High-fat diet induces depression-like phenotype via astrocyte-mediated hyperactivation of ventral hippocampal glutamatergic afferents to the nucleus accumbens.

Comorbidity exists between metabolic disorders and depressive syndrome with unclear mechanisms. To characterize the causal relationship, we adopted a 12-week high-fat diet (HFD) to induce metabolic disorder and depressive phenotypes in mice. Initially, we identified an enhanced glutamatergic input in the nucleus accumbens of HFD mice. Retrograde tracing and chemogenetic inhibition showed that the hyperactive ventral hippocampal glutamatergic afferents to the nucleus accumbens determined the exhibition of depression-like behavior in HFD mice. Using lentiviral knockdown and overexpression approaches, we proved that HFD-induced downregulation of glial glutamate transporters, GLAST and GLT-1, contributed to the observed circuit maladaptations and subsequent depression-like behaviors. Finally, we identified a potential therapeutic agent, riluzole, which could mitigate the HFD-induced behavioral deficits by normalizing the expressions of GLAST and GLT-1 and ventral hippocampal glutamatergic afferents to the nucleus accumbens. Overall, astrocyte-mediated disturbance in glutamatergic transmission underlies the metabolic disorder-related depressive syndrome and represents a therapeutic target for this subtype of depressive mood disorders.

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution.

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

In vitro antioxidant and cholinesterase inhibitory activities of Elatostema papillosum leaves and correlation with their phytochemical profiles: a study relevant to the treatment of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), one of the major causes of dementia, is an overwhelming neurodegenerative disease that particularly affects the brain, leading to memory loss and impairment of language and judgment capacity. The aim of the present study was to investigate the antioxidant and anticholinesterase properties of the leaves of Elatostema papillosum (EPL) and correlate with their phytochemical profiles, which are relevant to the treatment of AD. METHODS: The dried coarse powder of EPL was extracted with 80% methanol (EPL-M80) by cold extraction method. The resultant EPL-M80 was assessed for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activity by the Ellman method. The antioxidant activity was determined by DPPH (1, 1-diphenyl-2-picrylhydrazyl) and hydroxyl radical scavenging assays. Quantitative phytochemical (phenolic and flavonoid contents) analysis of endogenous substances in EPL-M80 was performed by standard spectrophotometric methods. RESULTS: EPL-M80 significantly (p < 0.05) inhibited AChE and BChE activity with IC50 of 165.40 ± 4.01 and 213.81 ± 3.57 µg/mL, respectively in a dose-dependent manner. Additionally, EPL-M80 exhibited strong radical scavenging activity against DPPH (IC50 = 32.35 ± 0.68 µg/mL) and hydroxyl radical (IC50 = 19.67 ± 1.42 µg/mL) when compared to that of standards. EPL-M80 was found to be rich in phenolic (23.74 mg gallic acid equivalent/g of dry extract) and flavonoid (31.18 mg quercetin equivalent/g of dry extract) content. Furthermore, a positive correlation (p < 0.001) was observed between the total phenolics and antioxidant as well as the anticholinesterase potential. CONCLUSIONS: The marked inhibition of AChE and BChE, and potent antioxidant activity of the leaves of Elatostema papillosum highlight its potential to provide an effective treatment for AD.

A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty.

The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule based expert system.

An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer's disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer's diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model's exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer's disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.

Comparison of Structural Diagnosis and Management (SDM) approach and MyoFascial Release (MFR) for improving plantar heel pain, ankle range of motion and disability: A randomized clinical trial.

[Purpose] The purpose of this study was to compare the effectiveness of the Structural Diagnosis and Management (SDM) approach with Myofascial Release (MFR) in improving plantar heel pain, ankle range of motion, and disability. [Subjects] Sixty-four subjects, aged 30-60 years, with a diagnosis of plantar heel pain, plantar fasciitis, or calcaneal spur by a physician according to ICD-10, were equally allocated to the MFR (n = 32) and SDM (n = 32) groups by hospital randomization and concealed allocation. [Methods] In this assessor-blinded randomized clinical trial, the control group performed MFR to the plantar surface of the foot, triceps surae, and deep posterior compartment calf muscles, while the experimental group performed a multimodal approach utilizing the SDM concept for 12 sessions over 4 weeks. Both groups also received strengthening exercises, ice compression, and ultrasound therapy. Pain, activity limitations and disability were assessed as primary outcomes using the Foot Function Index (FFI) and Range of motion (ROM) assessment of the ankle dorsiflexors and plantar flexors using a universal goniometer. Secondary outcomes were measured using the Foot Ankle Disability Index (FADI) and a 10-point manual muscle testing process for the ankle dorsiflexors and plantar flexors. [Results] Both MFR and SDM groups exhibited significant improvements from baseline in all outcome variables, including pain, activity level, disability, range of motion, and function after the 12-week intervention period (p < .05). The SDM group showed more improvements than MFR for FFI pain (p < .01), FFI activity (p < .01), FFI (p < .01) and FADI (p = <.01). [Conclusion] Both MFR and SDM approaches are effective in reducing pain, improving function, ankle range of motion, and reducing disability in plantar heel pain, however, the SDM approach may be a preferred treatment option.

