Dubin-Johnson syndrome (DJS) and Rotor syndrome (RS) are rare, autosomal recessive disorders that result in chronic, predominantly conjugated hyperbilirubinemia without cholestasis or hepatocellular injury. Although both conditions are benign and non-progressive, they reflect distinct molecular defects in hepatocellular transport pathways. DJS arises from mutations in the ABCC2 gene encoding the canalicular transporter multidrug resistance–associated protein 2, leading to impaired biliary excretion of conjugated bilirubin and organic anions. In contrast, RS results from combined deficiencies of the sinusoidal transporters OATP1B1 and OATP1B3, encoded by SLCO1B1 and SLCO1B3 genes, respectively, which mediate hepatic reuptake of conjugated bilirubin from the sinusoidal blood. These defects explain the characteristic biochemical and clinical distinctions between the syndromes, including the black hepatic pigmentation and markedly elevated urinary coproporphyrin I fraction in DJS, and the absence of pigmentation with moderate coproporphyrin I predominance in RS. Recent studies have expanded the understanding of how these transporters influence not only bilirubin handling but also the hepatic disposition of various drugs and endogenous metabolites. Recognition of DJS and RS is essential to prevent misdiagnosis of cholestatic or hepatocellular disease, avoid unnecessary investigations, and anticipate altered pharmacokinetics in affected individuals. This review synthesizes current evidence from molecular, biochemical, and clinical studies to highlight how these syndromes illuminate broader principles of hepatic transporter physiology and its relevance to inherited and acquired disorders of bilirubin metabolism.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.

Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning.

We conducted a cross-sectional study between November 2023 and May 2024 in Fortaleza, Ceará, Brazil. Based on the Framingham Cardiovascular Risk Scale, patients were categorized into three cardiovascular risk groups: Low (22 participants), Moderate (14 participants), and High (17 participants). From ECG signals at two different positions (ECG_Down and ECG_UP), 27 non-linear features were extracted using multi-band analysis. Additionally, 42 medical features provided by physicians were included. From a pool of 19 machine learning classifiers, models were trained and tested within a nested leave-one-out cross-validation procedure using information solely from ECG, solely from medical features, and combining both (multimodal), respectively, to distinguish between Low vs. Moderate, Low vs. High, Moderate vs. High, and All vs. All.

The multimodal model presented the best results for every comparison group, reaching (1) 88.89% Accuracy and 0.8831 area under the curve (AUC) for Low vs. Moderate; (2) 97.44% Accuracy and 0.9706 AUC for Low vs. High; (3) 93.55% Accuracy and an AUC of 0.9412 for Moderate vs. High; (4) 86.79% Accuracy and 0.9346 AUC for All vs. All.

The multimodal model outperformed single-source models in cardiovascular risk classification. ECG-derived non-linear features, especially from ECG_Down, were key drivers, with medical features adding complementary value. The results support its potential use in clinical triage and diagnosis.

Amoebiasis, or amoebic dysentery, is a gastrointestinal disorder caused by the parasite Entamoeba histolytica. The disease is endemic in parts of Africa, Asia, North and South America, leading to several deaths annually. Reported adverse effects associated with the current first-line treatment for amoebiasis, coupled with the evolution of resistance to it, call for the need to search for plant-based alternatives. This study systematically reviews medicinal plants with activity against Entamoeba histolytica.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to retrieve scholarly literature. The study reviewed 70 articles from 7 popular databases: Google Scholar, PubMed, ScienceDirect, Booksc.org, Emerald, Scopus, and MEDLINE, highlighting several plants with anti-amoebic properties.

The primary parts of the plant used in the treatment of Entamoeba histolytica were the leaves (61%), followed by rhizomes (13%), roots (8%), seeds (8%), stems (4%), and fruits (4%). The families Asteraceae (18%) and Zingiberaceae (18%) contain most plants that are effective against Entamoeba histolytica. These medicinal plants families are rich in phytochemicals such as terpenoids and flavonoids that have anti-entamoeba histolytica activity. Maceration is the most commonly used extraction method.

