Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine.

The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.

Effect of geometrical design variables on implantation configuration and fixation stiffness of titling bone anchors: A parametric finite element study.

The mechanical interaction of a tilting anchor and cancellous bones of various densities was simulated using finite element modeling. The model enjoyed a sophisticated representation of the bone, as an elasto-plastic material with large deformation capability. The anchor's tilting action during implantation phase, as well as its fixation stiffness during pull-out test, were predicted by the model and a parametric study was performed to investigate the effects of the anchor's distal width and corner fillet radius, on these measures. The model predictions were validated against the results of an experimental test on ovine humerus specimens. The model could reasonably reproduce the tilting action of the anchor during the implantation phase. Comparison of the model predictions with the experimental results revealed similar trends during both the implantation and the pull-out phases, but smaller displacement magnitudes (end points: 1.4 vs. 2.1 mm and 4.6 vs. 5.2 mm, respectively). The results of the parametric study indicated substantial increase in the fixation stiffness with increasing bone density. Reducing the distal width and increasing the fillet radius improved the anchor's implantation configuration and fixation stiffness in low-density bones. For high-density bone applications, however, a larger distal width was favored for improving the fixation stiffness.

Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review.

Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices. This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients' health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients' health outcomes, most involving evaluating patients' exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients' homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients. However, further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics.

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling.

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

An additively manufactured titanium tilting suture anchor: a biomechanical assessment on human and ovine bone specimens.

Introduction: A novel titanium tilting suture anchor was designed and fabricated using additive manufacturing. The anchor enjoyed a nonsymmetrical structure to facilitate its insertion procedure through a weight-induced tilt, a saw-teeth penetrating edge to provide a strong initial fixation into cancellous bones of various densities, and an appropriate surface texture to enhance the longterm fixation strength through bone ingrowth. Methods: Biomechanical tests were performed on 10 ovine and 10 human cadaveric humeri to examine the insertion procedure and assess the initial fixation strength of the anchor, in comparison with a standard screw-type anchor as control. Results: This study indicated a simple yet reliable insertion procedure for the tilting anchor. All anchors survived after 400 cycles of cyclic loadings and failed in the load-to-failure step. There were no significant differences between the displacements and fixation stiffnesses of the anchors in either group. The ultimate failure load was significantly smaller (p<0.05) for tilting anchors in ovine group (273.7 ± 129.72 N vs. 375.6 ± 106.36 N), but not different in human group (311.8 ± 82.55 N vs. 281.9 ± 88.35). Also, a larger number of tilting anchors were pulled out in ovine group (6 vs. 3) but a smaller number in human group (4 vs. 6). Conclusion: It was concluded that the biomechanical performance of the designed tilting anchor is comparable with that of the standard screw-type anchors.

Clinical manifestations of pain in patients suffering from COVID-19 infected with Delta variant of SARS-Cov-2.

Background: Although respiratory presentations of COVID-19 predominate, the extra pulmonary involvement such as muscle pain, joint pain, headache, back pain, abdominal pain, and sore throat are usually included in the clinical picture of the disease and it can be considered as an early symptom in COVID-19 patients. The objective of the present study was to determine the frequency, localization, and intensity of pain in COVID-19 patients hospitalized in Imam Khomeini hospital of Ardabil, Iran. Methods and materials: A prospective study was conducted on 388 COVID-19 patients who were admitted to Ardabil, Iran's Imam Khomeini Central Hospital between March and June 2020. Demographic characteristics of patients and general clinical manifestations of pain at the first admission to the hospital, localization, severity, and continuity of pain were evaluated by using a questionnaire and the Visual Analog Scale (VAS). Findings: For the 388 (51.3% female, age 47.25 + 15.55 and 48.7% male, age 50.12 + 15.26 years old) Delta COVID-19 patients, the median duration from illness onset to hospitalization was 5 days. Patients' complaints included 89.7% fatigue, 85.56% cough, 67.8% fever, 64.17% loss of taste, 63.91% loss of smell, 37.9% diarrhea, and 11.85% skin lesions, respectively. Pain including muscle, joint, bone and low back pain was the chief complaint in both sexes. Pain complaints had started on average 5 days before admission. The distribution of pain was 313 (80.41%) muscle pain, 264 (70.61%) joint pain, 299 (77.1%) headache, 208 (53.6%) low back pain, 312 (80.41%) sore throat, and 157 (40.46%) abdominal pain. Out of 388 patients, 292 (75.25%) had diffuse pain. Conclusions: Acute pain including myalgia, sore throat, arthralgia, headache, and low back pain were the most common symptoms of COVID-19 patients. Viral diseases such as COVID-19 may trigger the immune system to release cytokines that lead to muscle pain. Patients presenting to healthcare centers with complaints of pain should be evaluated for suspected COVID-19 infection.

