Green Synthesized Chitosan Nanoparticles for Controlling Multidrug-Resistant mecA- and blaZ-Positive Staphylococcus aureus and aadA1-Positive Escherichia coli.

Antimicrobial resistance has recently been considered an emerging catastrophe globally. The public health and environmental threats were aggravated by the injudicious use of antibiotics in animal farming, aquaculture, and croup fields, etc. Consequently, failure of antibiotic therapies is common because of the emergence of multidrug-resistant (MDR) bacteria in the environment. Thus, the reduction in antibiotic spillage in the environment could be an important step for overcoming this situation. Bear in mind, this research was focused on the green synthesis of chitosan nanoparticles (ChiNPs) using Citrus lemon (Assam lemon) extract as a cross-linker and application in controlling MDR bacteria to reduce the antibiotic spillage in that sector. For evaluating antibacterial activity, Staphylococcus aureus and Escherichia coli were isolated from environmental specimens, and their multidrug-resistant pattern were identified both phenotypically by disk diffusion and genotypically by detecting methicillin- (mecA), penicillin- (blaZ), and streptomycin (aadA1)-resistance encoding genes. The inhibitory zone's diameter was employed as a parameter for determining the antibacterial effect against MDR bacteria revealing 30 ± 0.4 mm, 34 ± 0.2 mm, and 36 ± 0.8 mm zones of inhibition against methicillin- (mecA) and penicillin (blaZ)-resistant S. aureus, and streptomycin (aadA1)-resistant E. coli, respectively. The minimum inhibitory concentration at 0.31 mg/mL and minimum bactericidal concentration at 0.62 mg/mL of yielded ChiNPs were used as the broad-spectrum application against MDR bacteria. Finally, the biocompatibility of ChiNPs was confirmed by showing a negligible decrease in BHK-21 cell viability at doses less than 2 MIC, suggesting their potential for future application in antibiotic-free farming practices.

Development of sustainable dual core-spun yarns using several filaments and recycled cotton sourced from pre-consumer fabric waste.

Textile industries are now focusing on sustainable issues in manufacturing operations to save the environment. The study focuses on the use of cotton fibers (recycled) sourced from fabric (knitted) waste (pre-consumer) to manufacture elastic yarn (dual-core) for denim fabric. The study involves the production of yarns (dual-core) using a redesigned ring spinning method with different elastomeric components, including T400® (Polyethylene terephthalate)/Polytrimethylene terephthalate), Polybutylene terephthalate (PBT), Polyester (PES), Lycra® (elastane), virgin cotton and cotton (recycled) fiber. The study investigates various yarn (Ne 18/1) characteristics such as strength, IPI (imperfection index), elongation %, unevenness %, and hairiness. It is noticed that the elongation and strength of recycled yarn (double core) are lower and IPI (Imperfection index), unevenness %, and hairiness values are higher than 100 % cotton (virgin) yarn (double core). One-way ANOVA (statistical analysis) is employed to assess the significance of differences among yarns manufactured from various core materials and found significant variation for all characteristics. Additionally, the article introduces the MOORA (multi-objective optimization based on ratio analysis) technique as a decision-making tool to determine the best yarn among three alternatives (PES yarn, PBT yarn, and T400 yarn) based on their properties, considering attributes and finding T400 filament containing yarn as the best option. The study introduces a sustainable approach using recycled cotton in yarn (double core) production and employs decision-making tools to assess and rank the performance of different yarn alternatives.

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors.

