants.
Conclusions:
The optimized premix showed acceptable nutritional quality, sensory attributes, cost feasibility, and a low estimated GI, indicating its potential for health-conscious and at-risk populations.
Aim:
Malnutrition and micronutrient deficiencies remain major public health concerns, while the rising prevalence of diabetes highlights the need for low glycemic index (GI) foods. Pearl millet has potential for value-added product development but is underutilized. This study aimed to optimize a pearl millet-based savory porridge premix formulation based on sensory attributes using a mixture design.
Methods:
A two-component simplex lattice mixture design was used to optimize the proportions of key ingredients based on sensory evaluation. Data were analyzed using response surface modeling, and the optimized formulation was validated through a house-use test. Additionally, the functional properties, proximate composition, iron and zinc content, and cost analysis were analyzed.
Results:
The optimized product consists of 33.524 g of pearl millet flour and 6.476 g of spice mixture, yielding a desirability index of 0.917. Carr’s index (%) and Hausner ratio of the optimized premix were 20.95 ± 0.19 and 1.2650 ± 0.0031. The fiber, iron, and zinc content of the premix were 3.63 g/100 g, 4.216 mg/100 g, and 1.287 mg/100 g, respectively. The cost analysis of the optimized premix demonstrated that the product is economically viable as it can be manufactured at Indian Rupee (INR) 2.32 per 40 g pouch. The house-use test results indicated that 52.7% of participants reported “like very much”, while 27.1% expressed “like” for the product. However, the strongly purchase intention for the optimized product was reported by only approximately 27.1% of the participants.
Conclusions:
The optimized premix showed acceptable nutritional quality, sensory attributes, cost feasibility, and a low estimated GI, indicating its potential for health-conscious and at-risk populations.
DOI: https://doi.org/10.37349/eff.2026.1010167
The brain–gut axis, first described in the 19th century, refers to the complex bidirectional communication network between the central nervous system and the gastrointestinal tract. This dynamic system operates through neuronal, endocrine, and immune pathways. It has since expanded to include the influence of gut microbiota, given its significant role in gut motility disorders and neurological diseases. The intricate relationship involves multiple signaling mechanisms, including toll-like receptors, nuclear factor-kappa B, α-synuclein, the hypothalamic–pituitary–adrenal axis, and vagal signaling. Dysregulation of the brain–gut axis has been implicated in numerous neurological conditions, including Parkinson’s disease, stroke, multiple sclerosis, autism spectrum disorder, spinal cord injury, and peripheral neuropathies, many of which present with well-recognized gastrointestinal manifestations. Conversely, neurological sequelae are frequently associated with primary gastrointestinal disorders such as inflammatory bowel disease, celiac disease, and hepatic failure. This narrative literature review aims to examine the epidemiology, clinical presentation, and pathogenesis of common neurological and gastrointestinal diseases through the lens of the brain–gut axis. By highlighting the interconnected metabolic, immune, and physiological mechanisms underlying these conditions, this review seeks to promote a more integrated understanding of disease processes and to support improved diagnostic strategies, therapeutic approaches, and long-term patient outcomes.
The brain–gut axis, first described in the 19th century, refers to the complex bidirectional communication network between the central nervous system and the gastrointestinal tract. This dynamic system operates through neuronal, endocrine, and immune pathways. It has since expanded to include the influence of gut microbiota, given its significant role in gut motility disorders and neurological diseases. The intricate relationship involves multiple signaling mechanisms, including toll-like receptors, nuclear factor-kappa B, α-synuclein, the hypothalamic–pituitary–adrenal axis, and vagal signaling. Dysregulation of the brain–gut axis has been implicated in numerous neurological conditions, including Parkinson’s disease, stroke, multiple sclerosis, autism spectrum disorder, spinal cord injury, and peripheral neuropathies, many of which present with well-recognized gastrointestinal manifestations. Conversely, neurological sequelae are frequently associated with primary gastrointestinal disorders such as inflammatory bowel disease, celiac disease, and hepatic failure. This narrative literature review aims to examine the epidemiology, clinical presentation, and pathogenesis of common neurological and gastrointestinal diseases through the lens of the brain–gut axis. By highlighting the interconnected metabolic, immune, and physiological mechanisms underlying these conditions, this review seeks to promote a more integrated understanding of disease processes and to support improved diagnostic strategies, therapeutic approaches, and long-term patient outcomes.
DOI: https://doi.org/10.37349/edd.2026.1005126
Aim:
Mango (Mangifera indica L.) is a nutrient-rich tropical fruit with high economic value, but it faces significant post-harvest preservation challenges. This study aimed to optimize the fermentation of drinking vinegar from mango to develop products with high nutritional value and desirable sensory properties.
Methods:
The effects of the raw material-to-water ratio, ethanol concentration, pH, and TSS on total acid content were determined using the titration method. A Box-Behnken design was applied to optimize inoculum density, fermentation time, and temperature. After fermentation, mango juice was incorporated to enhance flavor, and the product quality was analyzed through physicochemical, microbiological, and antioxidant activity assessments. A consumer preference test was conducted using an untrained sensory panel.
Results:
The optimal fermentation conditions were determined as follows: a mango juice-to-water ratio of 1:10 (v/v), ethanol concentration of 4% (v/v), total soluble solids of 10 °Brix, initial pH of 6, and an inoculum level of 1% (v/v), corresponding to a cell density of 6.7 × 105 CFU/mL, at 30.08°C for 9.59 days. Under these conditions, the final product, with 40% juice supplementation, contained 13.30 g/L acetic acid, 18.91 g/L reducing sugars, 52.79% antioxidant capacity, and 827.79 mg GAE/L total phenolic content.
Conclusions:
The optimized fermentation conditions for producing drinking vinegar from mango juice can be applied to create high-quality mango vinegar from grade-2 mangoes with desirable nutritional and sensory properties while ensuring food safety.
