PlantMedOman: an online database for Oman's medicinal plants.

The Sultanate of Oman has a rich biodiversity, particularly in medicinal plants, and plays a crucial role in traditional healthcare practices. However, the wealth of knowledge about these plants is scattered across various literature, making it challenging for researchers, practitioners, and the public to access comprehensive information. Therefore, the availability of a centralized, user-friendly online database to catalog Oman's medicinal plants is of great importance. PlantMedOman presented here, which currently holds 186 records helps to enhance academic research, support drug discovery studies, promote the conservation of medicinal plants, and foster greater awareness of Oman's ethnomedicinal heritage.

CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images.

INTRODUCTION: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images. METHODS: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images. RESULTS: The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates. CONCLUSION: The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.

The Leptospermum scoparium (Manuka)-Specific Nectar and Honey Compound 3,6,7-Trimethyllumazine (LepteridineTM) That Inhibits Matrix Metalloproteinase 9 (MMP-9) Activity.

3,6,7-trimethyllumazine (Lepteridine™) is a newly discovered natural pteridine derivative unique to Manuka (Leptospermum scoparium) nectar and honey, with no previously reported biological activity. Pteridine derivative-based medicines, such as methotrexate, are used to treat auto-immune and inflammatory diseases, and Manuka honey reportedly possesses anti-inflammatory properties and is used topically as a wound dressing. MMP-9 is a potential candidate protein target as it is upregulated in recalcitrant wounds and intestinal inflammation. Using gelatin zymography, 40 µg/mL LepteridineTM inhibited the gelatinase activities of both pro- (22%, p < 0.0001) and activated (59%, p < 0.01) MMP-9 forms. By comparison, LepteridineTM exerted modest (~10%) inhibition against a chromogenic peptide substrate and no effect against a fluorogenic peptide substrate. These findings suggest that LepteridineTM may not interact within the catalytic domain of MMP-9 and exerts a negligible effect on the active site hydrolysis of small soluble peptide substrates. Instead, the findings implicate fibronectin II domain interactions by LepteridineTM which impair gelatinase activity, possibly through perturbed tethering of MMP-9 to the gelatin matrix. Molecular modelling analyses were equivocal over interactions at the S1' pocket versus the fibronectin II domain, while molecular dynamic calculations indicated rapid exchange kinetics. No significant degradation of synthetic or natural LepteridineTM in Manuka honey occurred during simulated gastrointestinal digestion. MMP-9 regulates skin and gastrointestinal inflammatory responses and extracellular matrix remodelling. These results potentially implicate LepteridineTM bioactivity in Manuka honey's reported beneficial effects on wound healing via topical application and anti-inflammatory actions in gastrointestinal disorder models via oral consumption.

Enablers & challenges of tribal women & health system for implementation of screening of non-communicable diseases & common cancers: A mixed-methods study in Palghar district of Maharashtra, India.

Background & objectives: Non-communicable diseases (NCDs) and cancers of breast, oral cavity and cervix contribute to around 5.87 million (60%) deaths in India. Despite this, there is limited evidence on preparedness of the tribal health system in mitigating these conditions. This mixed-methods study aimed at identifying enablers and challenges using a multistakeholder approach for the screening of NCDs and common cancers in a tribal block of Maharashtra, India. Methods: This study was conducted in a tribal community of Dahanu taluka in Palghar district of Maharashtra. A total of nine focus group discussions (FGDs) among tribal women and accredited social health activists (ASHAs), 13 key informant interviews (KIIs) among auxiliary nurse midwives (ANMs) and community health officers (CHO) and facility surveys of five public health facilities were conducted. The FGDs and KIIs were conducted using guides, recorded digitally, transcribed, analyzed and triangulated to identify emerging themes. Results: The tribal women had limited knowledge about NCDs and common cancers. Paucity of health facilities, out-of-pocket expenditure, misconceptions, belief on traditional healers and inability to prioritize health were identified as major challenges. The ASHAs were recognized as a key connecting link between health system and community while provision of culturally appropriate IEC materials and adequate training were recognized as critical enablers by healthcare providers in implementing screening for NCDs and common cancers. Interpretation & conclusions: The study recommends incorporating socioculturally relevant strategies in the tribal population and strengthening health facilities in terms of infrastructure and training with involvement of ASHAs for successful implementation of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) through health and wellness centres.

Asymmetrically Encapsulated Vertical ITO/MoS2 /Cu2 O Photodetector with Ultrahigh Sensitivity.