An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty.

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.

Profiling of phytochemical and antioxidant activity of wild mushrooms: Evidence from the in vitro study and phytoconstituent's binding affinity to the human erythrocyte catalase and human glutathione reductase.

This study was undertaken to evaluate the appearance of phytochemicals and antioxidant activity of seven wild mushrooms of the University of Chittagong campus. Phytochemical screening was performed using standard methods, whereas DPPH radical scavenging assay was used to elucidate the antioxidant effect. Besides, in silico studies were implemented using the targets of human erythrocyte catalase 3-amino-1,2,4-triazole, human glutathione reductase, and selected compounds. Again, the absorption, distribution, metabolism, elimination and toxicity (ADME/T) analysis has been determined by using online tools. Both Ganoderma lucidum (Curtis) Karst. and Ganoderma applanatum (Pers.) Pat. showed a significant (p < .001) increase in the percentage of scavenging activity at 400 µg/ml concentration when compared with ascorbic acid. The methanol extract of G. lucidum, G. applanatum, and Rhodofomes cajanderi (P. Karst.) B. K. Cui, M. L. Han & Y. C. Dai showed strong antioxidant activity with an IC50 value. In addition, molecular docking studies of the previously isolated compounds from three selective mushrooms revealed that the targeted compounds along with positive controls were able to interact strongly (range: -3.498 to -8.655) with the enzymes. The study concludes that the G . lucidum, G. applanatum, and R. cajanderi mushrooms can be a strong source in the management of oxidative stress-induced diseases.

Wild mushrooms showed analgesic and cytotoxic properties along with phytoconstituent's binding affinity to COX-1, COX-2 and cytochrome P450 2C9.

This study was designed to evaluate the cytotoxic and analgesic potential of methanol extracts of five wild mushrooms available in the University of Chittagong, Bangladesh. The acetic acid-induced writhing method was used for the analgesic activity, while cytotoxicity was tested using brine shrimp lethality bioassay. In silico molecular docking and ADME/T study have been employed by using Schrodinger v11.1, BIOVIA Discovery Studio 2020 and online tool (AdmeSAR) respectively. The methanol extracts of Daldinia concentrica, Trametes lactinea, Fomitopsis cajanderi and Daedaleopsis confragosa exhibited a significant (p < 0.001) decrease in the number of writhing when compared to the control group. Except for Lentinus squarrosulus at 200 mg/kg body weight, the remaining mushroom extracts showed equal to or above 50 % inhibition of writhing. Daldinia concentrica showed the lowest LC50 values with 0.63 µg/mL, while Daedaleopsis confragosa showed the highest LC50 values of 2.33 µg/mL, indicating decisive cytotoxic action all mushrooms extracts. Considering the secondary metabolites, daldinan A and fomlactone A were found the most promising myco-compounds in analgesic and cytotoxic molecular docking studies. Besides, all the selected metabolites meet the rule of Lipinski's drug-likeliness. These results concluded that each mushroom extracts except Lentinus squarrosulus possess a potential analgesic. All the mushroom extracts embrace a promising cytotoxic activity that may guide the progress of a new drug.

Genotypic and phenotypic characterization of Streptococcus mutans strains isolated from patients with dental caries.

BACKGROUND AND OBJECTIVES: The oral cavity harbors numerous Streptococcus mutans strains which display remarkable genotypic and phenotypic diversity. This study evaluated the genotypic and phenotypic diversity of 209 S. mutans strains isolated from 336 patients with dental caries and compared with the universal reference strain, UA159. MATERIALS AND METHODS: Selective cultivation on mitis-salivaries-bacitracin agar and species-specific polymerase chain reaction (PCR) was carried out to isolate and identify the 209 S. mutans isolates from 336 patients with dental caries. Arbitrarily primed polymerase chain reaction (AP-PCR), PCR amplification of specific gene, acid production and biofilm formation capacity were performed to evaluate the genotypic and phenotypic variation. Student's t-test and Chi-square test were used for analysis of variables and a probability (P) of <0.05 was considered as significant. RESULTS: Our study revealed a high degree of genotypic and phenotypic variability among the clinical strains. We observed significant differences in colony morphology, generation time, biofilm formation, and acid production while growing in culture medium. All the clinical isolates were able to lower pH while growing in Todd-Hewitt broth. Consistent with phenotypic variations, we also observed genotypic variation by AP-PCR and gene specific PCR. AP-PCR analysis suggested that most of the patients with dental caries have distinct type of S. mutans strains. Genes related to various two component systems were highly conserved among the isolated strains, however, bacteriocin encoding genes such as nlmAB, nlmC were absent in nearly half of the clinical isolates. CONCLUSION: Our results support that S. mutans clinical isolates have wide genotypic diversity and show variation in growth kinetics, acid production, acid tolerance and biofilm formation capacity and indicates the presence of diverse mechanism to initiate and establish the biofilm lifestyle which leads to tooth decay.

