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 if 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).

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer.

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies.Communicated by Ramaswamy H. Sarma.

Transcriptomic analysis through bioinformatics revealed 218 significant differentially expressed genes (DEGs) that unfolded new molecular pathways responsible for cervical cancer (CC); The PPI network sorted major hub-genes that can be accounted as potential biomarkers with prominent roles in CC progression and helpful for its diagnosis, prognosis and therapies;TFs-DEGs and miRNAs-DEGs regulatory network assessment detected transcriptional and post-transcriptional elements;The gene-set enrichment provided gene ontological terms and pathway enrichment analysis shared biological relevance of CC development;Integrated statistics and cheminformatics approaches predicted some highly potential candidate drugs against CC;All the outcomes of the study were cross-validated through survival analyses, molecular docking and literature review.

Assessment of the relationship between serum xanthine oxidase levels and type 2 diabetes: a cross-sectional study.

Xanthine oxidase (XO) is an enzyme associated with purine metabolism. The relationship between XO levels and type 2 diabetes (T2D) is not clear yet or little is known so far. Therefore, we conducted a cross-sectional study to determine the association of XO levels with T2D in a Bangladeshi adult cohort. A total of 325 participants (234 males and 91 females) were enrolled in the study. The participants were divided into three groups; diabetic (n = 173), prediabetic (n = 35), and non-diabetic control (n = 117). Serum levels of XO were measured by enzyme-linked immunosorbent assay (ELISA) and other biochemical parameters including fasting blood glucose (FBG), serum uric acid (SUA), and lipid profile markers measured by colorimetric methods. Participants with T2D were confirmed according to the definition of the American Diabetic Association. The association between serum XO levels and T2D was determined by logistic regression models. The mean level of serum XO was significantly higher in females (6.0 ± 3.7 U/L) compared to male (4.0 ± 2.8 U/L) participants (p < 0.001). In contrast, males had a higher mean level of SUA (6.1 ± 1.9 mg/dL) than female (4.4 ± 1.9 mg/dL) participants (p < 0.001). The mean level of XO was significantly higher in the diabetic group (5.8 ± 3.6 U/L) compared to the prediabetic (3.7 ± 1.9 U/L) and control (2.9 ± 1.8 U/L) groups (p < 0.001). On the other hand, the mean SUA concentration was significantly lower in the diabetic group than in the other two groups (p < 0.001). A significant increasing trend was observed for FBG levels across the XO quartiles (p < 0.001). A decreasing trend was found for SUA levels in the XO quartiles (p < 0.001). Serum levels of XO and SUA showed a positive and negative correlation with FBG, respectively. In regression analysis, serum XO levels showed an independent association with T2D. In conclusion, this study reports a positive and independent association between XO levels and T2D in Bangladeshi adults. Monitoring serum levels of XO may be useful in reducing the risk of T2D. Further research is needed to determine the underlying mechanisms of the association between elevated XO levels and T2D.

Human health risk ​and receptor model-oriented sources of heavy metal pollution in commonly consume vegetable and fish species of high Ganges river floodplain agro-ecological area, Bangladesh.

This study was intended to assess heavy metal contents and sources in commonly consumed vegetables and fish collected from the Jashore district of Bangladesh and to evaluate the probable human health risks via the ingesting of those vegetables and fish species. A total of 130 vegetable and fish samples were analyzed for As, Mn, Cu, Cr, Ni, and Pb concentration by an atomic absorption spectrophotometer. Metals and metalloids like As, Pb, and Cr in vegetable species were greater than the maximum allowable concentration (MAC), while Pb and cu in fish species exceeded the MAC. Pollution evaluation index values were ranges from 0.40-10.35 and 1.53-2.78 for vegetable and fish species, respectively, indicating light to serious pollution. Lactuca sativa followed by Cucurbita moschata, Amaranthus gangeticus for vegetables and Channa punctate, Oreochromis mossambicus, followed by Dendrobranchiata for fish are the most contaminated food items. The positive matrix factorization model showed that As (81.9%), Ni (48%), Cr (49.6%), Mn (46%), Pb (44.3%), and Cu (44.4%) for vegetable species and As (86.9%), Ni (90.5%), Mn (67.6%), Pb (65.3%), Cr (57%) and Cu (46.2%) for fish species were resulting from agrochemical, atmospheric emission, irrigation, contaminated feed, and mixed sources. The self-organizing map and principle component analysis indicates three spatial patterns e.g., As-Mn-Cu, Pb-Cr, and Ni in vegetables and As-Mn-Cr, Cu-Ni, and Pb in fish samples. The THQ values for single elements were less than 1 (except As for vegetables and Pb for fish species) for all food items but the HI values for all of the vegetables (2.18E+00 to 2.04E+01) and fish (1.07E+00 to 9.39E+00) samples were exceeded the USEPA acceptable risk level (HI > 1E+00). While the cancer risks only induced by As for all vegetables and fish species, which exceeded the USEPA safe level (TCR>1E-04). Sensitivity analysis indicates that metal concentration was the most responsible factor for carcinogenic risk.