Recent studies have shown the potential of wearable sensors for objective detection of health and safety risks in construction workers through their collected physiological data. Body temperature, as the focus of the current study, is one of the most important physiological parameters that can help to detect various health and safety risks such as heat stress, physical fatigue, and infectious diseases. This study aims to assess the applicability and performance of off-the-shelf wearable sensor devices to monitor workers' body temperature in construction sites by evaluating the accuracy of temperature measurements as well as the comfort of the devices. A total of nine off-the-shelf wearable sensor devices available on the market were initially trialed in the laboratory, and three devices were shortlisted considering a set of selection criteria for further assessment. Over three weeks, the shortlisted wearable sensors were tested on 26 workers in two large construction sites in Australia. The reliability/validity of the selected wearable sensors in measuring body temperature was investigated using Bland-Altman analysis. Human factors were also investigated in terms of the comfort of the devices, their impact on workers' performance, and the acceptability of being worn for an extended period (i.e., 8 h or more). It was found that all selected devices measured body temperature with a bias of less than one indicating a slight difference in measurements compared to the reference hospital-grade thermometers. Two devices out of the three were also comfortable. The achieved results indicate that it is feasible to develop a continuous temperature monitoring platform using off-the-shelf wearable sensors to detect a range of significant health and safety risks in construction sites objectively. Considering the rapid advancements in manufacturing wearable sensors, future research can adopt a similar approach to include the newly introduced off-the-shelf temperature sensors and select the most appropriate device.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Aflatoxin M1 Analysis in Urine of Mill Workers in Bangladesh: A Pilot Study.

Presence of aflatoxin B1 (AFB1) in food and feed is a serious problem, especially in developing countries. Human exposure to this carcinogenic mycotoxin can occur through dietary intake, but also through inhalation or dermal contact when handling and processing AFB1-contaminated crops. A suitable biomarker of AFB1 exposure by all routes is the occurrence of its hydroxylated metabolite aflatoxin M1 (AFM1) in urine. To assess mycotoxin exposure in mill workers in Bangladesh, we analyzed AFM1 levels in urine samples of this population group who may encounter both dietary and occupational AFB1 exposure. In this pilot study, a total of 76 participants (51 mill workers and 25 controls) were enrolled from the Sylhet region of Bangladesh. Urine samples were collected from people who worked in rice, wheat, maize and spice mills and from controls with no occupational contact to these materials. A questionnaire was used to collect information on basic characteristics and normal food habits of all participants. Levels of AFM1 in the urine samples were determined by a competitive enzyme linked immunosorbent assay. AFM1 was detected in 96.1% of mill workers' urine samples with a range of LOD (40) of 217.7 pg/mL and also in 92% of control subject's urine samples with a range of LOD of 307.0 pg/mL). The mean level of AFM1 in mill workers' urine (106.5 ± 35.0 pg/mL) was slightly lower than that of the control group (123.3 ± 52.4 pg/mL), whilst the mean AFM1 urinary level adjusted for creatinine was higher in mill workers (142.1 ± 126.1 pg/mg crea) than in the control group (98.5 ± 71.2 pg/mg crea). Yet, these differences in biomarker levels were not statistically significant. Slightly different mean urinary AFM1 levels were observed between maize mill, spice mill, rice mill, and wheat mill workers, yet biomarker values are based on a small number of individuals in these subgroups. No significant correlations were found between the study subjects' urine AFM1 levels and their consumption of some staple food items, except for a significant correlation observed between urinary biomarker levels and consumption of groundnuts. In conclusion, this pilot study revealed the frequent presence of AFM1 in the urine of mill workers in Bangladesh and those of concurrent controls with dietary AFB1 exposure only. The absence of a statistical difference in mean biomarker levels for workers and controls suggests that in the specific setting, no extra occupational exposure occurred. Yet, the high prevalence of non-negligible AFM1 levels in the collected urines encourage further studies in Bangladesh regarding aflatoxin exposure.

Occurrence of ochratoxin A in breast milk and urine samples of nursing mothers in Bangladesh.