Ganoderma applanatum mushroom provides new insights into the management of diabetes mellitus, hyperlipidemia, and hepatic degeneration: A comprehensive analysis.

This study was undertaken to evaluate the antidiabetic, hypolipidemic, and hepatoprotective effects of methanol and aqueous extracts of Ganoderma applanatum (MEGA, AEGA) in alloxan-induced diabetic rats. The antidiabetic study was implemented by the induction of alloxan to the rats. The analysis of the hypolipidemic and liver-protective effects of fungus extracts was studied by estimating the lipid profile and the liver marker enzymes. Besides, in silico screening of the compounds of Ganoderma applanatum has been incorporated thus to check the binding affinity of compounds and enzymes affinity. The Discovery Studio 2020, UCSF Chimera, and PyRx AutoDock Vina have been used to implement the docking analysis. Nine days of oral feeding of MEGA and AEGA of Ganoderma applanatum resulted in a significant (p < .001) reduction in blood glucose, lipid profile, and liver marker enzymes. Besides, Myrocin C scored the highest score in the docking study. The biological and computational approaches suggested the MEGA and AEGA could be a potential source for antidiabetic, hypolipidemic, and hepatoprotective effects.

Tendencies and attitudes towards dietary supplements use among undergraduate female students in Bangladesh.

BACKGROUND: Dietary supplements (DS) are products that improve the overall health and well-being of individuals and reduce the risk of disease. Evidence indicates a rising prevalence of the use of these products worldwide especially among the age group 18-23 years. AIM: The study investigates the tendencies and attitudes of Bangladeshi undergraduate female students towards dietary supplements (DS). METHODS: A three-month (March 2018-May 2018) cross-sectional face-to-face survey was conducted in undergraduate female students in Chittagong, Bangladesh using a pre-validated dietary supplement questionnaire. The study was carried among the four private and three public university students of different disciplines in Chittagong to record their prevalent opinions and attitudes toward using DS. The results were documented and analyzed by SPSS version 22.0. RESULTS: Ninety two percent (N = 925, 92.0%) of the respondents answered the survey questions. The prevalence of DS use was high in undergraduate female students. The respondents cited general health and well-being (n = 102, 11.0%) and physician recommendation (n = 101, 10.9%) as a reason for DS use. Majority of the students (n = 817, 88.3%) used DS cost monthly between USD 0.12 and USD 5.90. Most of the students (n = 749, 81.0%) agreed on the beneficial effect of DS and a significant portion (n = 493, 53.3%) recommended for a regular use of DS. Highly prevalent use of dietary supplements appeared in Chittagonian undergraduate female students. They were tremendously positive in using DS. The results demonstrate an increasing trend of using DS by the undergraduate females for both nutritional improvement and amelioration from diseases. CONCLUSION: Dietary supplements prevalence was so much higher in students of private universities as compared to students of public universities. Likewise, maximal prevalence is indicated in pharmacy department compared to other departments. Students preferred brand products, had positive opinions and attitudes towards dietary supplements.

Evaluation of morning glory (Jacquemontia tamnifolia (L.) Griseb) leaves for antioxidant, antinociceptive, anticoagulant and cytotoxic activities.

BACKGROUND: The present study was planned to investigate the phytochemical, antioxidant, antinociceptive, anticoagulant and cytotoxic activities of the Jacquemontia tamnifolia (L.) Griseb leaf methanol extract (MExJT) in the laboratory using both in vitro and in vivo methods. METHODS: Phytochemical values, namely, total phenolic and flavonoid contents, 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging effect and FeCl3 reducing power effects, were studied by established methods. In vivo antinociceptive activity was performed by acidic acid-induced writhing test and formalin-induced pain test on Swiss albino mice at doses of 125, 250 and 500 mg/kg body weight. The clot lysis and brine shrimp lethality bioassay in vitro were used to evaluate the thrombolytic and cytotoxic activities of the plant extract, respectively. RESULTS: Phytochemical screening illustrates the presence of tannins, saponins, flavonoids, gums and carbohydrates, steroids, alkaloids and reducing sugars in the extract. The results showed the total phenolic content (146.33 g gallic acid equivalents/100 g extract) and total flavonoid content (133.33 g quercetin/100 g). Significant (p<0.05) IC50 values compared to respective standards were recorded in DPPH radical scavenging (289.5 µg/mL) and FeCl3 reduction (245.2 µg/mL). The antinociceptive effect was evaluated in the acetic acid-induced writhing test and formalin-induced pain models in Swiss albino mice with doses of 125, 250 and 500 mg/kg body weight. Significant (p<0.05) inhibition (72.87±2.73%) of writhing response compared to diclofenac sodium was achieved by 500 mg/kg body weight. The extract also significantly inhibited the licking response in both the early phase (51.59±1.57%, p<0.05) and the late phase (64.82±1.87%, p<0.05) in the formalin-induced writhing test. MExJT also showed (38.10±1.79%) clot lytic activity in the thrombolytic test and cytotoxicity with an LC50 value of 31.70 µg/mL in the brine shrimp lethality bioassay. CONCLUSIONS: The plant is a potential source of antioxidants and might have one or more secondary metabolite(s) with central and peripheral analgesic activity. The results also demonstrate that MExJT has moderate thrombolytic and lower cytotoxic properties that may warrant further exploration.