Aim:
Mango (Mangifera indica L.) is a nutrient-rich tropical fruit with high economic value, but it faces significant post-harvest preservation challenges. This study aimed to optimize the fermentation of drinking vinegar from mango to develop products with high nutritional value and desirable sensory properties.
Methods:
The effects of the raw material-to-water ratio, ethanol concentration, pH, and TSS on total acid content were determined using the titration method. A Box-Behnken design was applied to optimize inoculum density, fermentation time, and temperature. After fermentation, mango juice was incorporated to enhance flavor, and the product quality was analyzed through physicochemical, microbiological, and antioxidant activity assessments. A consumer preference test was conducted using an untrained sensory panel.
Results:
The optimal fermentation conditions were determined as follows: a mango juice-to-water ratio of 1:10 (v/v), ethanol concentration of 4% (v/v), total soluble solids of 10 °Brix, initial pH of 6, and an inoculum level of 1% (v/v), corresponding to a cell density of 6.7 × 105 CFU/mL, at 30.08°C for 9.59 days. Under these conditions, the final product, with 40% juice supplementation, contained 13.30 g/L acetic acid, 18.91 g/L reducing sugars, 52.79% antioxidant capacity, and 827.79 mg GAE/L total phenolic content.
Conclusions:
The optimized fermentation conditions for producing drinking vinegar from mango juice can be applied to create high-quality mango vinegar from grade-2 mangoes with desirable nutritional and sensory properties while ensuring food safety.
DOI: https://doi.org/10.37349/eff.2026.1010168
Background:
Breast cancer encompasses heterogeneous pathological and molecular subtypes with distinct aetiologies and clinical outcomes. Although cigarette smoking is an established carcinogenic exposure, its subtype-specific associations and molecular effects in breast cancer remain insufficiently clarified. This systematic review synthesizes epidemiological, molecular, and prognostic evidence on how active cigarette smoking influences the risk of specific breast cancer subtypes.
Methods:
We conducted a systematic review, including observational and translational human studies that assessed active cigarette smoking in relation to breast cancer subtypes defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, or by intrinsic molecular classifications. We also included studies evaluating smoking-associated molecular alterations within breast tumors.
Results:
Nineteen studies met the eligibility criteria. Epidemiological evidence suggested a possible modest increase in the risk of luminal/ER-positive breast cancer, particularly among women with longer smoking duration, heavier cumulative exposure, or smoking initiation before first full-term pregnancy; however, the pooled meta-analysis for current vs. never smoking was not statistically significant. No meaningful association was identified for triple-negative breast cancer (TNBC), and findings for HER2-positive breast cancer were heterogeneous. Molecular studies were associated with smoking-related changes in promoter DNA methylation, higher overall mutational burden, increased genomic instability, altered immune-cell infiltration within the tumor microenvironment, and conversion of receptor phenotype—especially toward HER2 positivity—suggesting a potential association with more aggressive tumor characteristics. Prognostic studies generally showed poorer overall survival and a higher risk of disease recurrence among smokers.
Discussion:
Active cigarette smoking may be associated with a possible modest increase in the risk of luminal/ER-positive breast cancer, while being associated with molecular alterations linked to more aggressive tumor phenotypes and poorer clinical outcomes.
Background:
Breast cancer encompasses heterogeneous pathological and molecular subtypes with distinct aetiologies and clinical outcomes. Although cigarette smoking is an established carcinogenic exposure, its subtype-specific associations and molecular effects in breast cancer remain insufficiently clarified. This systematic review synthesizes epidemiological, molecular, and prognostic evidence on how active cigarette smoking influences the risk of specific breast cancer subtypes.
Methods:
We conducted a systematic review, including observational and translational human studies that assessed active cigarette smoking in relation to breast cancer subtypes defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, or by intrinsic molecular classifications. We also included studies evaluating smoking-associated molecular alterations within breast tumors.
Results:
Nineteen studies met the eligibility criteria. Epidemiological evidence suggested a possible modest increase in the risk of luminal/ER-positive breast cancer, particularly among women with longer smoking duration, heavier cumulative exposure, or smoking initiation before first full-term pregnancy; however, the pooled meta-analysis for current vs. never smoking was not statistically significant. No meaningful association was identified for triple-negative breast cancer (TNBC), and findings for HER2-positive breast cancer were heterogeneous. Molecular studies were associated with smoking-related changes in promoter DNA methylation, higher overall mutational burden, increased genomic instability, altered immune-cell infiltration within the tumor microenvironment, and conversion of receptor phenotype—especially toward HER2 positivity—suggesting a potential association with more aggressive tumor characteristics. Prognostic studies generally showed poorer overall survival and a higher risk of disease recurrence among smokers.
Discussion:
Active cigarette smoking may be associated with a possible modest increase in the risk of luminal/ER-positive breast cancer, while being associated with molecular alterations linked to more aggressive tumor phenotypes and poorer clinical outcomes.
DOI: https://doi.org/10.37349/etat.2026.1002376
Aim:
This study investigated the anti-inflammatory effect of the extract and fractions of Eucalyptus camaldulensis and also profiled the secondary metabolites of its most active fraction.
Methods:
The leaves of E. camaldulensis were collected, authenticated and extracted with methanol. The extract (100, 200, and 400 mg/kg), normal saline (negative control), and aspirin (positive control) were administered orally to egg-induced paw oedema rats of five groups of five rats each. The extract was partitioned, and each of the solvent fractions was assayed for its anti-inflammatory activity. The results obtained were subjected to one-way analysis of variance (ANOVA) followed by Bonferroni post hoc tests, and p < 0.05 was considered significant. Also, the most active fraction was subjected to gas chromatography-mass spectrometry (GC-MS) analysis.
Results:
The extract at 100 mg/kg demonstrated the best anti-inflammatory effect at 29%, while the n-hexane (N-HEX) fraction gave the highest inflammatory inhibition at 43%. α-Phellandrene, o-cymene, n-hexadecanoic acid, and beta-sitosterol were identified as the most abundant compounds in the N-HEX fraction.