Performance of a Convolutional Neural Network Derived from PPG Signal in Classifying Sleep Stages.

Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study, we propose an automatic technique for multi-stage sleep classification using photoplethysmographic (PPG) signal. We have proposed a convolutional neural network (CNN) that learns directly from the PPG signal and classifies multiple sleep stages. We developed models for two- (Wake-Sleep), three- (Wake-NREMREM) and four- (Wake-Light sleep-Deep sleep-REM) stages of sleep classification. Our proposed approach shows an average classification accuracy of 96.30%, 95.96%, and 93.60% for two, three, and four stages, respectively. Experimental results show that the proposed CNN model outperforms existing state-of-the-art models (classical and deep learning) in the literature.

Ambient Ionization Mass Spectrometry: Application and Prospective.

Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future.

Arsenic, iron, and manganese in groundwater and its associated human health risk assessment in the rural area of Jashore, Bangladesh.

This study investigated groundwater pollution and potential human health risks from arsenic, iron, and manganese in the rural area of Jashore, Bangladesh. Study results show that the mean value of groundwater pH is 7.25 ± 0.31, with a mean conductivity of 633.94 ± 327.41 µs/cm, while about 73, 97, and 91% of groundwater samples exceeded the Bangladesh drinking water standard limits for As, Fe, and Mn, respectively. Groundwater pollution evaluation indices, including the heavy metal pollution index, the heavy metal evaluation index, the degree of contamination, and the Nemerow pollution index, show that approximately 97, 82, 100, and 100% of samples are in the high degree of pollution category, respectively. Spatial distribution exhibited that the study area is highly exposed to As (73%), Fe (82%), and Mn (46%). In the case of non-carcinogenic health risk via oral exposure, about 94% of samples suggest a high category of risk for infants, and 97% of samples are found to be at high risk for children and adults. The carcinogenic risk of arsenic via an oral exposure pathway suggests that approximately 97% of the samples are found to be at high risk for infants, and all of the samples are at high risk for both adults and children.

Source identification, contamination status and health risk assessment of heavy metals from road dusts in Dhaka, Bangladesh.

In this study, concentrations of Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined in road dusts collected from different locations in Dhaka to assess source, contamination status and health risk. Energy-dispersive X-ray fluorescence spectroscopy and energy-dispersive X-ray spectroscopy were used to determine Cr, Mn, Ni, Cu, Zn, Cd and Pb and their mean concentrations were 162.27 ± 29.46, 721.18 ± 180.14, 35.65 ± 12.55, 104.56 ± 128.33, 515.32 ± 321.90, BDL, and 342.82 ± 591.20 mg/kg, respectively. Among the heavy metals, highest concentrations of Cu, Zn and Pb were found at urban sites-7 (municipal waste dumping) and 8 (medical waste incineration). Highest concentration of Cr followed by Cu and Zn was found at site-5 (Tejgaon, urban). Principal component analysis revealed that anthropogenic activities are the potential sources for Cr, Ni, Cu, Zn and Pb while earth crust for Mn. Pollution index and pollution load index results suggested that all the sites were contaminated and/or degraded by Cr, Cu, Zn and Pb except sites-9 (urban), 10 (sub-urban), 11 (rural) while sites-7 and 8 (urban) were extremely degraded. For noncarcinogenic health risk, hazard quotient values for dermal were higher compared to that of inhalation/ingestion. Though hazard index values were less than 1 at all the sites, these were at least one order of magnitude higher for children group than that of adult group, thus the children group may face more noncarcinogenic health risk at sites-7 and 8. Values of incremental lifetime cancer risk were from 10-9 to 10-11 showed no carcinogenic health risk by road dusts contaminated with the heavy metals.