The results suggest that plants are a promising source of new agents to combat amoebiasis caused by Entamoeba histolytica. The most frequently used plant parts were leaves (61%), and the maceration method was the most common extraction technique due to its simplicity and cost-effectiveness. The majority of studies were limited to in vitro models, with only one plant (Adenophyllum aurantium) tested in vivo. Further research is needed to establish their mechanisms of action, toxicities, and clinical potential.

The COVID-19 pandemic profoundly impacted university students, presenting multifaceted challenges including the abrupt transition to virtual learning and significant disruptions to emotional well-being and dietary habits. This study aimed to investigate the dietary and nutritional characteristics associated with academic stress among Mexican university students during the COVID-19 lockdown.

This cross-sectional study was conducted with a sample of 114 university students in Mexico. Participants completed a self-reported questionnaire assessing dietary patterns, nutritional intake, and academic stress levels. Informed consent was obtained from all participants prior to data collection.

Among study participants (n = 114), 57.8% experienced moderate academic stress, while 25.7% reported high academic stress during the COVID-19 lockdown. Notably, 13.5% of students demonstrated food cravings that were significantly associated with increased consumption of red and fatty meats (P = 0.030) and sausages (P = 0.017). A negative virtual education experience was associated with food cravings towards high-calorie and saturated-fat foods (P = 0.014), as well as elevated academic stress levels (P = 0.009). Furthermore, high academic stress levels were positively associated with food cravings (P = 0.020), particularly towards carbohydrate-rich foods (P = 0.037).

The COVID-19 lockdown substantially disrupted the dietary habits and nutritional status of university students, with academic stress serving as a significant mediating factor.

Radiotherapy remains one of the essential treatment modalities for brain gliomas, brain metastases, pediatric neuroblastomas, and primary central nervous system lymphomas. With continuous advancements in modern radiotherapy techniques, patients have achieved significantly improved local control rates and prolonged survival. However, the long-term complications associated with radiotherapy have become increasingly evident. Radiation-induced brain injury (RIBI) is a clinical syndrome characterized primarily by neurological dysfunction following focal or whole-brain radiotherapy. It negatively impacts patients’ quality of life and imposes a considerable burden on families and society. With the rapid development of medical imaging and artificial intelligence technologies, multimodal imaging techniques, including structural magnetic resonance imaging, diffusion-weighted imaging, functional magnetic resonance imaging, perfusion imaging, positron emission tomography-computed tomography metabolic imaging, and radiomics, have demonstrated significant potential for early detection, dynamic monitoring, and quantitative evaluation of RIBI. Meanwhile, treatment strategies for RIBI are shifting from traditional symptomatic and supportive care toward multidimensional interventions aimed at protecting the blood-brain barrier, modulating neuroinflammation, and implementing precise targeted therapies. Additionally, emerging studies have explored neuromodulation techniques and gut-brain axis regulation, offering new directions for the prevention and treatment of RIBI. Although conventional imaging methods remain valuable for diagnosing RIBI, they exhibit notable limitations in the early stages of the disease and in differentiating RIBI from tumor recurrence. This review focuses on the current state of technological development, key findings, and existing limitations, with the aim of providing a theoretical foundation and technical support for the early identification and precise intervention of RIBI.

Parenteral nutrition (PN)-associated cholestasis (PNAC) is frequently diagnosed in premature infants; however, not all PN-exposed infants develop PNAC. We propose that, in premature infants receiving PN and varying amounts of enteral feeds, differences in the gut microbiome and fecal bile acid content are associated with PNAC development. This study aimed to examine the fecal microbiome and bile acid content of premature infants on PN to determine if there is a relationship with the development of PNAC.

Twenty-two preterm infants had serial bilirubin measurements and fecal samples collected during their neonatal intensive care unit admission. Fecal samples underwent 16S rRNA gene sequencing and bile acid analysis. Binomial regression, adjusting for postmenstrual age with feed amount as a moderator, was used to assess the impact of the fecal microbiome and bile acids on PNAC development.

Cholestatic patients (n = 11) had greater PN and antibiotic exposure (p = 0.020; p = 0.010) and longer neonatal intensive care unit stays (p = 0.0038) than non-cholestatic patients. Microbiome richness was higher in non-cholestatic infants (p < 2E-16), with no difference in β diversity (p = 1.0). Cholestatic infants had a significantly higher abundance of Proteobacteria and Fusobacteriota and a lower abundance of Bacteroidota (p < 2E-16). Akkermansia was abundant in all infants on low feeds; as feed volume increased, Akkermansia abundance significantly increased in non-cholestatic infants (p < 2E-16). Bile acid analysis demonstrated significantly lower deoxycholic acid concentrations in cholestatic infants (p < 2E-16). Metagenomic analysis revealed an increase in Proteobacteria requiring augmented stress responses in non-cholestatic infants.

