Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs. METHODS: The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation . RESULTS: Experimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set. CONCLUSION: These findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions.

Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.

In-silico formulation of a next-generation polyvalent vaccine against multiple strains of monkeypox virus and other related poxviruses.

Mpox (formerly known as monkeypox) virus and some related poxviruses including smallpox virus pose a significant threat to public health, and effective prevention and treatment strategies are needed. This study utilized a reverse vaccinology approach to retrieve conserved epitopes for monkeypox virus and construct a vaccine that could provide cross-protection against related viruses with similar antigenic properties. The selected virulent proteins of monkeypox virus, MPXVgp165, and Virion core protein P4a, were subjected to epitope mapping for vaccine construction. Two vaccines were constructed using selected T cell epitopes and B cell epitopes with PADRE and human beta-defensins adjuvants conjugated in the vaccine sequence. Both constructs were found to be highly antigenic, non-allergenic, nontoxic, and soluble, suggesting their potential to generate an adequate immune response and be safe for humans. Vaccine construct 1 was selected for molecular dynamic simulation studies. The simulation studies revealed that the TLR8-vaccine complex was more stable than the TLR3-vaccine complex. The lower RMSD and RMSF values of the TLR8 bound vaccine compared to the TLR3 bound vaccine suggested better stability and consistency of hydrogen bonds. The Rg values of the vaccine chain bound to TLR8 indicated overall stability, whereas the vaccine chain bound to TLR3 showed deviations throughout the simulation. These results suggest that the constructed vaccine could be a potential preventive measure against monkeypox and related viruses however, further experimental validation is required to confirm these findings.

Correction: An immunoinformatics and extended molecular dynamics approach for designing a polyvalent vaccine against multiple strains of Human T-lymphotropic virus (HTLV).

[This corrects the article DOI: 10.1371/journal.pone.0287416.].

An immunoinformatics and extended molecular dynamics approach for designing a polyvalent vaccine against multiple strains of Human T-lymphotropic virus (HTLV).

Human T-lymphotropic virus (HTLV), a group of retroviruses belonging to the oncovirus family, has long been associated with various inflammatory and immunosuppressive disorders. At present, there is no approved vaccine capable of effectively combating all the highly pathogenic strains of HTLV that makes this group of viruses a potential threat to human health. To combat the devastating impact of any potential future outbreak caused by this virus group, our study employed a reverse vaccinology approach to design a novel polyvalent vaccine targeting the highly virulent subtypes of HTLV. Moreover, we comprehensively analyzed the molecular interactions between the designed vaccine and corresponding Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccine's stability, antigenicity, and immunogenicity. Initially, two vaccine constructs were formulated, and among them, vaccine construct-2 exhibited superior solubility and structural stability. Molecular docking analyses also revealed strong binding affinity between the vaccine construct-2 and both targeted TLR2 and TLR4. Molecular dynamics simulations demonstrated enhanced stability, compactness, and consistent hydrogen bonding within TLR-vaccine complexes, suggesting a strong binding affinity. The stability of the complexes was further corroborated by contact, free energy, structure, and MM-PBSA analyses. Consequently, our research proposes a vaccine targeting multiple HTLV subtypes, offering valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to developing effective preventive and treatment approaches against HTLV-related diseases and preventing possible outbreaks. However, future research should focus on in-depth validation through experimental studies to confirm the interactions identified in silico and to evaluate the vaccine's efficacy in relevant animal models and, eventually, in clinical trials.

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases.

The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7th March 2020, but while writing this paper in June 2021, the total confirmed, recovered, and death cases were 826922, 766266 and 13118, respectively. Due to the emergence of COVID-19 in Bangladesh, the country is facing a major public health crisis. Unfortunately, the country does not have a comprehensive health policy to address this issue. This makes it hard to predict how the pandemic will affect the population. Machine learning techniques can help us detect the disease's spread. To predict the trend, parameters, risks, and to take preventive measure in Bangladesh; this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory. Here, we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh. We extracted the data for daily confirmed, recovered, and death cases from March 2020 to August 2021. The obtained Root Mean Square Error (RMSE) values of confirmed, recovered, and death cases indicates that our result is more accurate than other contemporary techniques. This study indicates that the LSTM model could be used effectively in predicting contagious diseases. The obtained results could help in explaining the seriousness of the situation, also mayhelp the authorities to take precautionary steps to control the situation.

Radionuclides Transfer from Soil to Tea Leaves and Estimation of Committed Effective Dose to the Bangladesh Populace.