Interpretability and Optimisation of Convolutional Neural Networks Based on Sinc-Convolution.

Interpretability often seeks domain-specific facts, which is understandable to human, from deep-learning (DL) or other machine-learning (ML) models of black-box nature. This is particularly important to establish transparency in ML model's inner-working and decision-making, so that a certain level of trust is achieved when a model is deployed in a sensitive and mission-critical context, such as health-care. Model-level transparency can be achieved when its components are transparent and are capable of explaining reason of a decision, for a given input, which can be linked to domain-knowledge. This study used convolutional neural network (CNN), with sinc-convolution as its constrained first-layer, to explore if such a model's decision-making can be explained, for a given task, by observing the sinc-convolution's sinc-kernels. These kernels work like band-pass filters, having only two parameters per kernel - lower and upper cutoff frequencies, and optimised through back-propagation. The optimised frequency-bands of sinc-kernels may provide domain-specific insights for a given task. For a given input instance, the effects of sinc-kernels was visualised by means of explanation vector, which may help to identify comparatively significant frequency-bands, that may provide domain-specific interpretation, for the given task. In addition, a CNN model was further optimised by considering the identified subset of prominent sinc frequency-bands as the constrained first-layer, which yielded comparable or better performance, as compared to its all sinc-bands counterpart, as well as, a classical CNN. A minimal CNN structure, achieved through such an optimisation process, may help design task-specific interpretable models. To the best of our knowledge, the idea of sinc-convolution layer's task-specific significant sinc-kernel-based network optimisation is the first of its kind. Additionally, the idea of explanation-vector-based joint time-frequency representation to analyse time-series signals is rare in the literature. The above concept was validated for two tasks, ECG beat-classification (five-class classification task), and R-peak localisation (sample-wise segmentation task).

Hollow Cathode Discharge Ionization Mass Spectrometry: Detection, Quantification and Gas Phase Ion-Molecule Reactions of Explosives and Related Compounds.

Mass spectrometry (MS) has become an essential analytical method in every sector of science and technology. Because of its unique ability to provide direct molecular structure information on analytes, an extra method is rarely required. This review describes fabrication of a variable-pressure hollow cathode discharge (HCD) ion source for MS in detection, quantification and investigation of gas-phase ion molecule reactions of explosives and related compounds using air as a carrier gas. The HCD ion source has been designed in such a way that by altering the ion source pressures, the system can generate both HCD and conventional GD. This design enables for the selective detection and quantification of explosives at trace to ultra-trace levels. The pressure-dependent HCD ion source has also been used to investigate ion-molecule reactions in the gas phase of explosives and related compounds. The mechanism of ion formation in explosive reactions is also discussed.

The association between elevated lipid profile and liver enzymes: a study on Bangladeshi adults.

Dyslipidemia, a major contributor to cardiovascular diseases, is rapidly increasing in Asian countries including Bangladesh. In addition to the cardiovascular system, abnormal lipid levels are also known to cause complications in renal and hepatic systems. The data regarding dyslipidemia and its relationship with liver enzymes are scarce for the Bangladeshi population. Therefore, this study was conducted to estimate the prevalence of dyslipidemia and determine the relationship between lipid profile and liver enzymes in Bangladeshi adults. A total of 405 participants (318 males and 87 females) were enrolled in the study. Serum levels of TG, TC, LDL, HDL and liver enzymes including ALT, AST, GGT and ALP were analyzed using standard methods. Dyslipidemia and liver function tests abnormalities were defined according to the international standard guidelines. The association between elevated lipid profile markers and liver enzyme abnormalities was assessed by logistic regression analysis. Overall, the prevalence of elevated TG, TC, LDL and low HDL were 30.9%, 23.7%, 26.2% and 78.8%, respectively. On the other hand, the prevalence of elevated liver enzymes ALT, AST, GGT and ALP were 18.8%, 21.6%, 12.9% and 21.9%, respectively. Dyslipidemia and liver enzyme abnormalities were higher in diabetic and hypertensive participants than in the healthy participants. About 61% of participants with dyslipidemia had at least one or more elevated liver enzymes. In regression analysis, an independent association was observed between serum GGT and all lipid components. In conclusion, a high prevalence of dyslipidemia and liver enzyme abnormalities were observed among the study participants. Of the four liver enzymes, the serum levels of GGT showed an independent association with all lipid components. Moreover, this study indicates that subjects with dyslipidemia often have a higher chance of having liver diseases than subjects with no dyslipidemia. However, large-scale prospective studies are needed to understand the underlying mechanisms of lipid-induced hepatic dysfunction in the Bangladeshi population.