Conclusions:
The study concluded that the methanol extract of E. camaldulensis possesses good anti-inflammatory properties, and its non-polar fraction was responsible for the observed activity. Bioassay-guided purification of anti-inflammatory constituents of the N-HEX fraction is recommended for future studies.
Aim:
This study investigated the anti-inflammatory effect of the extract and fractions of Eucalyptus camaldulensis and also profiled the secondary metabolites of its most active fraction.
Methods:
The leaves of E. camaldulensis were collected, authenticated and extracted with methanol. The extract (100, 200, and 400 mg/kg), normal saline (negative control), and aspirin (positive control) were administered orally to egg-induced paw oedema rats of five groups of five rats each. The extract was partitioned, and each of the solvent fractions was assayed for its anti-inflammatory activity. The results obtained were subjected to one-way analysis of variance (ANOVA) followed by Bonferroni post hoc tests, and p < 0.05 was considered significant. Also, the most active fraction was subjected to gas chromatography-mass spectrometry (GC-MS) analysis.
Results:
The extract at 100 mg/kg demonstrated the best anti-inflammatory effect at 29%, while the n-hexane (N-HEX) fraction gave the highest inflammatory inhibition at 43%. α-Phellandrene, o-cymene, n-hexadecanoic acid, and beta-sitosterol were identified as the most abundant compounds in the N-HEX fraction.
Conclusions:
The study concluded that the methanol extract of E. camaldulensis possesses good anti-inflammatory properties, and its non-polar fraction was responsible for the observed activity. Bioassay-guided purification of anti-inflammatory constituents of the N-HEX fraction is recommended for future studies.
DOI: https://doi.org/10.37349/eds.2026.1008163
Gastric mucosa-associated lymphoid tissue (MALT) lymphoma is an indolent extranodal B-cell lymphoma that arises in close association with chronic Helicobacter pylori (H. pylori) infection and represents a unique paradigm of infection-driven oncogenesis. Persistent H. pylori colonization induces organized lymphoid tissue within the normally lymphoid-poor gastric mucosa, promoting sustained antigen-dependent T-cell-mediated B-cell proliferation and eventual clonal transformation. In contrast to many other lymphoid malignancies, early-stage gastric MALT lymphoma often regresses following microbial eradication, highlighting the central role of antigenic stimulation in disease pathogenesis. This review provides a contemporary overview of H. pylori-associated gastric MALT lymphoma, integrating epidemiology, molecular and immunologic mechanisms of lymphomagenesis, diagnostic evaluation, and modern management strategies. This review gives particular attention to molecular determinants of treatment response, including the t(11;18)(q21;q21)/API2-MALT1 translocation and other NF-κB-activating alterations that promote antigen-independent growth and resistance to eradication therapy. Current therapeutic approaches are reviewed, including antibiotic eradication regimens, radiotherapy, immunotherapy, and systemic treatment strategies for refractory or disseminated disease. By integrating mechanistic insights with clinical practice, this review highlights a precision-based framework for the diagnosis, risk stratification, and management of gastric MALT lymphoma in the modern era.
Gastric mucosa-associated lymphoid tissue (MALT) lymphoma is an indolent extranodal B-cell lymphoma that arises in close association with chronic Helicobacter pylori (H. pylori) infection and represents a unique paradigm of infection-driven oncogenesis. Persistent H. pylori colonization induces organized lymphoid tissue within the normally lymphoid-poor gastric mucosa, promoting sustained antigen-dependent T-cell-mediated B-cell proliferation and eventual clonal transformation. In contrast to many other lymphoid malignancies, early-stage gastric MALT lymphoma often regresses following microbial eradication, highlighting the central role of antigenic stimulation in disease pathogenesis. This review provides a contemporary overview of H. pylori-associated gastric MALT lymphoma, integrating epidemiology, molecular and immunologic mechanisms of lymphomagenesis, diagnostic evaluation, and modern management strategies. This review gives particular attention to molecular determinants of treatment response, including the t(11;18)(q21;q21)/API2-MALT1 translocation and other NF-κB-activating alterations that promote antigen-independent growth and resistance to eradication therapy. Current therapeutic approaches are reviewed, including antibiotic eradication regimens, radiotherapy, immunotherapy, and systemic treatment strategies for refractory or disseminated disease. By integrating mechanistic insights with clinical practice, this review highlights a precision-based framework for the diagnosis, risk stratification, and management of gastric MALT lymphoma in the modern era.
DOI: https://doi.org/10.37349/edd.2026.1005125
This article belongs to the special issue Helicobacter Pylori and Infection: Genomics, Diagnosis, Pathogenesis, Antibiotic Resistance, Microbiota, Cancer, Prevention and Therapeutics
Aim:
Tremor is one of the most common neurological movement disorders, arising from dysfunction in the neuromuscular system. However, comprehensive analyses of peripheral blood elements, red blood cells (RBC) and white blood cells (WBC) counts, as well as liver and kidney function in patients with tremor remain limited. This cross-sectional study investigated alterations in serum elements, complete blood counts, and liver function in patients with tremor. The study sought to identify independent risk factors and evaluate their diagnostic performance.
Methods:
Blood samples from 79 patients with tremor and 82 healthy controls were analyzed. Serum elements, RBC, WBC, platelet (PLT), liver function, and renal function were measured using the QL8000 element analyzer, XN 2800 automated hematology analyzer, and Roche Cobas 8000 system.
Results:
Serum copper (Cu) and lead (Pb) levels were significantly elevated in tremor patients. These patients also showed increased monocytes, decreased eosinophils, and impaired liver function, including elevated aspartate aminotransferase and globulin with reduced albumin.
Conclusions:
Tremor patients show distinct alterations in Cu, Pb, monocyte counts, eosinophil counts, and liver function markers. These findings suggest that these parameters may serve as potential diagnostic indicators and therapeutic targets. Cu and Pb were identified as independent risk factors, and their combination significantly improved diagnostic efficiency.