Strong light absorption, coupled with moderate carrier transport properties, makes 2D layered transition metal dichalcogenide semiconductors promising candidates for low intensity photodetection applications. However, the performance of these devices is severely bottlenecked by slow response with persistent photocurrent due to long lived charge trapping, and nonreliable characteristics due to undesirable ambience and substrate effects. Here ultrahigh specific detectivity (D*) of 3.2 × 1014 Jones and responsivity (R) of 5.77 × 104 A W-1 are demonstrated at an optical power density (Pop ) of 0.26 W m-2 and external bias (Vext ) of -0.5 V in an indium tin oxide/MoS2 /copper oxide/Au vertical multi-heterojunction photodetector exhibiting small carrier transit time. The active MoS2 layer being encapsulated by carrier collection layers allows us to achieve repeatable characteristics over large number of cycles with negligible trap assisted persistent photocurrent. A large D* > 1014 Jones at zero external bias is also achieved due to the built-in field of the asymmetric photodetector. Benchmarking the performance against existing reports in literature shows a viable pathway for achieving reliable and highly sensitive photodetectors for ultralow intensity photodetection applications.

Amyloid Fibrils from Hemoglobin.

Amyloid fibrils are a class of insoluble protein nanofibers that are formed via the self-assembly of a wide range of peptides and proteins. They are increasingly exploited for a broad range of applications in bionanotechnology, such as biosensing and drug delivery, as nanowires, hydrogels, and thin films. Amyloid fibrils have been prepared from many proteins, but there has been no definitive characterization of amyloid fibrils from hemoglobin to date. Here, nanofiber formation was carried out under denaturing conditions using solutions of apo-hemoglobin extracted from bovine waste blood. A characteristic amyloid fibril morphology was confirmed by transmission electron microscopy (TEM) and atomic force microscopy (AFM), with mean fibril dimensions of approximately 5 nm diameter and up to several microns in length. The thioflavin T assay confirmed the presence of ß-sheet structures in apo-hemoglobin fibrils, and X-ray fiber diffraction showed the characteristic amyloid cross-ß quaternary structure. Apo-hemoglobin nanofibers demonstrated high stability over a range of temperatures (-20 to 80 °C) and pHs (2-10), and were stable in the presence of organic solvents and trypsin, confirming their potential as nanomaterials with versatile applications. This study conclusively demonstrates the formation of amyloid fibrils from hemoglobin for the first time, and also introduces a cost-effective method for amyloid fibril manufacture using meat industry by-products.

A True Human Tail in a Neonate: Case report and literature review.

A true human tail is a benign vestigial caudal cutaneous structure composed of adipose, connective tissue, muscle, vessels, nerves and mechanoreceptors. A true human tail can be distinguished from a pseudotail as the latter is commonly associated with underlying spinal dysraphism, which requires specialised management. True human tails are very rare, with fewer than 40 cases reported to date. We report a healthy one-day-old male newborn who was referred to the Bharath Hospital, Kottayam, Kerala, India, in 2014 with a cutaneous appendage arising from the lumbosacral region. Magnetic resonance imaging of the spine ruled out spinal dysraphism. The appendage was removed by simple surgical excision. Clinicians should emphasise use of 'true tail' and 'pseudotail' as specific disparate terms as the clinical, radiological and histological findings of these conditions differ significantly, along with management strategies and outcomes.

Potentiation of etoposide and temozolomide cytotoxicity by curcumin and turmeric force™ in brain tumor cell lines.

We have investigated on the potentiation of etoposide (ETP) and temozolomide (TMZ) cytotoxicity in U-87MG glioblastoma and D283 medulloblastoma cell lines by curcumin (CUR) and turmeric force (TF), a nutraceutical formulation of turmeric, with the objective of assessing the potential for their adjuvant use in brain tumor chemotherapy. While U-87MG cell line was generally resistant to TMZ, IC50 values for CUR and TF were 37.33 and 30.75 µg/ml, respectively. TF is the only agent that demonstrated efficacy at the IC90 level. When CUR or TF was combined with ETP and TMZ, increased chemotherapeutic efficiency in the U-87MG cells was observed. TF is highly cytotoxic to D283 Med cell line compared to curcumin with an IC50 value of 1.55 ug/ml. Although both CUR and TF potentiated ETP and TMZ cytotoxicity, TF is more efficient than CUR in both U-87MG and D283 Med cell lines. Treatment of U-87MG cells with the triple combination of TMZ+ETP+TF induced a high percentage of apoptotic cells. Potential mechanisms that may explain evidence of synergy include down regulation of p10 and p53 mRNAs and increase in BAX/Bcl-2 mRNA ratio. These pre-clinical results suggest that TF may be useful as an adjuvant with ETP and TMZ for brain tumor chemotherapy.

Evaluation of transgenic tobacco plants expressing a bacterial Co-Ni transporter for acquisition of cobalt.

