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Jujube (Ziziphus jujuba) fruit has been routinely used as a food, additive, and flavoring agent for thousands of years due to its rich nutritional contents. Mounting evidence proves the therapeutic benefits of Ziziphus jujuba, including anticancer, anti-inflammatory, antiobesity, antioxidant, immune-stimulating, neuroprotective, and hepato- and gastro-protective properties, which are due to its main biologically active components: phenolics, triterpenic acids, flavonoids, α-tocopherol, β-carotene, cerebrosides, and polysaccharides. It is also used in conventional health care, for treating anorexia, fatigue, and diarrhea. Therefore, a deep focus on clinical studies and the phytochemical definition of jujube fruits will be critical for following research efforts. The current work aimed to cover all existing in vitro and in vivo discoveries linked to the therapeutically beneficial effects of Z. jujuba bioactive substances and the underlying molecular mechanisms. To write this review, the data were collected from valid scientific databases, including Science Direct, PubMed, Scopus, and Google Scholar (2014-2024). These findings could provide several lines of evidence for the medicinal uses of jujube as supplementary products for the prevention or treatment of several diseases such as cancer and other immune disorders.

Abstract The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

Abstract The production of date syrup yields a substantial amount of date press cake (DPC), fibrous and moisturising material with great potential for generating value through bioprocessing. However, the recalcitrant structure of DPC affects the yield of products in bioprocesses. To boost the accessibility of the structure as well as increase the soluble fraction of carbohydrates and facilitate further enzymatic hydrolysis, hydrothermal and dilute acid (0.5% (v/v) sulfuric acid) pretreatments as cost-effective and feasible methods were applied on DPC at relatively low temperatures (80, 100, 120 and 140 °C) and reaction times (60 and 90 min). The success in pretreatment was then evaluated by a post-enzymatic treatment using an enzyme cocktail of cellulases and hemicelluloses. Based on total accessible sugar with minimum produced inhibitors, an optimal operating condition was considered acid pretreatment at 120 °C for 90 min with a 55.02% increase in total sugar yield. To explore the potential use of pretreated DPC, an anaerobic digestion was conducted on untreated and acid-pretreated DPC at 120 °C for 90 min. The results showed that pretreatment increased the total bioproduct yield, including hydrogen, ethanol, and volatile fatty acid yields, by 59.75%. This demonstrates the significant impact of pretreatment on product yields in a bioprocess.

Abstract Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

The recent emergence of the omicron variant of the SARS-CoV-2 virus with large numbers of mutations has raised concern about a potential new surge in infections. Here we use molecular dynamics to study the biophysics of the interface of the BA1 and BA2 omicron spike protein binding to (i) the ACE2 receptor protein, (ii) antibodies from all known binding regions, and (iii) the furin binding domain. Our simulations suggest that while there is a significant reduction of antibody (Ab) binding strength corresponding to escape, the omicron spikes pay a cost in terms of weaker receptor binding as measured by interfacial hydrogen bonds (H-bond). The furin cleavage domain (FCD) is the same or weaker binding than the delta variant, suggesting lower fusogenicity resulting in less viral load and disease intensity than the delta variant. IMPORTANCE The BA1 and BA2 and closely related BA2.12.2 and BA.5 omicron variants of SARS-CoV-2 dominate the current global infection landscape. Given the high number of mutations, particularly those which will lead to antibody escape, it is important to establish accurate methods that can guide developing health policy responses that identify at a fundamental level whether omicron and its variants are more threatening than its predecessors, especially delta. The importance of our work is to demonstrate that simple in silico simulations can predict biochemical binding details of the omicron spike protein that have epidemiological consequences, especially for binding to the cells and for fusing the viral membrane with the cells. In each case, we predicted weaker binding of the omicron spike, which agreed with subsequent experimental results. Future virology experiments will be needed to test these predictions further.