This is the first study to directly explore the relationship between PNAC susceptibility, the microbiome, and fecal bile acids in preterm infants. The microbiome and bile acid patterns identified here may inform the development of targeted therapeutics for this vulnerable population.

Micro- and nanoplastics (MNPs) are plastic particles smaller than 5 mm and 1 µm, respectively, and are emerging environmental pollutants with growing implications for human health. These particles stem from either ‘primary sources’, such as intentionally manufactured microbeads and industrial abrasives, or ‘secondary sources’, where larger plastic items break down into smaller fragments over time. Human exposure primarily occurs through ingestion and inhalation, with contaminated seafood and plastic-laden food packaging representing key routes of entry. Once ingested, MNPs can cross the intestinal barrier, accumulate in gastrointestinal (GI) tissues, and trigger biological responses. Mechanistic studies reveal that MNPs induce oxidative stress, DNA damage, chronic inflammation, and endocrine disruption, all of which are hallmarks of carcinogenic pathways. They also alter gut microbiota, potentially promoting dysbiosis and immune dysregulation. The GI tract is particularly vulnerable to these effects due to direct luminal mucosal contact and high epithelial turnover. Epidemiological data remain limited, but early evidence supports a plausible link between MNPs exposure and GI malignancies. Such findings are particularly concerning given the increasing global incidence and early age presentation of colorectal and esophageal cancers. Given that MNPs may represent a modifiable environmental risk factor in GI cancer prevention, public health strategies must prioritize reducing plastic exposure, promoting antioxidant-rich diets, and improving environmental monitoring. This review explores the potential carcinogenic effects of microplastics while also examining their emerging roles in cancer therapeutics. It highlights critical avenues for future investigation and underscores the importance of cross-disciplinary efforts to tackle this growing global health concern.

Prognostic scores are valuable tools for predicting survival in patients with chronic liver disease. Recently, the albumin-bilirubin (ALBI) score has emerged as a potential prognostic indicator in liver-related conditions. This study aimed to compare the prognostic efficacy of the ALBI score with the Model for End-stage Liver Disease (MELD), MELD-Na+, and Child-Turcotte-Pugh (CTP) scores in predicting survival among patients with alcohol-associated liver disease (ALD).

This study included consecutive ALD patients admitted to the Medicine and Gastroenterology wards of MKCG Medical College and Hospital, Berhampur, Odisha, India, between November 2019 and November 2022. Upon hospitalization, baseline characteristics, clinical and laboratory parameters, ALBI, MELD, MELD-Na+, and CTP scores were recorded. The accuracy of these scores in predicting survival up to three years was compared.

A total of 490 ALD patients were included. Higher ALBI scores were observed in patients who died during hospitalization (p < 0.001), at 28 days (p < 0.001), 90 days (p < 0.001), six months (p < 0.001), one year (p < 0.001), two years (p < 0.001), and three years (p < 0.001), compared to those who survived. However, the area under the receiver operating characteristic (AUROC) curves showed that the ALBI score was inferior to MELD, MELD-Na+, and CTP scores in predicting survival at admission [AUROC: ALBI (0.719), MELD-Na+ (0.823), MELD (0.817), CTP (0.770)] and at three years [AUROC: ALBI (0.755), MELD-Na+ (0.787), MELD (0.758), CTP (0.784)]. Furthermore, Cox regression analysis revealed that components used in the MELD, MELD-Na+, and CTP scores—such as serum creatinine, serum sodium, and hepatic encephalopathy—were independent predictors of mortality, whereas the components of the ALBI score (serum albumin and serum bilirubin) were not.

All hospitalized ALD patients had a grade 3 ALBI score, with significantly higher scores observed among non-survivors compared to survivors. However, MELD, MELD-Na+, and CTP scores were superior to the ALBI score in predicting survival both during hospitalization and over a three-year follow-up period.