Phytoremediation is a viable strategy for management of toxic wastes in a large area/volume with low concentrations of toxic elemental pollutants. With increased industrial use of cobalt and its alloys, it has become a major metal contaminant in soils and water bodies surrounding these industries and mining sites with adverse effects on the biota. A bacterial Co-Ni permease was cloned from Rhodopseudomonas palustris and introduced into Nicotiana tabacum to explore its potential for phytoremediation and was found to be specific for cobalt and nickel. The transgenic plants accumulated more cobalt and nickel as compared to control, whereas no significant difference in accumulation of other divalent ions was observed. The transgenic plants were evaluated for cobalt content and showed increased acquisition of cobalt (up to 5 times) as compared to control. The plants were also assessed for accumulation of nickel and found to accumulate up to 2 times more nickel than control. At the same initial concentration of cobalt and nickel, transgenic plant preferentially accumulated cobalt as compared to nickel. The present study is perhaps the first attempt to develop transgenic plants expressing heterologous Co transporter with an improved capacity to uptake cobalt.

Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.

Motif mining: an assessment and perspective for amyloid fibril prediction tool.

Amyloid fibril forming regions in protein sequences are associated with a number of diseases. Experimental evidences compel in favor of the hypothesis that short motif regions are responsible for its amyloidogenic behavior. Thus, identifying these short peptides is critical in understanding the cause of diseases associated with aggregation of proteins and developing sequencetargeted anti-aggregation drugs. Owing to the constraints of wet lab molecular techniques for the identification of amyloid fibril forming targets, computational methods are implemented to offer better and affordable in silico predictions. The present study takes into consideration an assessment and perspective of the recent tools available for predicting a peptide status: amyloidogenic or non-amyloidogenic. To the best of our knowledge, the existing review articles on amyloidogenic prediction tools have not touched upon their effectiveness in terms of true positive rates or false positive rates. In this work, we compare few tools such as Aggrescan, Amylpred and FoldAmyloid to evaluate the performance of their predictability based on the experimentally proved data in terms of specificity, sensitivity, Matthews Correlation Coefficient and Balanced accuracy. As evident from the results, a significant reduction of sensitivity associated with a gain in specificity is noted in all the tools considered under the present study.

AmylPepPred: Amyloidogenic Peptide Prediction tool.

UNLABELLED: We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses. AVAILABILITY: AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred.

Identification of B cell epitopes of alcohol dehydrogenase allergen of Curvularia lunata.

BACKGROUND/OBJECTIVE: Epitope identification assists in developing molecules for clinical applications and is useful in defining molecular features of allergens for understanding structure/function relationship. The present study was aimed to identify the B cell epitopes of alcohol dehydrogenase (ADH) allergen from Curvularia lunata using in-silico methods and immunoassay. METHOD: B cell epitopes of ADH were predicted by sequence and structure based methods and protein-protein interaction tools while T cell epitopes by inhibitory concentration and binding score methods. The epitopes were superimposed on a three dimensional model of ADH generated by homology modeling and analyzed for antigenic characteristics. Peptides corresponding to predicted epitopes were synthesized and immunoreactivity assessed by ELISA using individual and pooled patients' sera. RESULT: The homology model showed GroES like catalytic domain joined to Rossmann superfamily domain by an alpha helix. Stereochemical quality was confirmed by Procheck which showed 90% residues in most favorable region of Ramachandran plot while Errat gave a quality score of 92.733%. Six B cell (P1-P6) and four T cell (P7-P10) epitopes were predicted by a combination of methods. Peptide P2 (epitope P2) showed E(X)(2)GGP(X)(3)KKI conserved pattern among allergens of pathogenesis related family. It was predicted as high affinity binder based on electronegativity and low hydrophobicity. The computational methods employed were validated using Bet v 1 and Der p 2 allergens where 67% and 60% of the epitope residues were predicted correctly. Among B cell epitopes, Peptide P2 showed maximum IgE binding with individual and pooled patients' sera (mean OD 0.604±0.059 and 0.506±0.0035, respectively) followed by P1, P4 and P3 epitopes. All T cell epitopes showed lower IgE binding. CONCLUSION: Four B cell epitopes of C. lunata ADH were identified. Peptide P2 can serve as a potential candidate for diagnosis of allergic diseases.

Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.

BACKGROUND: Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. RESULTS: Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. CONCLUSION: A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status - amyloidogenic or non-amyloidogenic. The proposed approach achieves acceptable results, comparable to most online predictors. Besides, it compensates the lacuna in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity.

Molecular and immunological characterization of subtilisin like serine protease, a major allergen of Curvularia lunata.

