Conectar
by Abdallah Tageldein Mansour, Safaa I. Khater, Hemmat M. Eissa, Helal F. Al-Harthi, Areej A. Eskandrani, Mohammed Ageeli Hakami, Wafa S. Alansari, Amirah Albaqami, Hanan M. Alharbi, Tarek Khamis, Doaa Ibrahim
The medicinal application of pomegranate peel extract enriched with polyphenols (PPE) as a therapeutic strategy for managing inflammatory bowel diseases (IBD) is still limited. Integrating pomegranate peel extract (PPE) into an effective nanocarrier system could enhance its mechanistic actions, potentially aiding in the remission of colitis. Therefore, this approach aimed to enhance PPE’s stability and bioavailability and investigate mitigating impact of pomegranate peel extract-loaded nanoparticles (PPE-NPs) in a colitis model. Colonic injury was induced by 5% dextran sulfate sodium (DSS) and efficacy of disease progression after oral administration of PPE-NPs for 14 days was assessed by evaluating clinical signs severity, antioxidant and inflammatory markers, expressions of endoplasmic reticulum associated genes and histopathological and immunostaining analysis in colonic tissues. Clinical signs and disease activity index were effectively reduced, and the levels of fecal calprotectin were decreased in groups treated with PPE-NPs compared to DSS group. The colitic group showed a significant increase (P IL-17, TNF-α, and IL-1β (increased up to 2.99, 4.36 and 4.90 respectively unlike PPE-NPsIII that recorded reduced levels of CRP, MPO and NO (8,96, 78.30 and 123 nmol/g tissue respectively) and much lower (P CHOP, JUNK, ATF6, BIP, and Elf-2) and immunostaining expression regulation of key markers regulating autophagy (Beclin-2) in this group. The histopathological changes in the colon were less severe in the PPE-NPs received groups (especially at the level of 150 mg/kg) compared to DSS group. Collectively, these findings suggest that the nanoencapsulation of PPE enhances its effectiveness in promoting recovery of colonic tissue damage and achieving remission of colitis.
by Bewketu Mehari, Tarekegn Fentie Yimer, Tihitna Beletkachew, Eyob Alem, Worku Negash, Mengistu Mulu, Dereje Yenealem, Ayalnesh Miretie
Sesame (Sesamum indicum L.) is a major oilseed crop globally, and white sesame is a key contributor to the foreign exchange earnings of Ethiopia. The main production districts of white sesame in Ethiopia are Humera, Metema, Tegedie, Mirab-Armachiho and Tachi-Armaciho. This study assessed the levels of trace metals (Fe, Cu, Zn, Mn and Ni) in white sesame seeds from these regions and evaluated the associated health risks to consumers. A total of 53 samples were collected from 19 farmer villages across the five districts. Homogenized samples from each village were analyzed using the acid digestion method followed by flame atomic absorption spectroscopy (FAAS). The limit of detection of the method ranged from 0.75 to 865 mg/kg, and the limit of quantitation ranged from 2.55 to 28.8 mg/kg for the different elements analyzed. The recovery of the method was in the range of 90.9‒99.6%. The results showed trace metal levels ranging from 164 ± 6 to 381 ± 4 mg/kg for Fe, 94.0 ± 1.9 to 126 ± 0.8 mg/kg for Zn, 11.8 ± 0.4 to 14.2 ± 0.4 mg/kg for Cu, 11.9 ± 0.9 to 15.0 ± 0.7 mg/kg for Mn and 16.2 ± 1.1 to 21.0 ± 1.2 mg/kg for Ni across the production districts. One-way ANOVA revealed significant differences (p
by Mohsen Askar, Masoud Tafavvoghi, Lars Småbrekke, Lars Ailo Bongo, Kristian Svendsen
AimIn this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.
MethodsWe searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.
ResultsWe screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.
ConclusionThis review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.
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