Potential role of saffron and its components on miRNA levels in various disorders, a comprehensive review.

The potential therapeutic benefits of saffron and its active constituents have been investigated for the treatment of numerous illnesses. In this review, the impacts of saffron and its essential components on the levels of microRNAs (miRNAs) in different diseases have been delineated. Relevant articles were obtained through databases such as PubMed, Web of Sciences, Scopus, and Google Scholar up to the end of November 2022. miRNA expression has been altered by saffron and its active substances (crocin, crocetin, and safranal) which has been of great advantage in treating diseases such as cardiovascular, type 2 diabetes, cancers, gastrointestinal and liver disorders, central and peripheral nervous system disorders, asthma, osteoarthritis, ischemic-reperfusion induced injury conditions, and renal disorder. This study uncovered the potential restorative advantages of saffron and its derivatives, in miRNA imbalances in a variety of diseases.

Influence of Occlusal Hypofunction on Alveolar Bone Healing in Rats.

The aim of this in vivo study was to investigate the effect of occlusal hypofunction on alveolar bone healing in the absence or presence of an enamel matrix derivative (EMD). A standardized fenestration defect over the root of the mandibular first molar in 15 Wistar rats was created. Occlusal hypofunction was induced by extraction of the antagonist. Regenerative therapy was performed by applying EMD to the fenestration defect. The following three groups were established: (a) normal occlusion without EMD treatment, (b) occlusal hypofunction without EMD treatment, and (c) occlusal hypofunction with EMD treatment. After four weeks, all animals were sacrificed, and histological (hematoxylin and eosin, tartrate-resistant acid phosphatase) as well as immunohistochemical analyses (periostin, osteopontin, osteocalcin) were performed. The occlusal hypofunction group showed delayed bone regeneration compared to the group with normal occlusion. The application of EMD could partially, but not completely, compensate for the inhibitory effects of occlusal hypofunction on bone healing, as evidenced by hematoxylin and eosin and immunohistochemistry for the aforementioned molecules. Our results suggest that normal occlusal loading, but not occlusal hypofunction, is beneficial to alveolar bone healing. Adequate occlusal loading appears to be as advantageous for alveolar bone healing as the regenerative potential of EMD.

Saponin protects against cyclophosphamide-induced kidney and liver damage via antioxidant and anti-inflammatory actions.

Background: The liver and kidney are organs affected by chemotherapy drugs such as cyclophosphamide (CP). This study examined the protective effects of treatment with saponin (SP) against CP-induced nephrotoxicity and hepatotoxicity. Methods: 24 adult male mice were divided into four groups (N = 6): Control group, CP (15 mg kg-1), SP (2.5 mg kg-1) and CP + SP. After treatment, blood samples were collected for the determination of biochemical parameters. Liver and kidney samples were taken for histological analysis and assessment of oxidative stress and inflammatory markers. Results: Cyclophosphamide decreased renal and liver functions and antioxidant enzymes, which significantly increased blood urea nitrogen and creatinine (BUN, Cr), liver enzyme levels, malondialdehyde, nuclear factor kappa ß (NF-kB) and Interleukin 1 beta (IL-1B) concentrations. Moreover, histopathological findings of the CP group showed that there were acute tubular necrosis and glomerular atrophy in the renal tissues and lymphocyte infiltration in the liver samples. Treatment with saponin improved hepatic and renal functions, pathological changes and antioxidant capacity, and also decreased lipid peroxidation and inflammation. Conclusion: It seems that saponin could exert a hepato-nephroprotective effect against cyclophosphamide toxicity.

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches.

In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.

Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer's disease in independently dwelling adults.

INTRODUCTION: Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD. METHOD: Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD. RESULTS: In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value. DISCUSSION: AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

Dietary Changes among the Indian Population during the Covid-19 Pandemic: A Cross-Sectional Study.