Aim:
Tremor is one of the most common neurological movement disorders, arising from dysfunction in the neuromuscular system. However, comprehensive analyses of peripheral blood elements, red blood cells (RBC) and white blood cells (WBC) counts, as well as liver and kidney function in patients with tremor remain limited. This cross-sectional study investigated alterations in serum elements, complete blood counts, and liver function in patients with tremor. The study sought to identify independent risk factors and evaluate their diagnostic performance.
Methods:
Blood samples from 79 patients with tremor and 82 healthy controls were analyzed. Serum elements, RBC, WBC, platelet (PLT), liver function, and renal function were measured using the QL8000 element analyzer, XN 2800 automated hematology analyzer, and Roche Cobas 8000 system.
Results:
Serum copper (Cu) and lead (Pb) levels were significantly elevated in tremor patients. These patients also showed increased monocytes, decreased eosinophils, and impaired liver function, including elevated aspartate aminotransferase and globulin with reduced albumin.
Conclusions:
Tremor patients show distinct alterations in Cu, Pb, monocyte counts, eosinophil counts, and liver function markers. These findings suggest that these parameters may serve as potential diagnostic indicators and therapeutic targets. Cu and Pb were identified as independent risk factors, and their combination significantly improved diagnostic efficiency.
DOI: https://doi.org/10.37349/emed.2026.1001414
Artificial intelligence (AI) has rapidly advanced in radiology, demonstrating high performance across a wide range of diagnostic tasks. However, clinical adoption remains slower and more uneven than anticipated. This discrepancy reflects a fundamental gap between algorithm validation and clinical implementation. Current validation strategies primarily rely on controlled datasets and performance metrics such as accuracy and area under the curve, which often fail to capture the complexity of clinical environments. This article examines the nature of this “validation gap” and argues that it reflects a broader structural mismatch between how AI systems are evaluated and how clinical care operates. We propose a conceptual framework comprising three levels of validation: technical validity, workflow validity, and clinical validity. While most studies focus on technical performance, limited attention is given to integration into clinical workflows and impact on patient outcomes. Key factors contributing to this gap include limited generalizability across diverse populations and imaging protocols, poor alignment with clinical workflows, and the underrepresentation of uncertainty in model outputs. These limitations hinder effective implementation and may reduce trust in AI systems. Bridging this gap requires a shift toward more comprehensive validation strategies, including multicenter and prospective studies, improved workflow integration, and explicit incorporation of uncertainty and human–AI interaction. Ultimately, the clinical value of AI in radiology should be assessed not only by its performance in controlled settings but also by its ability to support decision-making and improve patient outcomes in real-world practice.
Artificial intelligence (AI) has rapidly advanced in radiology, demonstrating high performance across a wide range of diagnostic tasks. However, clinical adoption remains slower and more uneven than anticipated. This discrepancy reflects a fundamental gap between algorithm validation and clinical implementation. Current validation strategies primarily rely on controlled datasets and performance metrics such as accuracy and area under the curve, which often fail to capture the complexity of clinical environments. This article examines the nature of this “validation gap” and argues that it reflects a broader structural mismatch between how AI systems are evaluated and how clinical care operates. We propose a conceptual framework comprising three levels of validation: technical validity, workflow validity, and clinical validity. While most studies focus on technical performance, limited attention is given to integration into clinical workflows and impact on patient outcomes. Key factors contributing to this gap include limited generalizability across diverse populations and imaging protocols, poor alignment with clinical workflows, and the underrepresentation of uncertainty in model outputs. These limitations hinder effective implementation and may reduce trust in AI systems. Bridging this gap requires a shift toward more comprehensive validation strategies, including multicenter and prospective studies, improved workflow integration, and explicit incorporation of uncertainty and human–AI interaction. Ultimately, the clinical value of AI in radiology should be assessed not only by its performance in controlled settings but also by its ability to support decision-making and improve patient outcomes in real-world practice.
DOI: https://doi.org/10.37349/emed.2026.1001412
This article belongs to the special issue Artificial Intelligence in Precision Imaging: Innovations Shaping the Future of Clinical Diagnostics
Artificial intelligence (AI) is transforming clinical decision-making across cranio-maxillofacial trauma, oral health, and systemic disease. These domains are increasingly recognised as biologically and clinically interconnected, yet they are often studied and managed independently. This perspective introduces the concept of an integrative triangle linking facial trauma, oral health, and systemic disease, with AI serving as the computational bridge that enables cross-domain modelling and coordinated care. AI applications within this framework include imaging-based fracture detection, patient-specific implant design, automated oral disease diagnosis, multimodal risk prediction, and longitudinal outcome modelling. By integrating imaging, clinical, laboratory, and behavioural data, AI can identify shared inflammatory and metabolic pathways influencing trauma recovery and chronic disease progression. This closed-loop paradigm supports continuous learning, allowing outcomes in one domain to inform prediction and intervention in the others. The integrative triangle provides a translational roadmap for precision medicine, moving from isolated prediction toward coordinated prevention and intervention. Future development will require multimodal data integration, prospective validation, and responsible governance to ensure explainable and equitable AI deployment. This framework positions facial trauma and oral health as central components of systemic precision medicine and highlights AI as a catalyst for integrated, patient-centred care.
Artificial intelligence (AI) is transforming clinical decision-making across cranio-maxillofacial trauma, oral health, and systemic disease. These domains are increasingly recognised as biologically and clinically interconnected, yet they are often studied and managed independently. This perspective introduces the concept of an integrative triangle linking facial trauma, oral health, and systemic disease, with AI serving as the computational bridge that enables cross-domain modelling and coordinated care. AI applications within this framework include imaging-based fracture detection, patient-specific implant design, automated oral disease diagnosis, multimodal risk prediction, and longitudinal outcome modelling. By integrating imaging, clinical, laboratory, and behavioural data, AI can identify shared inflammatory and metabolic pathways influencing trauma recovery and chronic disease progression. This closed-loop paradigm supports continuous learning, allowing outcomes in one domain to inform prediction and intervention in the others. The integrative triangle provides a translational roadmap for precision medicine, moving from isolated prediction toward coordinated prevention and intervention. Future development will require multimodal data integration, prospective validation, and responsible governance to ensure explainable and equitable AI deployment. This framework positions facial trauma and oral health as central components of systemic precision medicine and highlights AI as a catalyst for integrated, patient-centred care.