Thymocytes in Lyve1-CRE/S1pr1f/f Mice Accumulate in the Thymus due to Cell-Intrinsic Loss of Sphingosine-1-Phosphate Receptor Expression.

T cell emigration from the thymus is essential for immunological homeostasis. While stromal cell-produced sphingosine-1-phosphate (S1P) has been shown to promote thymocyte egress via the S1P receptor, S1PR1, the significance of S1P/S1PR1 signaling in the thymic stromal cells that surround T cells remains unclear. To address this issue, we developed conditional knockout mice (Lyve1-CRE/S1pr1f/f mice) in which S1pr1 was selectively targeted in cells expressing the lymphatic endothelial cell marker, Lyve1. In these mice, T cells were significantly reduced in secondary lymphoid tissues, and CD62L+ mature CD4 and CD8 single-positive (SP) T cells accumulated in the medulla failed to undergo thymus egress. Using a Lyve1 reporter strain in which Lyve1 lineage cells expressed tdTomato fluorescent protein, we unexpectedly found that a considerable proportion of the thymocytes were fluorescently labeled, indicating that they belonged to the Lyve1 lineage. The CD4 and CD8 SP thymocytes in Lyve1-CRE/S1pr1f/f mice exhibited an egress-competent phenotype (HSAlow, CD62Lhigh, and Qa-2high), but were CD69high and lacked S1PR1 expression. In addition, CD4 SP thymocytes from these mice were unable to migrate to the periphery after their intrathymic injection into wild-type (WT) mice. In contrast, WT T cells could migrate to the periphery in both WT and Lyve1-CRE/S1pr1f/f thymuses. These results demonstrated that thymocyte egress is mediated by T cell-expressed, but not stromal cell-expressed, S1PR1 and caution against using the Lyve1-CRE system for selectively gene deletion in lymphatic endothelial cells.

Effects of conventional immunosuppressive treatment on CD244+ (CD28null) and FOXP3+ T cells in the inflamed muscle of patients with polymyositis and dermatomyositis.

BACKGROUND: T-cell infiltrates may persist in muscle tissue of polymyositis (PM) and dermatomyositis (DM) patients despite aggressive immunosuppressive treatment. Here, we investigated to what extent persistent T cells in affected muscle were FOXP3+, a marker for regulatory T cells (Tregs), or CD244+, a marker for CD28null T cells, and whether their presence correlated to clinical outcome. The sensitivity of CD28null T cells towards glucocorticoid and Treg-mediated immunosuppression was also investigated. METHODS: Muscle biopsies from 16 newly diagnosed or untreated patients with PM/DM were investigated by immunohistochemistry for expression of CD3, FOXP3 and CD244 before and after treatment with glucocorticoids and immunosuppressive agents. For clinical evaluation, serum levels of creatine kinase, muscle performance (FI and MMT8), disease activity (MITAX) and disability (HAQ) were measured. In vitro suppressive effects of glucocorticoids and Tregs on T-cell activation were measured by CD69 upregulation. RESULTS: Before treatment, CD244+ cells were present at higher proportions compared to FOXP3+ cells in the inflamed muscle. Following treatment, FOXP3+ cell numbers decreased while CD244+ cells persisted. Patients with impaired muscle function (<75 % FI) post-treatment had higher levels of CD244+ cells in the follow-up biopsy compared to those with FI >75 %. MITAX and HAQ correlated with the number of CD244+ cells post-treatment. CD4+CD28null T cells displayed lower sensitivity towards both glucocorticoid and Treg-mediated immunosuppression in vitro compared to their CD28+ counterparts. CONCLUSIONS: Poor outcome in patients with myositis following immunosuppressive therapy was linked to persistence of CD244+ (CD28null) T cells in muscle tissue, suggesting their resistance against immunosuppression. A relative loss of regulatory T cells could also contribute to poor clinical outcome given their recently ascribed role in muscle tissue regeneration.