Prebiotic diet normalizes aberrant immune and behavioral phenotypes in a mouse model of autism spectrum disorder.

Autism spectrum disorder (ASD) is a cluster of neurodevelopmental disorders characterized by deficits in communication and behavior. Increasing evidence suggests that the microbiota-gut-brain axis and the likely related immune imbalance may play a role in the development of this disorder. Gastrointestinal deficits and gut microbiota dysfunction have been linked to the development or severity of autistic behavior. Therefore, treatments that focus on specific diets may improve gastrointestinal function and aberrant behavior in individuals with ASD. In this study, we investigated whether a diet containing specific prebiotic fibers, namely, 3% galacto-oligosaccharide/fructo-oligosaccharide (GOS/FOS; 9:1), can mitigate the adverse effects of in utero exposure to valproic acid (VPA) in mice. Pregnant BALB/cByJ dams were injected with VPA (600 mg/kg, sc.) or phosphate-buffered saline (PBS) on gestational day 11 (G11). Male offspring were divided into four groups: (1) in utero PBS-exposed with a control diet, (2) in utero PBS-exposed with GOS/FOS diet, (3) in utero VPA-exposed with a control diet, and (4) in utero VPA-exposed with GOS/FOS diet. Dietary intervention started from birth and continued throughout the duration of the experiment. We showed that the prebiotic diet normalized VPA-induced alterations in male offspring, including restoration of key microbial taxa, intestinal permeability, peripheral immune homeostasis, reduction of neuroinflammation in the cerebellum, and impairments in social behavior and cognition in mice. Overall, our research provides valuable insights into the gut-brain axis involvement in ASD development. In addition, dietary interventions might correct the disbalance in gut microbiota and immune responses and, ultimately, might improve detrimental behavioral outcomes in ASD.

Infection prevention and control for COVID-19 response in the Rohingya refugee camps in Bangladesh: an intra-action review.

BACKGROUND: Infection Prevention and Control (IPC) is critical in controlling the COVID-19 pandemic and is one of the pillars of the WHO COVID-19 Strategic Preparedness and Response Plan 2020. We conducted an Intra-Action Review (IAR) of IPC response efforts to the COVID-19 pandemic in Cox's Bazar, Bangladesh, to identify best practices, challenges, and recommendations for improvement of the current and future responses. METHODS: We conducted two meetings with 54 participants purposively selected from different organizations and agencies involved in the frontline implementation of IPC in Cox's Bazar district, Bangladesh. We used the IPC trigger questions from the WHO country COVID-19 IAR: trigger question database to guide the discussions. Meeting notes and transcripts were then analyzed manually using content analysis, and results were presented in text and quotes. RESULTS: Best practices included: assessments, a response plan, a working group, trainings, early case identification and isolation, hand hygiene in Health Facilities (HFs), monitoring and feedback, general masking in HFs, supportive supervision, design, infrastructure and environmental controls in Severe Acute Respiratory Infection Isolation and Treatment Centers (SARI ITCs) and HFs and waste management. Challenges included: frequent breakdown of incinerators, limited PPE supply, inconsistent adherence to IPC, lack of availability of uniforms for health workers, in particular cultural and gender appropriate uniforms and Personal Protective Equipment (PPE). Recommendations from the IAR were: (1) to promote the institutionalization of IPC, programs in HFs (2) establishment of IPC monitoring mechanisms in all HCFs, (3) strengthening IPC education and training in health care facilities, and (4) strengthen public health and social measures in communities. CONCLUSION: Establishing IPC programmes that include monitoring and continuous training are critical in promoting consistent and adaptive IPC practices. Response to a pandemic crisis combined with concurrent emergencies, such as protracted displacement of populations with many diverse actors, can only be successful with highly coordinated planning, leadership, resource mobilization, and close supervision.

Bioengineered Kidney Tubules Efficiently Clear Uremic Toxins in Experimental Dialysis Conditions.