DOI: https://doi.org/10.37349/emed.2026.1001413
Recent studies argue that other physiological solutions are superior to normal saline, which is due to their physiological features, better outcomes in critical care, and lower risk of hyperchloremia and acidosis; nonetheless, it is still a mystery how normal saline has dominated the field of fluid therapy worldwide. Moreover, there is an ongoing debate on whether harm to human health may limit its spread in the future. Additionally, new evidence revealed some of the deleterious effects of normal saline, including coagulopathy, metabolic acidosis, acute kidney injury (AKI), and higher mortality in ICU. The predominant cause for these outcomes appears to be the excess chloride concentration of normal saline relative to plasma. Therefore, it appears relevant to suggest that a normal saline solution should be normalized to that of human serum to overcome these pitfalls. An ideal normal saline solution shall be similar to human serum in its pH, osmolarity, and content of sodium, chloride, and essential minerals.
Recent studies argue that other physiological solutions are superior to normal saline, which is due to their physiological features, better outcomes in critical care, and lower risk of hyperchloremia and acidosis; nonetheless, it is still a mystery how normal saline has dominated the field of fluid therapy worldwide. Moreover, there is an ongoing debate on whether harm to human health may limit its spread in the future. Additionally, new evidence revealed some of the deleterious effects of normal saline, including coagulopathy, metabolic acidosis, acute kidney injury (AKI), and higher mortality in ICU. The predominant cause for these outcomes appears to be the excess chloride concentration of normal saline relative to plasma. Therefore, it appears relevant to suggest that a normal saline solution should be normalized to that of human serum to overcome these pitfalls. An ideal normal saline solution shall be similar to human serum in its pH, osmolarity, and content of sodium, chloride, and essential minerals.
DOI: https://doi.org/10.37349/emed.2026.1001411
Aim:
In the context of increasing global demand for protein, edible insects are gaining attention as a sustainable food source. Rhynchophorus phoenicis larvae, a promising edible insect, are rich in proteins and lipids. However, their high lipid content limits food applications and stability. This study evaluated defatting methods on nutritional, techno-functional, and physicochemical properties of R. phoenicis larvae powders and oils.
Methods:
Cooking-pressing, hexane, hexane:isopropanol, and ethanol defatting methods were investigated. Parameters included macronutrient composition (moisture, carbohydrates, lipids, proteins, ash), techno-functional properties such as water absorption capacity (WAC), oil absorption capacity (OAC), and emulsifying capacity (EC), as well as physicochemical indices including acid value (AV), peroxide value (PV), anisidine value (AnV), and TBARS.
Results:
Defatted powders obtained using hexane and the hexane–isopropanol mixture showed the highest protein contents, reaching 77.63 ± 1.10 g/100 g and 71.86 ± 0.54 g/100 g, respectively. Cooking–press defatted powder exhibited the highest EC (66.70 ± 2.89%), while ethanol-defatted powder showed the highest OAC (3.11 ± 0.09 mL/g). WAC varied significantly depending on the extraction solvent, with the hexane–isopropanol mixture yielding the highest value (1.49 ± 0.05 mL/g) and ethanol-defatted powder the lowest (1.10 ± 0.02 mL/g). Physicochemical indices of R. phoenicis powders remained below critical thresholds, indicating good quality. In contrast, oils extracted by hexane and hexane:isopropanol showed elevated primary oxidation indices, requiring antioxidant protection and optimized storage conditions for long-term stability.
Conclusions:
Defatting method influences the nutritional, physicochemical, and techno-functional properties of R. phoenicis larvae.
Aim:
In the context of increasing global demand for protein, edible insects are gaining attention as a sustainable food source. Rhynchophorus phoenicis larvae, a promising edible insect, are rich in proteins and lipids. However, their high lipid content limits food applications and stability. This study evaluated defatting methods on nutritional, techno-functional, and physicochemical properties of R. phoenicis larvae powders and oils.
Methods:
Cooking-pressing, hexane, hexane:isopropanol, and ethanol defatting methods were investigated. Parameters included macronutrient composition (moisture, carbohydrates, lipids, proteins, ash), techno-functional properties such as water absorption capacity (WAC), oil absorption capacity (OAC), and emulsifying capacity (EC), as well as physicochemical indices including acid value (AV), peroxide value (PV), anisidine value (AnV), and TBARS.
Results:
Defatted powders obtained using hexane and the hexane–isopropanol mixture showed the highest protein contents, reaching 77.63 ± 1.10 g/100 g and 71.86 ± 0.54 g/100 g, respectively. Cooking–press defatted powder exhibited the highest EC (66.70 ± 2.89%), while ethanol-defatted powder showed the highest OAC (3.11 ± 0.09 mL/g). WAC varied significantly depending on the extraction solvent, with the hexane–isopropanol mixture yielding the highest value (1.49 ± 0.05 mL/g) and ethanol-defatted powder the lowest (1.10 ± 0.02 mL/g). Physicochemical indices of R. phoenicis powders remained below critical thresholds, indicating good quality. In contrast, oils extracted by hexane and hexane:isopropanol showed elevated primary oxidation indices, requiring antioxidant protection and optimized storage conditions for long-term stability.
Conclusions:
Defatting method influences the nutritional, physicochemical, and techno-functional properties of R. phoenicis larvae.