Considering the probable health risks due to radioactivity input via drinking tea, the concentrations of 226Ra, 232Th,40K and 137Cs radionuclides in the soil and the corresponding tea leaves of a large tea plantation were measured using high purity germanium (HPGe) γ-ray spectrometry. Different layers of soil and fresh tea leaf samples were collected from the Udalia Tea Estate (UTE) in the Fatickchari area of Chittagong, Bangladesh. The mean concentrations (in Bq/kg) of radionuclides in the studied soil samples were found to be 34 ± 9 to 45 ± 3 for 226Ra, 50 ± 13 to 63 ± 5 for 232Th, 245 ± 30 to 635 ± 35 for 40K and 3 ± 1 to 10 ± 1 for 137Cs, while the respective values in the corresponding tea leaf samples were 3.6 ± 0.7 to 5.7 ± 1.0, 2.4 ± 0.5 to 5.8 ± 0.9, 132 ± 25 to 258 ± 29 and <0.4. The mean transfer factors for 226Ra, 232Th and 40K from soil to tea leaves were calculated to be 0.12, 0.08 and 0.46, respectively, the complete range being 1.1 × 10-2 to 1.0, in accordance with IAEA values. Additionally, the most popularly consumed tea brands available in the Bangladeshi market were also analyzed and, with the exception of 40K, were found to have similar concentrations to the fresh tea leaves collected from the UTE. The committed effective dose via the consumption of tea was estimated to be low in comparison with the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) reference ingestion dose limit of 290 µSv/y. Current indicative tea consumption of 4 g/day/person shows an insignificant radiological risk to public health, while cumulative dietary exposures may not be entirely negligible, because the UNSCEAR reference dose limit is derived from total dietary exposures. This study suggests a periodic monitoring of radiation levels in tea leaves in seeking to ensure the safety of human health.

In silico analysis of nonsynonymous single-nucleotide polymorphisms (nsSNPs) of the SMPX gene.

Mutations in the SMPX gene can disrupt the regular activity of the SMPX protein, which is involved in the hearing process. Recent reports showing a link between nonsynonymous single-nucleotide polymorphisms (nsSNPs) in SMPX and hearing loss, thus classifying deleterious SNPs in SMPX will be an uphill task before designing a more extensive population study. In this study, damaging nsSNPs of SMPX from the dbSNP database were identified by using 13 bioinformatics tools. Initially, the impact of nsSNPs in the SMPX gene were evaluated through different in silico predictors; and the deleterious convergent changes were analyzed by energy-minimization-guided residual network analysis. In addition, the pathogenic effects of mutations in SMPX-mediated protein-protein interactions were also characterized by structural modeling and binding energy calculations. A total of four mutations (N19D, A29T, K54N, and S71L) were found to be highly deleterious by all the tools, which are located at highly conserved regions. Furthermore, all four mutants showed structural alterations, and the communities of amino acids for mutant proteins were readily changed, compared to the wild-type. Among them, A29T (rs772775896) was revealed as the most damaging nsSNP, which caused significant structural deviation of the SMPX protein, as a result reducing the binding affinity to other functional partners. These findings reflect the computational insights into the deleterious role of nsSNPs in SMPX, which might be helpful for subjecting wet-lab confirmatory analysis.

Unveiling the Structural Insights into the Selective Inhibition of Protein Kinase D1.

BACKGROUND: Although protein kinase D1 (PKD1) has been proved to be an efficient target for anticancer drug development, lack of structural details and substrate binding mechanisms are the main obstacles for the development of selective inhibitors with therapeutic benefits. OBJECTIVE: The present study described the in silico dynamics behaviors of PKD1 in binding with selective and non-selective inhibitors and revealed the critical binding site residues for the selective kinase inhibition. METHODS: Here, the three dimensional model of PKD1 was initially constructed by homology modeling along with binding site characterization to explore the non-conserved residues. Subsequently, two known inhibitors were docked to the catalytic site and the detailed ligand binding mechanisms and post binding dyanmics were investigated by molecular dynamics simulation and binding free energy calculations. RESULTS: According to the binding site analysis, PKD1 serves several non-conserved residues in the G-loop, hinge and catalytic subunits. Among them, the residues including Leu662, His663, and Asp665 from hinge region made polar interactions with selective PKD1 inhibitor in docking simulation, which were further validated by the molecular dynamics simulation. Both inhibitors strongly influenced the structural dynamics of PKD1 and their computed binding free energies were in accordance with experimental bioactivity data. CONCLUSION: The identified non-conserved residues likely to play critical role on molecular reorganization and inhibitor selectivity. Taken together, this study explained the molecular basis of PKD1 specific inhibition, which may help to design new selective inhibitors for better therapies to overcome cancer and PKD1 dysregulated disorders.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene.

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.

A Computational workflow for the identification of the potent inhibitor of type II secretion system traffic ATPase of Pseudomonas aeruginosa.