Patients with end-stage kidney disease (ESKD) suffer from high levels of protein-bound uremic toxins (PBUTs) that contribute to various comorbidities. Conventional dialysis methods are ineffective in removing these PBUTs. A potential solution could be offered by a bioartificial kidney (BAK) composed of porous membranes covered by proximal tubule epithelial cells (PTECs) that actively secrete PBUTs. However, BAK development is currently being hampered by a lack of knowledge regarding the cytocompatibility of the dialysis fluid (DF) that comes in contact with the PTECs. Here, we conducted a comprehensive functional assessment of the DF on human conditionally immortalized PTECs (ciPTECs) cultured as monolayers in well plates, on Transwell® inserts, or on hollow fiber membranes (HFMs) that form functional units of a BAK. We evaluated cell viability markers, monolayer integrity, and PBUT clearance. Our results show that exposure to DF did not affect ciPTECs' viability, membrane integrity, or function. Seven anionic PBUTs were efficiently cleared from the perfusion fluid containing a PBUTs cocktail or uremic plasma, an effect which was enhanced in the presence of albumin. Overall, our findings support that the DF is cytocompatible and does not compromise ciPTECs function, paving the way for further advancements in BAK development and its potential clinical application.

Animal Models for Studying Protein-Bound Uremic Toxin Removal-A Systematic Review.

Protein-bound uremic toxins (PBUTs) are associated with the progression of chronic kidney disease (CKD) and its associated morbidity and mortality. The conventional dialysis techniques are unable to efficiently remove PBUTs due to their plasma protein binding. Therefore, novel approaches are being developed, but these require validation in animals before clinical trials can begin. We conducted a systematic review to document PBUT concentrations in various models and species. The search strategy returned 1163 results for which abstracts were screened, resulting in 65 full-text papers for data extraction (rats (n = 41), mice (n = 17), dogs (n = 3), cats (n = 4), goats (n = 1), and pigs (n = 1)). We performed descriptive and comparative analyses on indoxyl sulfate (IS) concentrations in rats and mice. The data on large animals and on other PBUTs were too heterogeneous for pooled analysis. Most rodent studies reported mean uremic concentrations of plasma IS close to or within the range of those during kidney failure in humans, with the highest in tubular injury models in rats. Compared to nephron loss models in rats, a greater rise in plasma IS compared to creatinine was found in tubular injury models, suggesting tubular secretion was more affected than glomerular filtration. In summary, tubular injury rat models may be most relevant for the in vivo validation of novel PBUT-lowering strategies for kidney failure in humans.

An Observational Study of Paediatric Preoperative Transfusion Practice in a Resource-Limited Setting.

BACKGROUND: Paediatric anaemia is highly prevalent in low-middle-income countries and can negatively impact postoperative outcomes. Currently, there are no guidelines for the management of paediatric preoperative anaemia. To ensure optimal care in resource-limited settings: balancing the risks of anaemia and using resources such as blood transfusion, we first need to understand current practices. To address this, a joint UK-Bangladesh team conducted an observational study at a paediatric surgical centre in Bangladesh. METHODS: A total of 464 patients ≤16 years who underwent elective and emergency surgery were categorised into major (351/464), moderate (92/464) and minor (21/464) surgery groups according to anticipated blood loss. Preoperative anaemia testing and transfusion was assessed retrospectively through patient notes. RESULTS: Median age was 4 years and 73% were male. 32.5% (151/464) patients had preoperative blood testing for anaemia. 17.5% (81/464) children were transfused preoperatively. Of those children transfused, 40.7% (33/81) underwent transfusion solely based on visible signs of anaemia on clinical examination. Seventy-five percentage (36/48) of children who underwent transfusion after blood testing had haemoglobin ≥80 g/L. Major surgery category had the highest proportion of children who were transfused and tested for anaemia. CONCLUSION: A liberal transfusion approach is evident here. Discussion with local clinicians revealed that this was due to limitations in obtaining timely blood results and reduction in laboratory costs incurred by families when clinical suspicion of anaemia was high. Further research is needed to analyse the potential of using bedside haemoglobin testers in conjunction with patient blood management strategies to limit blood transfusions and its associated risks.

A robust, accurate, sensitive LC-MS/MS method to measure indoxyl sulfate, validated for plasma and kidney cells.