DOI: https://doi.org/10.37349/eff.2026.1010165
A variety of endocrine-relevant contaminant categories are now chronically co-exposed to the human population through the food chain, including direct dietary intake, packaging migration, and drinking-water pathway, such as per- and polyfluoroalkyl substances (PFAS), bisphenol analogues/phthalates, and micro- and nanoplastics (MNPs). There exists a fundamental incongruity between the current regulation of chemicals and our exposures to them. Regulatory agencies currently tend to test substances individually, but rising evidence on population-based studies shows that combined exposures are leading to thyroid ailments, metabolic issues, and negative reproductive outcomes. This review brings together mechanistic, toxicological, and human evidence that these structurally diverse contaminants functionally intersect three endocrine- and barrier-relevant signaling pathways: (i) the thyroid axis, (ii) nuclear receptor and steroidogenic signaling, (iii) gut barrier-inflammation circuits. Since the mixtures encountered in the real world cause cumulative stress on these common pathways, it is suggested that a pathway-based measurement be developed: the Pathway Disruption Load (PDL). PDL is operationalized: Tier 1 comprises pathway-specific biomarkers (TSH, free T4, sex-steroid panels, zonulin, LBP). Tier 2 is performed by applying receptor/enzyme assays (ER/AR/TR, TPO inhibition) of pertinent matrices (food extracts, water, serum) to measure the total endocrine activity, including unknown co-migrants. A combination of Tier 1 biological response and Tier 2 functional burden gives a realistic and chemical-agnostic foundation for cumulative risk evaluation, and provides a foodomics-relevant bridge between food-matrix signals (e.g., packaging/food extracts) and human biomonitoring/omics-derived biomarkers, and it also agrees with the current EFSA mixture guidance and key-characteristics frameworks. Operational priorities are re-analysis of biomarker-rich cohorts, pathway-level panels, and mixture toxicology at human-relevant doses.
A variety of endocrine-relevant contaminant categories are now chronically co-exposed to the human population through the food chain, including direct dietary intake, packaging migration, and drinking-water pathway, such as per- and polyfluoroalkyl substances (PFAS), bisphenol analogues/phthalates, and micro- and nanoplastics (MNPs). There exists a fundamental incongruity between the current regulation of chemicals and our exposures to them. Regulatory agencies currently tend to test substances individually, but rising evidence on population-based studies shows that combined exposures are leading to thyroid ailments, metabolic issues, and negative reproductive outcomes. This review brings together mechanistic, toxicological, and human evidence that these structurally diverse contaminants functionally intersect three endocrine- and barrier-relevant signaling pathways: (i) the thyroid axis, (ii) nuclear receptor and steroidogenic signaling, (iii) gut barrier-inflammation circuits. Since the mixtures encountered in the real world cause cumulative stress on these common pathways, it is suggested that a pathway-based measurement be developed: the Pathway Disruption Load (PDL). PDL is operationalized: Tier 1 comprises pathway-specific biomarkers (TSH, free T4, sex-steroid panels, zonulin, LBP). Tier 2 is performed by applying receptor/enzyme assays (ER/AR/TR, TPO inhibition) of pertinent matrices (food extracts, water, serum) to measure the total endocrine activity, including unknown co-migrants. A combination of Tier 1 biological response and Tier 2 functional burden gives a realistic and chemical-agnostic foundation for cumulative risk evaluation, and provides a foodomics-relevant bridge between food-matrix signals (e.g., packaging/food extracts) and human biomonitoring/omics-derived biomarkers, and it also agrees with the current EFSA mixture guidance and key-characteristics frameworks. Operational priorities are re-analysis of biomarker-rich cohorts, pathway-level panels, and mixture toxicology at human-relevant doses.
DOI: https://doi.org/10.37349/eff.2026.1010166
This article belongs to the special issue Food Contaminants: Analysis, Occurrence and Risk Assessment
Aim:
To evaluate ultrasound-derived congestion phenotypes in acute decompensated heart failure with preserved ejection fraction (HFpEF) and their association with cardiac remodeling and in-hospital outcomes.
Methods:
This prospective study included 235 patients (median age 77.0 years, 75.3% women) with acute decompensated HFpEF. Within 2 hours of admission, all patients underwent echocardiography, lung ultrasound (B-lines), venous excess ultrasound score (VExUS) assessment, and bioimpedance analysis. Patients were classified into three phenotypes based on pulmonary (B-lines > 3) and systemic venous congestion (VExUS): low-low (no significant pulmonary or systemic congestion), pulmonary-dominant, and mixed severe. The primary endpoint was in-hospital mortality.
Results:
Moderate-to-severe venous congestion (VExUS grade 2–3) was present in 60.8% of patients. The mixed severe phenotype predominated (60.9%) and was associated with higher body mass index (BMI) and waist (p < 0.001). This group demonstrated more advanced cardiac dysfunction, including higher E/e’ (14.9 vs. 11.9; p < 0.001), greater left atrial remodeling (left atrial volume index 45.0 vs. 39.0 mL/m2; p < 0.001), and increased left ventricular mass index (p = 0.010). Right ventricular (RV) involvement was more pronounced, with lower TAPSE (18.0 vs. 20.0 mm; p < 0.001) and higher tricuspid regurgitation velocity (p < 0.001). Markers of congestion showed a gradient, with higher NT-proBNP (3,072.5 vs. 1,197.0 pg/mL; p < 0.001), increased extracellular water (129% vs. 101%; p < 0.001), and lower phase angle (4.9 vs. 5.5; p < 0.001). In-hospital mortality was highest in the mixed severe phenotype [11.2% vs. 3.0% and 1.7%; p = 0.039; odds ratio (OR) 5.67]. B-lines correlated with tricuspid regurgitation velocity, E/e’, and extracellular water (all r ≥ 0.50).
Conclusions:
Ultrasound-derived congestion phenotyping in HFpEF identifies distinct profiles associated with atrial and ventricular remodeling and worse in-hospital outcomes. Future studies are required to determine whether phenotype-guided decongestive strategies can improve outcomes beyond risk stratification.
Aim:
To evaluate ultrasound-derived congestion phenotypes in acute decompensated heart failure with preserved ejection fraction (HFpEF) and their association with cardiac remodeling and in-hospital outcomes.