Bacterial type II secretion system has now become an attractive target for antivirulence drug development. The aim of the present study was to characterize the binding site of the type II secretion system traffic ATPase GspER of Pseudomonas aeruginosa, and identify potent inhibitors using extensive computational and virtual screening approaches. Initially, the designed platform focused on the evolutionary relationship of ATPase GspER of P. aeruginosa, followed by protein-protein interaction network analysis to characterize its function. In addition, homology modeling, virtual screening and molecular dynamics simulation have been performed to identify potent hits and understand the ligand binding properties of ATPase GspER. According to the evolutionary relationship, high level of genetic change was observed for P. aeruginosa, bearing orthology relationships with P.alcaligenes and P.otitidis. Concurrently, the binding site analysis represented residue Gly268, Ser267, Thr270, Thr271and Lys269 in Walker A motif directly formed hydrogen bonds with the ATP, which modulates the function of ATPase GspER in the secretion process, is also validated by the molecular docking analysis and molecular dynamics simulation. Furthermore, ZINC04325133 is one of the most potent hits has been identified from virtual screening approach followed by molecular dynamics simulation and MM-GBSA binding energy analysis. These results may provide new knowledge for the development of novel therapeutic strategies against P. aeruginosa, as well as inhibiting secretion system process of a wide range of gram-negative bacteria.

Natural and anthropogenic radioactivity levels and the associated radiation hazard in the soil of Oodalia Tea Estate in the hilly region of Fatickchari in Chittagong, Bangladesh.

Radioactivity in the soil of a tea garden in the Fatickchari area in Chittagong, Bangladesh, was measured using a high-resolution HPGe detector. The soil samples were collected from depths of up to 20 cm beneath the soil surface. The activity concentrations of naturally occurring (238)U and (232)Th were observed to be in the range of 27 ± 7 to 53 ± 8 Bq kg(-1) and 36 ± 11 to 72 ± 11 Bq kg(-1), respectively. The activity concentration of (40)K ranged from 201 ± 78 to 672 ± 81 Bq kg(-1), and the highest activity of fallout (137)Cs observed was 10 ± 1 Bq kg(-1). The average activity concentration observed for (238)U was 39 ± 8 Bq kg(-1), for (232)Th was 57 ± 11 Bq kg(-1), for (40)K was 384 ± 79 Bq kg(-1) and for (137)Cs was 5 ± 0.5 Bq kg(-1). The radiological hazard parameters (representative level index, radium equivalent activity, outdoor and indoor dose rates, outdoor and indoor annual effective dose equivalents, and radiation hazard index) were calculated from the radioactivity in the soil.

Changes in Acidity, TSS, and Sugar Content at Different Storage Periods of the Postharvest Mango (Mangifera indica L.) Influenced by Bavistin DF.

A detailed study was carried out with the postharvest mangoes (namely, the Langra and the Khirshapat) treated with different levels of Bavistin DF (BDF) solution (namely, 250, 500, and 750 ppm) for obtaining results on biochemical changes as well as storability of postharvest mango. The experiment was laid out in randomized complete block design with three replicates. The results of the experiments exhibited that only the single effect of varieties was found to be significant in most of the parameters studied. The Langra enriched a greater quantity of titratable acidity and total soluble solid (TSS) at 3rd day, over the Khirshapat. On the other hand, Khirshapat showed increased pulp pH and TSS at all the storage duration. The results explored that some physicochemical properties, namely, pulp pH, TSS, sugar (total, reducing, and nonreducing), and titratable acidity along with shelf life drastically decreased from untreated mangoes. Bavistin DF with the doses of 750 ppm showed better results in delaying the changes in physicochemical properties and extended shelf life.

Purification, characterizations of a snake guard seeds lectin with antitumor activity against Ehrlich ascites carcinoma cells in vivo in mice.

A lectin was purified (designated as TCSL) from the Snake guard seeds with molecular mass of 56±2 kDa containing two subunits (34±1 and 22±1 kDa.). TCSL exhibited high agglutination activity at the temperature range 30 to 70°C and did not lose its activity between pH 3.0 to 12.0. The lectin was stable in the presence of denaturants and agglutinated mouse, goat, cow, chicken and human erythrocytes. TCSL did not show antifungal activity whereas it agglutinated six pathogenic bacteria and showed less toxicity against brine shrimp nauplii with the LC50 of 261±29 µg/ml. TCSL showed 28% and 72% inhibition against Ehrlich ascites carcinoma (EAC) cells in vivo in mice when administered 1 mg/kg/day and 2 mg/kg/day (i.p.) respectively for five days. TCSL enhanced the number of macrophages remarkably in the normal mice. The lectin reduced the tumor burden to 62% of EAC cells and significantly increased the hemoglobin and RBC. Treating the EAC bearing mice with TCSL at 2 mg/Kg/day for ten days with a monitoring of 20 days decreased the total WBC towards the normal level and it increased the life span by 39%.