Proximal tubular damage is an important prognostic determinant in various chronic kidney diseases (CKDs). Currently available diagnostic methods do not allow for early disease detection and are neither efficient. Indoxyl sulfate (IS) is an endogenous metabolite and protein-bound uremic toxin that is eliminated via renal secretion, but accumulates in plasma during tubular dysfunction. Therefore, it may be suitable as a tubular function marker. To evaluate this, a fast bioanalytical method was developed and validated for IS in various species and a kidney cell line using LC-MS/MS. An isotope-labeled IS potassium salt as an internal standard and acetonitrile (ACN) as a protein precipitant were used for sample pretreatment. The analyte was separated on a Polaris 3 C18-A column by gradient elution using 0.1% formic acid in water and ACN, and detected by negative electrospray ionization in selected reaction monitoring mode. The within-day (≤ 4.0%) and between-day (≤ 4.3%) precisions and accuracies (97.7 to 107.3%) were within the acceptable range. The analyte showed sufficient stability at all conditions investigated. Finally, applying this assay, significantly higher plasma and lower urine concentrations of IS were observed in mice with diabetic nephropathy with tubular damage, which encourages validation toward its use as a biomarker.

The Autism Spectrum Disorder-Associated Bacterial Metabolite p-Cresol Derails the Neuroimmune Response of Microglial Cells Partially via Reduction of ADAM17 and ADAM10.

The bacterial metabolite 4-methylphenol (para-cresol or p-cresol) and its derivative p-cresyl sulfate (pCS) are elevated in the urine and feces of children with autism spectrum disorder (ASD). It has been shown that p-cresol administration induces social behavior deficits and repetitive behavior in mice. However, the mechanisms of p-cresol, specifically its metabolite pCS that can reach the brain, in ASD remain to be investigated. The pCS has been shown to inhibit LPS-stimulated inflammatory response. A Disintegrin And Metalloprotease 10 (ADAM10) and A Disintegrin And Metalloprotease 17 (ADAM17) are thought to regulate microglial immune response by cleaving membrane-bound proteins. In the present study, a neuroinflammation model of LPS-activated BV2 microglia has been used to unveil the potential molecular mechanism of pCS in ASD pathogenesis. In microglial cells pCS treatment decreases the expression or maturation of ADAM10 and ADAM17. In addition, pCS treatment attenuates TNF-α and IL-6 releases as well as phagocytosis activity of microglia. In in vitro ADAM10/17 inhibition experiments, either ADAM10 or ADAM17 inhibition reduces constitutive and LPS-activated release of TNF-α, TNFR-1 and IL-6R by microglial cells, while it increases constitutive and LPS-activated microglial phagocytotic activity. The in vivo results further confirm the involvement of ADAM10 and ADAM17 in ASD pathogenesis. In in utero VPA-exposed male mice, elevated concentration in serum of p-cresol-associated metabolites pCS and p-cresyl glucuronide (pCG) is associated with a VPA-induced increased ADAM10 maturation, and a decreased ADAM17 maturation that is related with attenuated levels of soluble TNF-α and TGF-ß1 in the mice brain. Overall, the present study demonstrates a partial role of ADAM10 and ADAM17 in the derailed innate immune response of microglial cells associated with pCS-induced ASD pathogenesis.

Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves (Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes.

Piper betle L. is widely distributed and commonly used medicinally important herb. It can also be used as a medication for type 2 diabetes patients. In this study, compounds of P. betle were screened to investigate the inhibitory action of alpha-amylase and alpha-glucosidase against type 2 diabetes through molecular docking, molecular dynamics simulation, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis. The molecule apigenin-7-O-glucoside showed the highest binding affinity among 123 (one hundred twenty-three) tested compounds. This compound simultaneously bound with the two-target proteins alpha-amylase and alpha-glucosidase, with high molecular mechanics-generalized born surface area (MM/GBSA) values (ΔG Bind = -45.02 kcal mol-1 for alpha-amylase and -38.288 for alpha-glucosidase) compared with control inhibitor acarbose, which had binding affinities of -36.796 kcal mol-1 for alpha-amylase and -29.622 kcal mol-1 for alpha-glucosidase. The apigenin-7-O-glucoside was revealed to be the most stable molecule with the highest binding free energy through molecular dynamics simulation, indicating that it could compete with the inhibitors' native ligand. Based on ADMET analysis, this phytochemical exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities and had no significant side effects, making them prospective drug candidates for type 2 diabetes. Additional in vitro, in vivo, and clinical investigations are needed to determine the precise efficacy of drugs.