Methods:
This prospective study included 235 patients (median age 77.0 years, 75.3% women) with acute decompensated HFpEF. Within 2 hours of admission, all patients underwent echocardiography, lung ultrasound (B-lines), venous excess ultrasound score (VExUS) assessment, and bioimpedance analysis. Patients were classified into three phenotypes based on pulmonary (B-lines > 3) and systemic venous congestion (VExUS): low-low (no significant pulmonary or systemic congestion), pulmonary-dominant, and mixed severe. The primary endpoint was in-hospital mortality.
Results:
Moderate-to-severe venous congestion (VExUS grade 2–3) was present in 60.8% of patients. The mixed severe phenotype predominated (60.9%) and was associated with higher body mass index (BMI) and waist (p < 0.001). This group demonstrated more advanced cardiac dysfunction, including higher E/e’ (14.9 vs. 11.9; p < 0.001), greater left atrial remodeling (left atrial volume index 45.0 vs. 39.0 mL/m2; p < 0.001), and increased left ventricular mass index (p = 0.010). Right ventricular (RV) involvement was more pronounced, with lower TAPSE (18.0 vs. 20.0 mm; p < 0.001) and higher tricuspid regurgitation velocity (p < 0.001). Markers of congestion showed a gradient, with higher NT-proBNP (3,072.5 vs. 1,197.0 pg/mL; p < 0.001), increased extracellular water (129% vs. 101%; p < 0.001), and lower phase angle (4.9 vs. 5.5; p < 0.001). In-hospital mortality was highest in the mixed severe phenotype [11.2% vs. 3.0% and 1.7%; p = 0.039; odds ratio (OR) 5.67]. B-lines correlated with tricuspid regurgitation velocity, E/e’, and extracellular water (all r ≥ 0.50).
Conclusions:
Ultrasound-derived congestion phenotyping in HFpEF identifies distinct profiles associated with atrial and ventricular remodeling and worse in-hospital outcomes. Future studies are required to determine whether phenotype-guided decongestive strategies can improve outcomes beyond risk stratification.
DOI: https://doi.org/10.37349/emed.2026.1001410
This article belongs to the special issue Assessment of Atrial and Ventricular Volumes and Functional Properties: Novel Insights
Aim:
Mycotoxins are the third most dangerous food contaminants, with one billion metric tons of food being contaminated annually. This study was conducted as a comprehensive assessment of aflatoxin B1 (AFB1) contamination in corn kernels and corn-growing soils across the six main corn-producing districts of Sri Lanka.
Methods:
A total of 12 soil samples were collected from the front, middle, and rear regions of each field from the subsurface and at various depths. In addition, six healthy corn kernel samples were harvested from the same locations. AFB1 was detected using enzyme-linked immunosorbent assay (ELISA). To verify the accuracy and precision of the assay, a recovery evaluation was conducted. To assess the distribution and correlations of AFB1 concentration in maize, its growing soil, and other environmental parameters, a comprehensive statistical study was conducted.
Results:
AFB1 level patterns implied that environmental factors influence the variability across the six districts. The temperature significantly affected AFB1 contamination in corn kernels with a p-value of 0.00014 (p < 0.05). Corn AFB1 levels showed a significant correlation with AFB1 levels in corn growing soils, with a p-value of 0.0261 (p < 0.05). Moreover, maximum AFB1 contamination was recorded at temperatures ranging from 26°C to 30°C.
Conclusions:
This study reveals a concerning trend; most of the corn samples from these districts exceeded the regulatory AFB1 levels set by the United States Food and Drug Administration (US FDA), and a significant positive correlation of corn AFB1 with soil AFB1 highlights soil as a potential reservoir for AFB1-producing fungi. Moreover, linking environmental elements to AFB1 data might encourage adaptive management strategies, which may help reduce contamination.
Aim:
Mycotoxins are the third most dangerous food contaminants, with one billion metric tons of food being contaminated annually. This study was conducted as a comprehensive assessment of aflatoxin B1 (AFB1) contamination in corn kernels and corn-growing soils across the six main corn-producing districts of Sri Lanka.
Methods:
A total of 12 soil samples were collected from the front, middle, and rear regions of each field from the subsurface and at various depths. In addition, six healthy corn kernel samples were harvested from the same locations. AFB1 was detected using enzyme-linked immunosorbent assay (ELISA). To verify the accuracy and precision of the assay, a recovery evaluation was conducted. To assess the distribution and correlations of AFB1 concentration in maize, its growing soil, and other environmental parameters, a comprehensive statistical study was conducted.
Results:
AFB1 level patterns implied that environmental factors influence the variability across the six districts. The temperature significantly affected AFB1 contamination in corn kernels with a p-value of 0.00014 (p < 0.05). Corn AFB1 levels showed a significant correlation with AFB1 levels in corn growing soils, with a p-value of 0.0261 (p < 0.05). Moreover, maximum AFB1 contamination was recorded at temperatures ranging from 26°C to 30°C.
Conclusions:
This study reveals a concerning trend; most of the corn samples from these districts exceeded the regulatory AFB1 levels set by the United States Food and Drug Administration (US FDA), and a significant positive correlation of corn AFB1 with soil AFB1 highlights soil as a potential reservoir for AFB1-producing fungi. Moreover, linking environmental elements to AFB1 data might encourage adaptive management strategies, which may help reduce contamination.
DOI: https://doi.org/10.37349/eff.2026.1010164
Scylla serrata (Forskål, 1775), or mud crab or mangrove crab, is an euryhaline edible crab belonging to the family Portunidae. This edible crustacean is used as a delicious foodstuff throughout the world and plays a role in traditional medicine for the treatment of various diseases such as tuberculosis, rheumatism, dropsy, bone fracture, asthma, insomnia, rickets, epilepsy, and convulsions. This review compiles and critically examines the reported ethnopharmacological uses, chemical constituents, and pharmacological activities of Scylla serrata. All data presented in this paper were collected by utilizing the online databases during 2015–2025. Chemical analysis on Scylla serrata resulted in the presence of proteins, amino acids, polyunsaturated fatty acids, monounsaturated fatty acids, and minerals. Chemical constituents will fluctuate depending on the sex, size, and season. Antioxidant, anti-anemic, anticancer, antimicrobial, and neuroprotective activities are reported from the crab. A significant study conducted on Scylla serrata evaluated its antimicrobial activity. Scylla serrata antimicrobial protein (SSAP), chitin, haemocyanin (HC), Scylla serrata beta-glucan binding protein (Ss-β-GBP), scygonadin, Scylla-anti-lipopolysaccharide (Sc-ALF), Scylla crustin (Sc-crustin), lectin, antibacterial haemocyanin (AB-Hcy), and Ss-arasin are the significant antimicrobial compounds isolated from the crab. In vitro research found substantial evidence that Scylla serrata has antioxidant, antianemic, anticancer, antibacterial, and neuroprotective properties. However, all described pharmacological activities were conducted in vitro, indicating a need for further pre-clinical and clinical research. Potential chemical compounds from different parts of the crab may need to be identified, and their pharmacological properties must be established.