Serine protease from numerous sources have been identified and characterized as major allergens. The present study aimed to clone, express and characterize a serine protease from Curvularia lunata. cDNA library screening identified partial protease clones. A clone showed significant homology to subtilisin like serine proteases from Aspergillus and Penicillium species. Full length sequence was generated by RACE PCR, subcloned in pET vector, protein expressed in Escherichia coli and purified from inclusion bodies yielding 0.5 mg/L of culture. Bioinformatic analysis identified serine protease motifs of subtilase family, catalytic triad and N-glycosylation sites on the primary sequence. The protein resolved at 54-kDa on SDS-PAGE and was recognized as a major allergen on immunoblot with 13/16 C. lunata sensitive patients' sera in ELISA and immunoblot. Recombinant protein reacted with rabbit polyclonal antibodies against alkaline serine proteases from C. lunata. Recombinant protein required 50-56 ng of same protein for 50% inhibition of IgE binding in competitive ELISA. In addition, 13 of 16 patients' samples showed significant basophil histamine release upon stimulation with purified recombinant protein. In conclusion, a 54 kDa major allergen of C. lunata was cloned, expressed, characterized and showed biological activity. It has potential to be used in molecule based approach for allergy diagnosis and therapy.

Mutated glutathione S-transferase in combination with reduced glutathione shows a synergistic effect in ameliorating oxidative stress and airway inflammation.

Oxidative stress is implicated in the pathogenesis of asthma, and antioxidant levels are reduced in asthma patients. Previously, glutathione S-transferase (GST) with reduced IgE binding suppressed oxidative stress and modulated airway inflammation to some extent in mice. GST catalyzes the quenching of reactive oxygen species by reduced glutathione (GSH) and the absence of any one of them may limit antioxidative behavior. This study evaluates the effects of mutated (m) GST with GSH in combination and individually in limiting oxidative stress and inflammatory responses in a mouse model. BALB/c mice were immunized and challenged with ovalbumin. The mice were treated with mGST, GSH, mGST + GSH, or alpha-lipoic acid by inhalation and sacrificed to evaluate inflammation and oxidative stress parameters. Treatment with the mGST + GSH combination showed significantly reduced total cell (p<0.01) and eosinophil (p<0.01) counts in BALF compared to other groups. The lung inflammation score was lowest for the mGST + GSH group, along with reduced IL-4 (p<0.01) and OVA-specific IgE compared to the other treatment groups. Oxidative stress as per the lipid peroxidation and 8-isoprostane level in BALF of mGST + GSH mice was reduced significantly compared to the individual antioxidants. In conclusion, mGST in combination with GSH has a synergistic effect in reducing airway inflammation compared to the individual antioxidants and has potential for the treatment of asthma.

Heavy metal accumulation in medicinal plants collected from environmentally different sites.

OBJECTIVE: To estimate the heavy metal content in soil and selected medicinal plants procured from environmentally different sites of the same city. METHODS: Soil and plant samples of Abutilon indicum, Calotropis procera, Euphorbia hirta, Peristrophe bycaliculata, and Tinospora cordifolia were collected from 3 environmentally different sites of the city: heavy traffic area (HTA), industrial area (IA), and residential area (RA). Pb, Cd, Cr, and Ni were estimated in soil and plant samples by inductively coupled plasma emission spectrometry and compared. RESULTS: The level of heavy metal was higher in soil than in plant parts studied. Accumulation of heavy metals varied from plant to plant. Pb was the highest in Calotropis procera root from HTA site and the lowest in Peristrophe bycaliculata whole plant from IA site. It was also lower in residential area than in heavy traffic area. CONCLUSION: The level of heavy metal content differed in the same medicinal plant collected from environmentally different sites of the same city. Thus, it reiterates our belief that every medicinal plant sample should be tested for contaminant load before processing it further for medication.

Relative axial ligand orientation in bis(imidazole)iron(II) porphyrinates: are "picket fence" derivatives different?

The synthesis of three new bis(imidazole)-ligated iron(II) picket fence porphyrin derivatives, [Fe(TpivPP)(1-RIm) 2] 1-RIm = 1-methyl-, 1-ethyl-, or 1-vinylimidazole) are reported. X-ray structure determinations reveal that the steric requirements of the four alpha,alpha,alpha,alpha-o-pivalamidophenyl groups lead to very restricted rotation of the imidazole ligand on the picket side of the porphyrin plane; the crowding leads to an imidazole plane orientation eclipsing an iron-porphyrin nitrogen bond. An unusual feature for these diamagnetic iron(II) species is that all three derivatives have the two axial ligands with a relative perpendicular orientation; the dihedral angles between the two imidazole planes are 77.2 degrees , 62.4 degrees , and 78.5 degrees . All three derivatives have nearly planar porphyrin cores. Mössbauer spectroscopic characterization shows that all three derivatives have quadrupole splitting constants around 1.00 mm/s at 100K.

SnCl4-catalyzed reaction of o-benzoquinones and aryl acetylenes: an unprecedented one-pot synthesis of tropone derivatives.

Highly substituted tropone derivatives were obtained as a result of SnCl4-catalyzed cycloaddition of 3-methoxy-substituted o-benzoquinones with aryl acetylenes and subsequent rearrangement of the adducts with concomitant decarbonylation.