Gas phase ion-molecule reactions of nitroaromatic explosive compounds studied by hollow cathode discharge ionization-mass spectrometry.

In this study, we have developed a variable pressure operating hollow cathode discharge (HCD) ion source to investigate the gas phase ion-molecule reactions of nitroaromatic explosive compounds. The developed HCD ion source coupled MS system has also been validated as an analytical method to analyze explosives at trace levels. The ion source was designed in such a way that the plasma can be generated alternatively at high pressure (~30 Torr), medium pressure (~5 Torr) and low pressure (~1 Torr) regions. The plasma contains a sufficient amount of reactant ions, electrons and excited species, thus the gaseous analyte molecules were efficiently ionized when they passed through the plasma. In the ion-molecule reactions of the nitroaromatic explosives, the discharge products of NOx- (x = 2,3), O3 and HNO3 originating from the plasma-excited air were suggested to contribute to the formation of mostly [M - H]-, [M - NO]-, [M+NO3-HNO2]- and [M-NO+HNO3]- adduct ions at the higher ion source pressures (~5 and 28 Torr) while the electron rich plasma leads to the formation of molecular ion, M-•, at the lower ion source pressure (~1 Torr). Formation of the hydride-adduct ions of the nitroaromatic compounds reveals the surface-assisted Birch type reduction in the HCD plasma. The variety of spectral patterns in the air-assisted glow discharge would be useful for high through-put detection of TNT and TNT-related explosives. An ambient helium dielectric barrier discharge (DBD) ion source was also used and gave identical mass spectra of the nitroaromatic explosive compounds to those observed by the HCD ion source, but did not give any hydride-adduct ions of the explosive compounds. Ion formation mechanism of these ions is also discussed.

Poly- and Perfluorinated Alkyl Substances in Air and Water from Dhaka, Bangladesh.

Bangladesh hosts extensive textile manufacturing, for some of which per- and polyfluorinated alkyl substances (PFAS) have been used to impart water and dirt repellency, among other things. Textile waste emissions to the atmosphere and discharge into rivers and other bodies of water could present a significant concern for human and ecosystem health, but there is little information on PFAS in Bangladesh. To assess the presence of ionic PFAS and their precursors in air and water from Dhaka, Bangladesh, polyethylene sheets were deployed for 28 days as passive samplers for neutral PFAS in outdoor air and water, while ionic PFAS were measured from discrete water grabs. Fluorotelomer alcohols (FTOHs) were detected at almost all sites in air and water; the most frequently detected compound was 6:2 FTOH, ranging from below instrumental detection limits (

Kinetics and mechanism of formation of nickel(II)porphyrin and its interaction with DNA in aqueous medium.