Kidney-based in vitro models for drug-induced toxicity testing.

The kidney is frequently involved in adverse effects caused by exposure to foreign compounds, including drugs. An early prediction of those effects is crucial for allowing novel, safe drugs entering the market. Yet, in current pharmacotherapy, drug-induced nephrotoxicity accounts for up to 25% of the reported serious adverse effects, of which one-third is attributed to antimicrobials use. Adverse drug effects can be due to direct toxicity, for instance as a result of kidney-specific determinants, or indirectly by, e.g., vascular effects or crystals deposition. Currently used in vitro assays do not adequately predict in vivo observed effects, predominantly due to an inadequate preservation of the organs' microenvironment in the models applied. The kidney is highly complex, composed of a filter unit and a tubular segment, together containing over 20 different cell types. The tubular epithelium is highly polarized, and the maintenance of this polarity is critical for optimal functioning and response to environmental signals. Cell polarity is dependent on communication between cells, which includes paracrine and autocrine signals, as well as biomechanic and chemotactic processes. These processes all influence kidney cell proliferation, migration, and differentiation. For drug disposition studies, this microenvironment is essential for prediction of toxic responses. This review provides an overview of drug-induced injuries to the kidney, details on relevant and translational biomarkers, and advances in 3D cultures of human renal cells, including organoids and kidney-on-a-chip platforms.

A Passive Tracking System Based on Geometric Constraints in Adaptive Wireless Sensor Networks.

Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.

Evaluation of THz wave transmission performance in TOPAS-based heptagonal photonic crystal fiber (He-PCF).

PCF denotes photonic crystal fiber which is utilized for terahertz (THz) waveguides and cladding in the shape of a hexagon with two elliptical air apertures (AHs), which are discussed. Such differentiation is made: When the frequency is 1 THz, effective material loss (EML) to a minimum of 0.028 cm-1 has been achieved. Making use of the heptagonal photonic crystal fiber (He-PCF) architecture, every simulation result utilizing COMSOL Multiphysics software implements the perfectly match layer (PML) and finite element method (FEM) boundary conditions. The He-PCF fiber demonstrates an effective mode loss (EML) of 0.028 cm-1 that is negligible, a substantial effective area (EA) measuring 7.31 × 10-8 m2 and an 80 % power concentration encompassing the central area at 1 THz frequency. Furthermore, regarding crucial optical guiding aspects like confinement loss, dispersion, and modality, a small study with respect to power fraction along with effective mode area (EMA) has again been conducted. Here, He-PCF THz waveguide is anticipated to provide a notable improvement in the current design for the communication field. Moreover, our suggested the PCF demonstrates perception by a solitary mode, as indicated through the utilization of the V-parameter, across a range in frequency spanning among 0.80 and 3 THz. Thus, it is anticipated that the layout of He-PCF fibers will facilitate efficient transmission of terahertz (THz) signals in a variety of communication applications.

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application.

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML-CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/.

Stakeholder engagement to introduce a standardised register for improved inpatient care of newborns and sick children in Bangladesh.