The mycotoxin ochratoxin A (OTA) is a potent nephrotoxin with carcinogenic properties and, thus, of concern as a food contaminant. Since food contaminant data are scarce in Bangladesh, we applied human biomonitoring to gain more insights into OTA exposure in the country's population. OTA concentrations in human milk and urine samples of nursing mothers were determined with the aim to assess also exposure to this mycotoxin in breastfed infants. Breastfeeding mothers (n = 74) from three districts of Bangladesh (Sylhet, Cumilla, and Mymensingh region) participated in this study. They provided demographic data, along with breast milk and urine samples. OTA levels were measured by a competitive enzyme-linked immunosorbent assay (ELISA) with a detection limit of 60 ng/L for milk and 30 ng/L for urine.OTA was detected in 62.2% of all breast milk samples (mean 74.8 ± 49.0 ng/L, range < LOD-243.3 ng/L) and in 51.4% of all urine samples (mean 44.3 ± 63.5 ng/L, range < LOD-519.3 ng/L). The differences observed between regions for mean breast milk or for urinary OTA levels were relatively small. No significant correlation was observed between OTA levels in breast milk and food consumption patterns among nursing mothers. Regarding infant exposure, the estimated average daily intake of OTA for all was 15.0 ng/kg bw/day (range 4.5-45 ng/kg bw/day). In 34.5% of these infants, their estimated daily OTA intake exceeded a preliminary TDI value set by EFSA (17 ng/kg bw/day). The mean OTA intake was slightly higher (16.2 ± 7.8 ng/kg bw/day) in 1-2 months babies than in older infants (< 2 to 12 months), although the difference was not significant. Presence of OTA in most milk and urine samples of nursing mothers documents their widespread dietary mycotoxin exposure. Although based on a relatively small number of participants, the present analysis indicates non-negligible exposure of some nursed infants in Bangladesh. Therefore, further biomonitoring studies and investigations on major sources of OTA in food commodities are encouraged.

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis.

BACKGROUND: Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions. OBJECTIVE: This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications. METHODS: Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors. RESULTS: Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles. CONCLUSIONS: The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

Removal of hazardous textile dye from simulated wastewater by municipal organic solid waste charcoal using machine learning approaches: Kinetics, isotherm, and thermodynamics.

This study focuses on the probable use of municipal organic solid waste charcoal (MOSWC) as an adsorbent for Methyl orange (MO) adsorption. The prepared MOSWC is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), adsorbate concentration (60-140 mg/L), adsorbent dose (1-5 g/L), pH (3-11), and temperature (25-60 °C). The high coefficient value (R2 = 0.96) of the process optimization model suggests that this model was significant, where pH and adsorbent dose expressively stimulus adsorption efficiency including 40.11 mg/g at pH (3), MO concentration (100 mg/L), and MOSWC dose (1 g/L). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected values. The highest monolayer adsorption capacity of MO was 90.909 mg/g. Pseudo-second-order was the well-suited kinetics, where Langmuir isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows MO adsorption is favourable, exothermic, and spontaneous. Finally, this study indicates that MOSWC could be a potential candidate for the adsorption of MO from wastewater.

Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19.

The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-ß signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development.Communicated by Ramaswamy H. Sarma.

Black carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies.

This study focuses on the probable use of PET waste black carbon (PETWBC) and rice straw black carbon (RSBC) as an adsorbent for Acid Red 27 (AR 27) adsorption. The prepared adsorbent is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), AR 27 concentration (5-70 mg/L), adsorbent dose (0.5-20 g/L), pH (2-10), and temperature (25-60°C). High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected value. Pseudo-second-order was the well-suited kinetics, where Freundlich isotherm could explain better equilibrium adsorption data. Thermodynamic study shows AR 27 adsorption is favourable, endothermic, and spontaneous. Environmental friendliness properties are confirmed by desorption studies and satisfactory results also attain from real wastewater experiments. Finally, this study indicates that PETWBC and RSBC could be potential candidates for the adsorption of AR 27 from wastewater.