Kinetics between 5,10,15,20-tetrakis(N-methylpyridium-4-yl)porphyrin and Ni2+ species were investigated in aqueous solution at 25 ±1 °C in I = 0.10 M (NaNO3). Speciation of Ni2+ was done in I = 0.10 M (NaNO3) for knowing distribution of Ni2+ species with solution pH. Experimental data were compared with speciation diagram constructed from the values of hydrolysis constants of Ni2+ ion. Speciation data showed that hexaaquanickel(II) ions took place in hydrolysis reactions through formation of [Ni(OH2)6-n(OH)n]2-n species with solution pH. According to speciation of Ni2+ and pH dependent rate constants, rate expression can be written as: d[Ni(TMPyP)4+]/dt = (k 1[Ni2+ (aq)] + k 2[Ni(OH)+ (aq)] + k 3[Ni(OH)2 o (aq)] + k 4[Ni(OH)3 - (aq)])[H2TMPyP4+], where k 1, k 2, k 3 and k 4 were found to be k 1 = (0.62 ± 0.22) × 10-2; k 2 = (3.60 ± 0.40) × 10-2; k 3 = (2.09 ± 0.52) × 10-2, k 4 = (0.53 ± 0.04) × 10-2 M-1s-1 at 25 ±1 °C, respectively. Formation of hydrogen bonding between [Ni(H2O)5(OH)]+ and [H2TMPyP]4+ causes enhanced reactivity. Rate of formation of [Ni(II)TMPyP]4+ complex was to be 3.99 × 10-2 M-1s-1 in I = 0.10 M, NaNO3 (25 ± 1 °C). UV-Vis and fluorescence data suggested that [Ni(II)TMPyP]4+ and [H2(TMPyP)]4+ interact with DNA via outside binding with self-stacking and intercalation, respectively. SYNOPSIS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12039-021-01945-y.

Simultaneous detection and quantification of explosives by a modified hollow cathode discharge ion source.

Detection of explosives at trace levels is crucial for security purposes because of increasing worldwide terrorist threats at public places. Previously, a hollow cathode discharge (HCD) ion source has been fabricated for detection of explosives. Recently, the HCD ion source has been modified for a dual pressures operating system and coupled to a linear ion trap MS to analyze explosives simultaneously. Here, trinitrotoluene (TNT), nitroglycerin (NG), pentaerythritol tetranitrate (PETN) and 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) were taken as model explosive compounds and the mass spectra were recorded in the negative mode ionization. At the higher ion source pressure (~28.0-30.0 Torr), NG, PETN and RDX gave adduct ions with the NO3- ion while TNT showed the [TNT + NO3-HNO2]- (m/z 242) simultaneously. However, NG and PETN did not give any ion signals at the lower ion source pressure (~0.8-1.0 Torr) while TNT exhibited its molecular ion, [TNT]-• (m/z 227), as a major ion through electron attachment and RDX showed fragment ions that followed electron capture dissociation concurrently. The modified HCD ion source exhibited better sensitivity in simultaneous detection and quantification of the explosives. The NO3- and NO2- as reagent ions in the air HCD plasma form stable adduct ions with the NG, PETN and RDX even with TNT at the higher temperature (140-200 °C). The formation of the NO3-, NO2- in the HCD plasma also causes the formation of [TNT-H]- (m/z 226) at the higher ion source pressure. The inner metallic surface of the hollow tube assists the Birch reduction type reaction that results in the formation of hydride ion of the TNT, [TNT + H]- (m/z 228). No significant difference in the spectral pattern for simultaneous and individual measurements for the explosives was observed at the higher ion source pressure. Therefore, it may conclude that the present modified HCD ion source can be used for simultaneous detection and quantification of the explosive compounds at trace and/or ultra-trace levels using air as a carrier gas.

Is haem the real target of COVID-19?

Although a vaccination campaign has been launched in many countries, the COVID-19 pandemic is not under control. The main concern is the emergence of new variants of SARS-CoV-2; therefore, it is important to find approaches to prevent or reduce the virulence and pathogenicity of the virus. Currently, the mechanism of action of SARS-CoV-2 is not fully understood. Considering the clinical effects that occur during the disease, attacking the human respiratory and hematopoietic systems, and the changes in biochemical parameters (including decreases in haemoglobin [Hb] levels and increases in serum ferritin), it is clear that iron metabolism is involved. SARS-CoV-2 induces haemolysis and interacts with Hb molecules via ACE2, CD147, CD26, and other receptors located on erythrocytes and/or blood cell precursors that produce dysfunctional Hb. A molecular docking study has reported a potential link between the virus and the beta chain of haemoglobin and attack on haem. Considering that haem is involved in miRNA processing by binding to the DGCR8-DROSHA complex, we hypothesised that the virus may check this mechanism and thwart the antiviral response.