Background: Despite a global decrease of 59% in under-five mortality rates from 1990 to 2021, child survival remains a pressing issue. This holds true for Bangladesh, as well. In response, the Government of Bangladesh introduced a standardised register for strengthening the inpatient management of newborns and sick children in 2021. Methods: We employed a comprehensive four-phase stakeholder engagement process to implement an inpatient register for newborns and sick children. The first stage included identifying and prioritising potential stakeholders at the national and district levels. We identified eight organisations involved in newborn and child health and selected 24 participants from various other sectors for workshops aimed at raising awareness about the register's introduction. These stakeholders also participated in the register's design, development strategies planning, and implementation phases. These phases were led by the 'National Newborn Health and IMCI programme' with support from various partners. A technical working group reviewed existing registers and helped prepare training materials. Feedback from each workshop was crucial in finalising the register. Results: The Government of Bangladesh has recognised the need for an indoor register for newborns and sick children, which was to be established in collaboration with development partners. This initiative can enhance the quality of care for sick children and increase service provider accountability. Due to its successful implementation, it will continue to be used in the Kushtia and Dinajpur districts, with plans for a nationwide scale-up. The Government has allocated funds in the next health sector programme for orientation and register printing. A strengths, weaknesses, opportunities, and threats (SWOT) analysis of the stakeholder engagement process highlighted strengths such as a context-specific approach and collaborative engagement, as well as challenges such as time resource requirements. Conclusions: Implementing an inpatient register for newborns and sick children through stakeholder engagement can effectively improve child health care services. Aside from challenges such as resource intensiveness and stakeholder commitments, success depended on the organising authority's expertise in relationship building, budget allocation, time management, and workforce dedication. Therefore, strategic planning, staff recruitment, networking, and budgeting are crucial for successful stakeholder engagement and health care initiatives.

Introducing a standardised register for strengthening the inpatient management of newborns and sick children: Implementation research in selected health facilities of Bangladesh.

Background: It is imperative to maintain accurate documentation of clinical interventions aimed at enhancing the quality of care for newborns and sick children. The National Newborn Health and IMCI programme of Bangladesh led the development of a standardised register for managing newborns and sick children under five years of age during inpatient care through stakeholder engagement. We aimed to assess the implementation outcomes of the standardised register in the inpatient department. Methods: We conducted implementation research in two district hospitals and two sub-district hospitals of Kushtia and Dinajpur districts from November 2022 to January 2023 to assess the implementation outcomes of the standardised register. We assessed the following World Health Organization implementation outcome variables: usability, acceptability, adoption (actual use), fidelity (completeness and accuracy), and utility (quality of care) of the register against preset benchmarks. We collected data through structured interviews with health care providers; participant enrolment; and data extraction from inpatient registers and case record forms. Results: The average usability and acceptability scores among health care providers were 73 (standard deviation (SD) = 14) and 82 (SD = 14) out of 100, respectively. The inpatient register recorded 96% (95% confidence interval (CI) = 95-97) of under-five children who were admitted to the inpatient department (adoption - actual use). The proportions of completed data elements in the inpatient register were above the preset benchmark of 70% for all the assessed data elements except 'investigation done' (24%; 95% CI = 23-26) (fidelity - completeness). The percentage agreements between government-appointed nurses posted and study-appointed nurses were above the preset benchmark of 70% for all the reported variables (fidelity - accuracy). The kappa coefficient for the overall level of agreement between these two groups regarding reported variables indicated moderate to substantial agreement. The proportion of newborns with sepsis receiving injectable antibiotics was 62% (95% CI = 47-75) (utility - quality of care). We observed some variability in the completeness and accuracy of the inpatient register by district and facility type. Conclusions: The inpatient register was positively received by health care providers, with evaluations of implementation outcome variables showing encouraging results. Our findings could inform evidence-based decision-making on the implementation and scale-up of the inpatient register in Bangladesh, as well as other low- and middle-income countries.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets.

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.

17 common differentially expressed genes (DEGs) were identified from 693 DEGs of the GBS dataset (GSE72748) and 365 DEGs of the ASD dataset (GSE113834), which is the preliminary part of this investigation.From the PPI network analysis, a total of 10 hub genes were identified and two common DEGs named CXCL9 and CXCL10 were found in both the hub gene and essential module analysis.The identified leading pathways and GO pathways, TF-gene interaction, and TF-miRNAs network has made the process more relevant and appropriate for suggesting probable drug candidates.Among the drug candidates, imatinib was suggested as the main drug candidate due to its interaction with the hub gene CXCL9 and CXCL10 and lower p value than the other candidates. It showed the highest binding affinity score and remained stable with the CXCR3 chemokine receptor.