The increasing issue of antibiotic resistance in bacterial infections has led to challenging and costly treatments. Free radicals significantly contribute to the progression of several diseases, such as cardiometabolic disorders, neurodegenerative conditions, and cancers. Antioxidants can help alleviate or prevent these health problems. This research aimed to assess the antibacterial and antioxidant effects of Eremostachys hyoscyamoides ethanol extract. Additionally, the chemical profile of the essential oil obtained from the aerial parts of E. hyoscyamoides was characterized through gas chromatography-mass spectrometry (GC-MS). The extract's antibacterial effect was tested against three Gram-positive bacteria (Micrococcus luteus, Staphylococcus epidermidis, and S. aureus), in addition to three Gram-negative bacteria (Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli). The cup-plate method was employed to assess the antibacterial activity, which was subsequently followed by the determination of the minimum inhibitory concentration through the agar dilution method. To evaluate the antioxidant effects, the DPPH radical scavenging assay was used. The total phenolic content (TPC) and flavonoid content (TFC) of the extract were quantified using the Folin Ciocalteu and Aluminum Chloride methods as spectrophotometric-based techniques, respectively. The essential oil of E. hyoscyamoides was extracted via hydrodistillation and subjected to GC-MS analysis. The findings demonstrated that the ethanol extract of E. hyoscyamoides effectively inhibited the growth of the bacterial strains examined. The IC50 value measured in the DPPH test was 48.194 ± 0.61 μg/mL. The TPC was found to be 84.15 ± 2.5 mg GAE/g, while the TFC was determined to be 19.35 ± 1.3 mg RE/g. A total of 50 components, accounting for 93.6 % of the essential oil composition, were identified. High concentrations of elemol (6.8 %), 2,4-di-tert-butylphenol (10.7 %), and linalyl acetate (4.2 %) were putatively identified in the essential oil. In conclusion, the ethanol extract of E. hyoscyamoides exhibited hopeful potential as a natural source of antioxidants and antibacterial agents.

Sodium benzoate (SB) is a salt of benzoic acid that is highly soluble in water, bland, and unscented. It is produced by mixing benzoic acid with sodium hydroxide. Sodium benzoate does not exist organically, yet when combined with water, it creates benzoic acid, which exists natively in particular crops. Because of its antifungal and antibacterial effects, it is used as a food preservative at tightly controlled dosages. Though some research suggests that sodium benzoate is effective in helping alleviate depression, pain, schizophrenia, autism spectrum disorders, and neurodegenerative illnesses, others claim that it is hazardous. It has been shown to produce mutagenesis consequences, oxidative stress, hormone disruption, and decreased fertility. In recent years, there have been worries regarding the growing use of this chemical as a preservative in meals, particularly processed foods. Due to the large number of reports regarding the harmfulness of food additives like sodium benzoate, more and more researchers are increasingly looking for techniques to quantify the quantity of this molecule utilized in foods. High-performance liquid chromatography (HPLC-UV and LC-MS/MS) methods are two examples of instrumental analytical methods that have been developed and validated for quantitative analyses of sodium benzoate in foods and beverages. Obviously, there are numerous other approaches to analyzing these materials. The purpose of this study is to comprehensively discuss the safety profile of sodium benzoate and its potential usefulness, as well as the hazardous effects. Furthermore, we have discussed the ways of determining the quantity and identity of this component in food.

Diabetes is one of the most rapidly growing health problems globally, and researchers working hard to develop novel efficient therapies against it. In this regard, hydrazine carboximidamide has a great deal of attention owing to its diverse biological activities. Herein, a new series of N, N-disubstituted hydrazine carboximidamide derivatives was designed, synthesized, and assessed for their antidiabetic activity. Docking studies were performed to initially evaluate the binding of the designed compounds to the target enzyme. It was found that all of the pivotal ligand-enzyme interactions, including the interactions with Glu205/Glu206 aminoacids were observed in docking studies. The molecular dynamic analysis showed that the antidiabetic activity of the newly developed compounds may be implemented through the inhibition of dipeptidyl peptidase 4 (DPP-4), an important target that involves enhancing insulin release and suppressing glucagon discharge from the pancreas. The target molecules were synthesized practically and all the newly-developed chemical structures were confirmed via spectrophotometric methods, including IR, LC-MS, 1H NMR, and 13C NMR techniques. Finally, the in-vivo studies were carried out to confirm the results obtained from the docking and molecular dynamics studies. Compounds 6b and 6d demonstrated improved glucose tolerance through oral glucose tolerance tests in NMRI mice (at a dose of 10 mg/kg). Furthermore, prolonged administration of compounds 6b and 6d to diabetic Wistar rats for 14 days led to a noteworthy reduction in blood levels of glucose, akin to the effects observed with sitagliptin, a standard diabetes medication.