Carbon Coated Iron-Cobalt Nanoparticles for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is an emerging imaging modality that provides direct and quantitative mapping of iron oxide tracers. To achieve high sensitivity and good spatial resolution images, a magnetic nanoparticle with a higher contrast intensity needs to be developed. Currently, a majority of MPIs being developed for potential clinical application are composed of iron oxide nanoparticles with a spherical shape. In this project, we intend to report development of high-performance carbon (C) coated iron-cobalt (FeCo) nanoparticles (FeCo/C) and investigate their feasibility as a MPI agent. We have synthesized FeCo/C through a facile and simple method at mild temperature that is safe, easy, and up-scalable. We studied the structural and functional relationships and biocompatibility of this MPI agent in vitro. However, to enhance the aqueous solubility and biocompatibility, the surface of FeCo/C was modified with polyethylene glycol (PEG). We found that variation in the ratio of Fe and Co plays a vital role in their physical properties and functionality. In vitro imaging confirms that the Fe3Co1/C nanoparticle has highly competitive MPI intensity compared to VivoTrax, a commercially available MPI agent. Confocal laser scanning microscopy imaging with Rhodamine B labeled FeCo/C displays cellular internalization by the A375 cancer cells. The in vitro toxicity analysis concludes that there is no significant toxicity of FeCo/C nanoparticles. Therefore, the newly developed MPI agent holds strong promise for biomedical imaging and could be further validated in vivo in small animals.

Rapid, efficient, and green analytical technique for determination of fluorotelomer alcohol in water by stir bar sorptive extraction.

Fluorotelomer alcohols (FTOHs) are one of the major classes of per- and polyfluoroalkyl substances (PFAS). Due to their potential toxicity, persistence, and ubiquitous presence in the environment, some common PFAS are voluntarily phased out; while FTOHs are used as alternatives to conventional PFAS. FTOHs are precursors of perfluorocarboxylic acids (PFCAs) and therefore they are commonly detected in water matrices, which eventually indicate PFAS contamination in drinking water supplies and thus a potential source of human exposure. Even though studies have been conducted nationwide to evaluate the degree of FTOHs in the water environment, robust monitoring is lacking because of the unavailability of simple and sustainable analytical extraction and detection methods. To fill the gap, we developed and validated a simple, rapid, minimal solvent use, no clean-up, and sensitive method for the determination of FTOHs in water by stir bar sorptive extraction (SBSE) coupled with thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Three commonly detected FTOHs (6:2 FTOH, 8:2 FTOH, and 10:2 FTOH) were selected as the model compounds. Factors such as extraction time, stirring speed, solvent composition, salt addition, and pH were investigated to achieve optimal extraction efficiency. This "green chemistry" based extraction provided good sensitivity and precision with low method limits of detection ranging from 2.16 ng/L to 16.7 ng/L and with an extraction recovery ranging 55%-111%. The developed method were tested on tap water, brackish water, and wastewater influent and effluent. 6:2 FTOH and 8:2 FTOH were detected in two wastewater samples at 78.0 and 34.8 ng/L, respectively. This optimized SBSE-TD-GC-MS method will be a valuable alternative to investigate FTOHs in water matrices.

Assessment of quality of commercial hand sanitizers using Fourier transform infrared spectroscopy and gas chromatography.

Since the beginning of the COVID-19 pandemic, the use and manufacture of alcohol-based hand sanitizers increased exponentially. Efficacy of hand sanitizers mainly depends on active ingredients like ethanol and isopropanol (IPA). Even though methanol is extremely hazardous to people, it is still illegally used in hand sanitizers in Bangladesh. Developing a quick and simple analytical method for detecting and quantifying ethanol/IPA/methanol is crucial. Here, Fourier transform infrared spectroscopy (FTIR) was used to identify and quantify alcohol content in commercially available hand sanitizers in a quick and easy way. Comparing the FTIR and GC data, provided quite similar results. Unlike previous studies by FTIR, C-H, CH3-C-CH3 stretching, and C-H bending vibrational modes were employed to construct analytical calibration curves to detect and quantify alcohol in hand sanitizers. According to FTIR and GC findings, ethanol and IPA content were found to be 43-82% and 40-69%, and 56-64% and 61-66%, respectively, whereas ethanol was labeled at 66-80% and IPA at 65-70%. FTIR and GC revealed methanol content ranging from 37 to 98 and 19 to 81%, respectively. Also, the FTIR was significantly faster than the GC. Therefore, FTIR can be used to commercially analyze the quality of hand sanitizers.•FTIR was used to identify and quantify alcohol content in commercially available hand sanitizers in a quick and easy way.•Comparing the FTIR and GC data, provided quite similar results.•Out of ten samples, five contained ethanol, three IPA, and two methanol.