Background: After the COVID-19 outbreak, significant changes in lifestyle and dietary patterns were observed. There are many studies indicating lifestyle changes but very few pointing out the intricate changes in consumption of different food groups, so our aim is to analyze the same. Methods: A cross-sectional study was conducted among 450 participants between the age group of 18 and 60 years. A self-developed questionnaire with questions regarding the change in consumption of different food groups during the pandemic was circulated online among the urban population. Results: It was observed that 46% participants turned down their consumption of fish and seafood and 48.2% participants lowered frozen protein consumption. 44.9% respondents increased their milk consumption and 41.8% reduced their intake of ice-creams. It was observed that 42.4% participants turned up their consumption of bread and buns and 47.3% of respondents increased their rice intake. It was further recorded that 39.6% and 40.9% participants raised their intake of sweets and chocolates and biscuits and cookies, respectively. 50% and 61.3% respondents ate more green leafy vegetables and fruits during the pandemic. There was a 53.3%, 46.9%, and 38.7% reduction in the consumption of hamburgers, pizza, and fried foods, respectively. Conclusion: Majority of the participants have increased consumption of healthy foods like milk, fruits, vegetables, and nuts, while reduced the consumption of junk foods, carbonated drinks, and ice-cream. There has been a positive shift in the dietary pattern of the Indian population toward foods that help develop immunity despite its limited availability during the pandemic.

Sleep Signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD).

OBJECTIVE: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. METHODS: In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.

Recent progress in the manipulation of biochemical and biophysical cues for engineering functional tissues.

Tissue engineering (TE) is currently considered a cutting-edge discipline that offers the potential for developing treatments for health conditions that negatively affect the quality of life. This interdisciplinary field typically involves the combination of cells, scaffolds, and appropriate induction factors for the regeneration and repair of damaged tissue. Cell fate decisions, such as survival, proliferation, or differentiation, critically depend on various biochemical and biophysical factors provided by the extracellular environment during developmental, physiological, and pathological processes. Therefore, understanding the mechanisms of action of these factors is critical to accurately mimic the complex architecture of the extracellular environment of living tissues and improve the efficiency of TE approaches. In this review, we recapitulate the effects that biochemical and biophysical induction factors have on various aspects of cell fate. While the role of biochemical factors, such as growth factors, small molecules, extracellular matrix (ECM) components, and cytokines, has been extensively studied in the context of TE applications, it is only recently that we have begun to understand the effects of biophysical signals such as surface topography, mechanical, and electrical signals. These biophysical cues could provide a more robust set of stimuli to manipulate cell signaling pathways during the formation of the engineered tissue. Furthermore, the simultaneous application of different types of signals appears to elicit synergistic responses that are likely to improve functional outcomes, which could help translate results into successful clinical therapies in the future.

Effect of saffron supplementation on oxidative stress markers (MDA, TAC, TOS, GPx, SOD, and pro-oxidant/antioxidant balance): An updated systematic review and meta-analysis of randomized placebo-controlled trials.

Introduction: This study aimed to perform an updated systematic review and meta-analysis to evaluate the effectiveness of saffron supplementation on oxidative stress markers [malondialdehyde (MDA), total antioxidant capacity (TAC), total oxidant status (TOS), glutathione peroxidase (GPx), superoxide dismutase (SOD), and prooxidant/antioxidant balance (PAB)] in randomized controlled trials (RCTs). Methods: We searched PubMed/Medline, Web of Science, Scopus, Cochrane CENTRAL, and Google Scholar until December 2022. Trial studies investigating the effects of oral saffron supplements on MDA, TAC, TOS, GPx, SOD, and PAB concentrations were included in the study. To analyze the results, mean differences (SMD) and 95% confidence intervals (CI) were pooled using a random effects model. Heterogeneity was assessed using the Cochrane Q and I 2 values. Sixteen cases were included in the meta-analysis (468 and 466 subjects in the saffron and control groups, respectively). Results: It was found that saffron consumption caused a significant decrease in MDA (SMD: -0.322; 95% CI: -0.53, -0.16; I 2 = 32.58%) and TOS (SMD: -0.654; 95% CI: -1.08, -0.23; I 2 = 68%) levels as well as a significant increase in TAC (SMD: 0.302; 95% CI: 0.13, 0.47; I 2 = 10.12%) and GPx (SMD: 0.447; 95% CI: 0.10, 0.80; I 2 = 35%). Subgroup analysis demonstrated a significant reduction in MDA levels in studies with a saffron dosage of >30 mg/day, age of <50 years, and study duration of <12 weeks. Among the limitations of the study, we can point out that the studies were from Iran, the different nature of the diseases included, and were not considered of some potential confounders such as smoking, physical activity, and diet in the studies. Discussion: In summary, the results showed that saffron has beneficial effects on oxidative stress markers.

Protein by-products: Composition, extraction, and biomedical applications.

Significant upsurge in animal by-products such as skin, bones, wool, hides, feathers, and fats has become a global challenge and, if not properly disposed of, can spread contamination and viral diseases. Animal by-products are rich in proteins, which can be used as nutritional, pharmacologically functional ingredients, and biomedical materials. Therefore, recycling these abundant and renewable by-products and extracting high value-added components from them is a sustainable approach to reclaim animal by-products while addressing scarce landfill resources. This article appraises the most recent studies conducted in the last five years on animal-derived proteins' separation and biomedical application. The effort encompasses an introduction about the composition, an overview of the extraction and purification methods, and the broad range of biomedical applications of these ensuing proteins.