Nutritional characteristics and antiradical activity of turmeric (Curcuma longa L.), beetroot (Beta vulgaris L.), and carrot (Daucus carota L.) grown in Bangladesh.

Curcuma longa L. (turmeric), Beta vulgaris L. (beetroot), and Daucus carota L. (carrot) grown in Bangladesh were analyzed for nutritional and phytochemical contents to reveal their comparative nutritional compositions and antiradical properties. Ash, protein, and carbohydrate content were significantly preeminent in beetroot as compared to others. Whereas fat content was found to be high in turmeric, carrots contained a great percentage of crude fiber. Beetroot was shown to have much greater potassium, calcium, and iron levels than others. Regarding amino acids, glutamic acid was found to be greater in beetroot and carrot whereas turmeric had significantly high aspartic acid content. Leucine had the highest concentration among essential amino acids in these three samples. Total antioxidant activity, total flavonoids, and phenolic contents in the methanolic extract of turmeric were found to be substantially higher than in beetroot and carrot. Furthermore, the extract of turmeric (IC50 value: 13.46 µg/mL) scavenged 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) free radicals considerably to a greater extent than beetroot (IC50 value: 380.61 µg/mL) and carrot (IC50 value: 1252.85 µg/mL). A positive correlation was found between the phytochemical contents and antiradical activity. The information from this study will help to find the potential ingredients from these plants for functional food.

In vitro cytotoxic activity of Aesculus indica against breast adenocarcinoma cell line (MCF-7) and phytochemical analysis.

Aesculus indica (Linn.) (Sapindaceae) is an ethanobotanically important plant specie traditionally used against rheumatism, skin and vein complaints. Cytotoxic potential of Aesculus indica crude leaf extract and its fractions was investigated against MCF-7 cell line. Crude extract of Aesculus indica was prepared in methanol by maceration technique. Crude extract was fractionated into four organic and one aqueous fraction on polarity basis. MTT assay was used to evaluate the reduction of viability of MCF-7 breast cancer cell line. Cell viability was inhibited by Aesculus indica crude extract in a dose dependent manner ranging from 34.2% at 10 µg/ml to 94% at 500µg/ml. Activity was found in an ascending order from hexane showing 29.8% inhibition to aqueous fraction indicating maximum inhibition, 60%. Phytochemical analysis of crude and fractionated extracts revealed presence of flavonoids, saponins, coumarins and tannins upto varying degrees. Methanol and aqueous fraction of methanol extract of Aesculus indica can be good source of cytotoxic compounds.

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning.

The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor.

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity, using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset (H-Activity), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.

Green synthesis of silver nanoparticles using Cinnamomum tamala (Tejpata) leaf and their potential application to control multidrug resistant Pseudomonas aeruginosa isolated from hospital drainage water.

Green Synthesis of Metal Nanoparticles is becoming a more common method for producing nanoparticles with a diameter of 1-100 nm that may be employed in a variety of medical applications. The antibacterial efficacy of silver nanoparticles (AgNPs) derived from Cinnamomum tamala (Tejpata) leaf extract against antibiotic-resistant Pseudomonas aeruginosa is investigated in this study. Green AgNP synthesis is safe, cost-effective, and ecologically friendly. The biosynthesized AgNPs were studied using UV-Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Dynamic Light Scattering (DLS), X-ray Diffraction (XRD), and Transmission Electron Microscopy (TEM). The AgNPs were virtually spherical, with an average size of 25-30 nm, according to TEM observations. Biochemical and molecular identification were used to isolate multidrug-resistant P. aeruginosa from the hospital's drainage water. The antibacterial potential of AgNPs against P. aeruginosa is determined using the agar diffusion method. Silver nanoparticles produced from Cinnamomum tamala (Tejpata) leaf extract were shown to be effective in inhibiting four strains of P. aeruginosa. According to the agar disc diffusion method, AgNPs had the largest inhibition zone of 17.67 ± 0.577 mm, while aqueous extract had 5.67 ± 0.5777 mm, indicating that AgNPs had antibacterial activity. This study on AgNPs might assist with managing multidrug resistant pathogenic bacteria and be a possible source of medicinal application due to its potential antibacterial effect.