Flexible, stretchable, and single-molecule-sensitive SERS-active sensor for wearable biosensing applications.

The development of wearable sensors for remote patient monitoring and personalized medicine has led to a revolution in biomedical technology. Plasmonic metasurfaces that enhance Raman scattering signals have recently gained attention as wearable sensors. However, finding a flexible, sensitive, and easy-to-fabricate metasurface has been a challenge for decades. In this paper, a novel wearable device, the flexible, stretchable, and single-molecule-sensetive SERS-active sensor, is proposed. This device offers an unprecedented SERS enhancement factor in the order of 1011, along with other long-desired characteristics for SERS applications such as a high scattering to absorption ratio (∼2.5) and a large hotspot volume (40 nm × 40 nm × 5 nm). To achieve flexibility, we use polydimethylsiloxane (PDMS) as the substrate, which is stable, transparent, and biologically compatible. Our numerical calculations show that the proposed sensor offers reliable SERS performance even under bending (up to 100° angles) or stretching (up to 50% stretch). The easy-to-fabricate and flexible nature of our sensor offers a promising avenue for developing highly sensitive wearable sensors for a range of applications, particularly in the field of personalized medicine and remote patient monitoring.

Modeling the path to >30% power conversion efficiency in perovskite solar cells with plasmonic nanoparticles.

Mixed organic-inorganic halide perovskite solar cells (PSCs) are a promising technology with increasing power conversion efficiency (PCE), low-cost material constituents, simple scalability, and a low-temperature solution fabrication process. Recent developments have seen energy conversion efficiencies increase from 3.8% to over 20%. However, to further improve PCE and reach the target efficiency of over 30%, light absorption through plasmonic nanostructures is a promising approach. In this work, we present a thorough quantitative analysis of the absorption spectrum of a methylammonium lead iodide (CH3NH3PbI3) perovskite solar cell using a nanoparticle (NP) array. Our multiphysics simulations using finite element methods (FEM) show that an array of Au nanospheres can increase average absorption >45%, compared to only 27.08% for the baseline structure without any NPs. Furthermore, we investigate the combined effect of engineered enhanced absorption on electrical and optical solar cell performance parameters using the one-dimensional solar cell capacitance software (SCAPS 1-D), which shows a PCE of ∼30.4%, significantly higher than the PCE of ∼21% for cells without NPs. Our findings demonstrate the potential of plasmonic perovskite research for next-generation optoelectronic technologies.

ß-Glucan and Fatty Acid Based Mucoadhesive Carrier for Gastrointestinal Tract Specific Local and Sustained Drug Delivery.

The oral route is considered the most convenient route of drug administration for both systemic and local delivery. Besides stability and transportation, another unmet but important issue regarding oral medication is retention duration within the specific region of the gastrointestinal (GI) tract. We hypothesize that an oral vehicle that can adhere and maintain retention within the stomach for a longer duration can be more effective to treat stomach-related diseases. Therefore, in this project, we developed a carrier that is highly specific to the stomach and maintains its retention for a longer duration. We developed a vehicle composed of ß-Glucan And Docosahexaenoic Acid (GADA) to observe its affinity and specificity to the stomach. GADA forms a spherical-shaped particle with negative zeta potential values that vary based on the feed ratio of docosahexaenoic acid. Docosahexaenoic acid is an omega-3 fatty acid that has transporters and receptors throughout the GI tract, such as CD36, plasma membrane-associated fatty acid-binding protein (FABP (pm)), and a family of fatty acid transport proteins (FATP1-6). The in vitro studies and characterization data showed that GADA has the capability to carry a payload of hydrophobic molecules and specifically deliver the payload to the GI tract, exert its therapeutic effects, and help to maintain stability for more than 12 h in the gastric and intestinal fluid. The particle size and surface plasmon resonance (SPR) data showed that GADA has a strong binding affinity with mucin in the presence of simulated gastric fluids. We observed a comparatively higher drug release of lidocaine in gastric juice than that in intestinal fluids, demonstrating the influence of the pH values of the media on drug-release kinetics. In vivo and ex vivo imaging of mice demonstrated that GADA maintains its retention within the stomach for at least 4 hr. This stomach-specific oral vehicle holds strong promise to translate various injectable therapeutic drugs to oral form upon further optimizations.