Scylla serrata (Forskål, 1775), or mud crab or mangrove crab, is an euryhaline edible crab belonging to the family Portunidae. This edible crustacean is used as a delicious foodstuff throughout the world and plays a role in traditional medicine for the treatment of various diseases such as tuberculosis, rheumatism, dropsy, bone fracture, asthma, insomnia, rickets, epilepsy, and convulsions. This review compiles and critically examines the reported ethnopharmacological uses, chemical constituents, and pharmacological activities of Scylla serrata. All data presented in this paper were collected by utilizing the online databases during 2015–2025. Chemical analysis on Scylla serrata resulted in the presence of proteins, amino acids, polyunsaturated fatty acids, monounsaturated fatty acids, and minerals. Chemical constituents will fluctuate depending on the sex, size, and season. Antioxidant, anti-anemic, anticancer, antimicrobial, and neuroprotective activities are reported from the crab. A significant study conducted on Scylla serrata evaluated its antimicrobial activity. Scylla serrata antimicrobial protein (SSAP), chitin, haemocyanin (HC), Scylla serrata beta-glucan binding protein (Ss-β-GBP), scygonadin, Scylla-anti-lipopolysaccharide (Sc-ALF), Scylla crustin (Sc-crustin), lectin, antibacterial haemocyanin (AB-Hcy), and Ss-arasin are the significant antimicrobial compounds isolated from the crab. In vitro research found substantial evidence that Scylla serrata has antioxidant, antianemic, anticancer, antibacterial, and neuroprotective properties. However, all described pharmacological activities were conducted in vitro, indicating a need for further pre-clinical and clinical research. Potential chemical compounds from different parts of the crab may need to be identified, and their pharmacological properties must be established.
DOI: https://doi.org/10.37349/eff.2026.1010163
Aim:
Older adults with dyslipidemia often have coexisting diabetes and hypertension, requiring triple therapy with statins, antihypertensives, and oral antidiabetics. Given that statin adherence is a key metric in the Medicare STAR Ratings program, understanding statin use in this population is critical. However, prior studies have focused on adherence to statin monotherapy or composite adherence to triple therapy, with limited evidence on statin-specific patterns in this population.
Methods:
We conducted a retrospective cohort study using a Texas-based Medicare Advantage database (2016–2017). Adults receiving concurrent triple therapy (statins, renin-angiotensin system antagonists, and oral antidiabetics) were followed for 12 months. Statin adherence was measured monthly using the proportion of days covered (PDC) and modeled using group-based trajectory modeling (GBTM). Multinomial logistic regression, informed by the Andersen behavioral model, was used to identify sociodemographic and clinical predictors of adherence trajectories.
Results:
Among 7,847 patients, three distinct statin adherence trajectories were identified: near-perfect adherence (57.0%), adherent (23.6%), and rapid decline (19.4%). Female sex was associated with higher odds of rapid decline than male sex. Younger age (≤ 65 years) and having at least one prior hospitalization were significant predictors of rapid adherence decline, whereas older age and a greater number of concomitant medications were associated with lower odds of decline. Notably, about 80% of patients were adherent to statin despite lower adherence to the overall triple-therapy regimen.
Conclusions:
This study identified three distinct statin adherence trajectories among older adults on triple therapy. By highlighting predictors of rapid adherence decline, including female sex and prior hospitalization, these findings can help clinicians identify high-risk patients and inform targeted interventions to improve adherence and cardiovascular outcomes.
Aim:
Older adults with dyslipidemia often have coexisting diabetes and hypertension, requiring triple therapy with statins, antihypertensives, and oral antidiabetics. Given that statin adherence is a key metric in the Medicare STAR Ratings program, understanding statin use in this population is critical. However, prior studies have focused on adherence to statin monotherapy or composite adherence to triple therapy, with limited evidence on statin-specific patterns in this population.
Methods:
We conducted a retrospective cohort study using a Texas-based Medicare Advantage database (2016–2017). Adults receiving concurrent triple therapy (statins, renin-angiotensin system antagonists, and oral antidiabetics) were followed for 12 months. Statin adherence was measured monthly using the proportion of days covered (PDC) and modeled using group-based trajectory modeling (GBTM). Multinomial logistic regression, informed by the Andersen behavioral model, was used to identify sociodemographic and clinical predictors of adherence trajectories.
Results:
Among 7,847 patients, three distinct statin adherence trajectories were identified: near-perfect adherence (57.0%), adherent (23.6%), and rapid decline (19.4%). Female sex was associated with higher odds of rapid decline than male sex. Younger age (≤ 65 years) and having at least one prior hospitalization were significant predictors of rapid adherence decline, whereas older age and a greater number of concomitant medications were associated with lower odds of decline. Notably, about 80% of patients were adherent to statin despite lower adherence to the overall triple-therapy regimen.
Conclusions:
This study identified three distinct statin adherence trajectories among older adults on triple therapy. By highlighting predictors of rapid adherence decline, including female sex and prior hospitalization, these findings can help clinicians identify high-risk patients and inform targeted interventions to improve adherence and cardiovascular outcomes.
DOI: https://doi.org/10.37349/ec.2026.1012110