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state.

Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273-637 K) and pressure (0.051-113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel-Teja (VPT), and Soave-Redlich-Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases' solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

Tannic acid: a versatile polyphenol for design of biomedical hydrogels.

Tannic acid (TA), a natural polyphenol, is a hydrolysable amphiphilic tannin derivative of gallic acid with several galloyl groups in its structure. Tannic acid interacts with various organic, inorganic, hydrophilic, and hydrophobic materials such as proteins and polysaccharides via hydrogen bonding, electrostatic, coordinative bonding, and hydrophobic interactions. Tannic acid has been studied for various biomedical applications as a natural crosslinker with anti-inflammatory, antibacterial, and anticancer activities. In this review, we focus on TA-based hydrogels for biomaterials engineering to help biomaterials scientists and engineers better realize TA's potential in the design and fabrication of novel hydrogel biomaterials. The interactions of TA with various natural or synthetic compounds are deliberated, discussing parameters that affect TA-material interactions thus providing a fundamental set of criteria for utilizing TA in hydrogels for tissue healing and regeneration. The review also discusses the merits and demerits of using TA in developing hydrogels either through direct incorporation in the hydrogel formulation or indirectly via immersing the final product in a TA solution. In general, TA is a natural bioactive molecule with diverse potential for engineering biomedical hydrogels.

Melatonin treatment improves cognitive deficits by altering inflammatory and neurotrophic factors in the hippocampus of obese mice.

Overweight and obesity are associated with an increased risk of developing dementia and cognitive deficits. Neuroinflammation is one of the most important mechanisms behind cognitive impairment in obese patients. In recent years, the neuroendocrine hormone melatonin has been suggested to have therapeutic effects for memory decline in several neuropsychiatric and neurological conditions. However, the effects of melatonin on cognitive function under obesity conditions still need to be clarified. The purpose of this study was to determine whether melatonin treatment can improve cognitive impairment in obese mice. To this end, male C57BL6 mice were treated with a high-fat diet (HFD) for 20 weeks to induce obesity. The animal received melatonin for 8 weeks. Cognitive functions were evaluated using the Y maze, object recognition test, and the Morris water maze. We measured inflammatory cytokines including tumor necrosis factor (TNF)-α, interferon (IFN)-γ, interleukin (IL)-17A, and brain-derived neurotrophic factor (BDNF) in the hippocampus of obese mice. Our results show that HFD-induced obesity significantly impaired working, spatial and recognition memory by increasing IFN-γ and IL-17A and decreasing BDNF levels in the hippocampus of mice. On the other hand, melatonin treatment effectively improved all cognitive impairments and reduced TNF-α, IFN-γ, and IL-17A and elevated BDNF levels in the hippocampus of obese mice. Taken together, this study suggests that melatonin treatment could have a beneficial role in the treatment of cognitive impairment in obesity.

Early life GABAA blockade alters the synaptic plasticity and cognitive functions in male and female rats.

Gamma-aminobutyric acid (GABA), the major inhibitory neurotransmitter in adults, has a critical contribution to balanced excitatory-inhibitory networks in the brain. Alteration in depolarizing action of GABA during early life is connected to a wide variety of neurodevelopmental disorders. Additionally, the effects of postnatal GABA blockade on neuronal synaptic plasticity are not known and therefore, we set out to determine whether postnatal exposure to bicuculline, a competitive antagonist of GABAA receptors, affects electrophysiologic changes in hippocampal CA1 neurons later on. To this end, male and female Wistar rats received vehicle or bicuculline (300 µg/kg) on postnatal days (PNDs) 7, 9 and 11, and then underwent different behavioral and electrophysiological examinations in adulthood. Postnatal exposure to bicuculline did not affect basic synaptic transmission but led to a pronounced decrease in paired-pulse facilitation (PPF) in CA1 pyramidal neurons. Bicuculline treatment also attenuated the long-term potentiation (LTP) and long-term depression (LTD) of CA1 neurons accompanied by decreased theta-burst responses in male and female adult rats. These electrophysiology findings together with the reduced brain-derived neurotrophic factor (BDNF) levels in the hippocampus and prefrontal cortex reliably explain the disturbance in spatial reference and working memories of bicuculline-treated animals. This study suggests that postnatal GABAA blockade deteriorates short- and long-term synaptic plasticity of hippocampal CA1 neurons and related encoding of spatial memory in adulthood.