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in the model's prediction stream using a basic Salt and Pepper filter, as well as, tasks that use domain-specific thresholds, including a minimum QRS size, and a minimum or maximum R-R distance. These thresholds were found to vary among QRS-detection studies and empirically determined for the target dataset, which may have implications if the target dataset differs such as the drop of performance in unknown test datasets. Moreover, these studies, in general, fail to identify the relative strengths of deep-learning models and the post-processing to weigh them appropriately. This study identifies the domain-specific post-processing, as found in the QRS-detection literature, as three steps based on the required domain knowledge. It was found that the use of minimal domain-specific post-processing is often sufficient for most of the cases and the use of additional domain-specific refinement ensures superior performance, however, it makes the process biased towards the training data and lacks generalisability. As a remedy, a domain-agnostic automated post-processing is introduced where a separate recurrent neural network (RNN)-based model learns required post-processing from the output generated from a QRS-segmenting deep learning model, which is, to the best of our knowledge, the first of its kind. The RNN-based post-processing shows superiority over the domain-specific post-processing for most of the cases (with shallow variants of the QRS-segmenting model and datasets like TWADB) and lags behind for others but with a small margin ( ≤ 2%). The consistency of the RNN-based post-processor is an important characteristic which can be utilised in designing a stable and domain agnostic QRS detector.

Receptor model-based sources and risks appraisal of potentially toxic elements in the urban soils of Bangladesh.

Rapid urbanization and industrial development have prompted potentially toxic elements (PTEs) in urban soil in Bangladesh, which is a great concern for ecological and public health matters. The present study explored the receptor-based sources, probable human health and ecological risks of PTEs (As, Cd, Pb, Cr, Ni, and Cu) in the urban soil of the Jashore district, Bangladesh. The USEPA modified method 3050B and atomic absorption spectrophotometers were used to digest and evaluate the PTEs concentration in 71 soil samples collected from eleven different land use areas, respectively. The concentration ranges of As, Cd, Pb, Cr, Ni, and Cu in the studied soils were 1.8-18.09, 0.1-3.58, 0.4-113.26, 0.9-72.09, 2.1-68.23, and 3.82-212.57 mg/kg, respectively. The contamination factor (CF), pollution load index (PLI), and enrichment factor (EF) were applied to evaluate the ecological risk posed by PTEs in soils. Soil quality evaluation indices showed that Cd was a great contributor to soil pollution. The PLI values range was 0.48-2.82, indicating base levels to continuous soil degradation. The positive matrix factorization (PMF) model showed that As (50.3 %), Cd (38.8 %), Cu (64.7 %), Pb (81.8 %) and Ni (47.2 %) were derived from industrial sources and mixed anthropogenic sources, while Cr (78.1 %) from natural sources. The highest contamination was found in the metal workshop, followed by the industrial area, and brick filed site. Soil from all land use types revealed moderate to high ecological risk after evaluating probable ecological risks, and the descending order of single metal potential ecological risk was Cd > As > Pb > Cu > Ni > Cr. Ingestion was the primary route of exposure to potentially toxic elements for both adults and children from the study area soil. The overall non-cancer risk to human health is caused by PTEs for children (HI=0.65 ± 0.1) and adults (HI=0.09 ± 0.03) under USEPA safe limit (HI>1), while the cancer risks from exclusively ingesting As through soil were 2.10E-03 and 2.74E-04 for children and adults, respectively, exceeding the USEPA acceptable standard (>1E-04).