Purification and characterization of a Ca(2+)-dependent novel lectin from Nymphaea nouchali tuber with antiproliferative activities.

A lectin (termed NNTL) was purified from the extracts of Nymphaea nouchali tuber followed by anion-exchange chromatography on DEAE-cellulose, hydrophobic chromatography on HiTrap Phenyl HP and by repeated anion-exchange chromatography on HiTrap Q FF column. The molecular mass of the purified lectin was 27.0 ± 1.0 kDa, as estimated by SDS/PAGE both in the presence and in the absence of 2-mercaptoethanol. NNTL was an o-nitrophenyl ß-D-galactopyranoside sugar-specific lectin that agglutinated rat, chicken and different groups of human blood cells and exhibited high agglutination activity over the pH range 5-9 and temperatures of 30-60 °C. The N-terminal sequence of NNTL did not show sequence similarity with any other lectin and the amino acid analysis revealed that NNTL was rich in leucine, methionine and glycine residues. NNTL was a glycoprotein containing 8% neutral sugar and showed toxicity against brine shrimp nauplii with an LC(50) value of 120 ± 29 µg/ml and exerted strong agglutination activity against four pathogenic bacteria (Bacillus subtilis, Sarcina lutea, Shigella shiga and Shigella sonnei). In addition, antiproliferative activity of this lectin against EAC (Ehrlich ascites carcinoma) cells showed 56% and 76% inhibition in vivo in mice at 1.5 and 3 mg·kg(-1)·day(-1) respectively. NNTL was a divalent ion-dependent glycoprotein, which lost its activity markedly in the presence of denaturants. Furthermore, measurement of fluorescence spectra in the presence and absence of urea and CaCl(2) indicated the requirement of Ca(2+) for the stability of NNTL.

Purification and Characterization of Intracellular Cellulase from Aspergillus oryzae ITCC-4857.01.

Purification and characterization of intracellular cellulase produced by A. oryzae ITCC-4857.01 are reported. The enzyme was purified by ion-exchange chromatography using DEAE-cellulose followed by Gel filtration. The purification achieved was 41 fold from the crude extract with yield of 27%. The purified enzyme showed single band on poly acrylamide gel. The molecular weight as determined by SDS-PAGE and gel filtration was 38 KDa and 38.6 KDa respectively and contained only one subunit. The enzyme is glycoprotien as nature and contained 0.67% neutral sugar. The apparent Km value of the enzyme against cellulose was 0.83%. The enzyme showed the highest relative ativities on CMC followed by avicel, salicin and filter paper. The optimum pH of activity was 5.5 and very slight activity was observed at or above pH 7.5 as well as bellow pH 3.5. The optimum tempreture of the activity was 45℃ and the highest activity was exhibited in 35 to 45℃. The enzyme lost their activities almost completely (95~100%) at 80 ℃ or above and as well as bellow 25℃.

Purification, characterization and N-terminal sequences alignment of a mannose specific protein purified from Potca fish, Tetraodon patoca.

A protein was isolated and purified from the ventral portion of the Potca fish, Tetraodon patoca. The method was accomplished by gel filtration of crude protein extract on Sephadex G-50 followed by Ion exchange chromatography on DEAE-cellulose and finally by affinity chromatography on ConA-Sepharose matrix. The molecular weight of the protein, determined by the gel filtration and SDS-PAGE was about 82,000 and 80,000 respectively, but 42,000 and 38,000 were indicated by SDS-PAGE in the presence of 2-mercaptoethanol. The protein agglutinated rat red blood cells and in a haptein-inhibition test, the protein was inhibited specifically by the D-mannose and mannose containing saccharides. The protein is glycoprotein with neutral sugar content of about 0.35%. The purified protein also showed strong cytotoxic effects, which was performed by brine shrimp lethality bioassay and histopathological examinations. The N-terminal amino acid sequences of both the subunits of the protein were also identified and used a blast search on N-terminal amino acid sequences of the subunits revealed that the protein showed significant homology with the homologous proteins in database.

Single step purification, characterization and N-terminal sequences of a mannose specific lectin from mulberry seeds.

A mannose/glucose specific lectin have been isolated and purified from mulberry seeds by affinity chromatography on ConA Sepharose. The lectin is monomer in nature as judged by SDS-PAGE and its MW was estimated to be 22,000. The lectin is glycoprotein with neutral sugar content of 28.57%, and mannose and glucose were identified as carbohydrate. The lectin agglutinated rat red blood cells and in a hapten inhibition test, D: -mannose and D: -glucose was found to be inhibitor. The lectin also exhibited cytotoxic effect in brine shrimp lethality bioassay. The N-terminal sequences of the lectin upto 45-residues except the positions of 21, 39, 42 and 44 were identified. Sequence homology of the lectin is also discussed.