Nanobiostimulant action of trigolic formulated zinc sulfide nanoparticles (ZnS-T NPs) on rice seeds by triggering antioxidant defense network and plant growth specific transcription factors.

Under a changing climate, nanotechnological interventions for climate resilience in crops are critical to maintaining food security. Prior research has documented the affirmative response of nano zinc sulfide (nZnS) on physiological traits of fungal-infested rice seeds. Here, we propose an application of trigolic formulated zinc sulfide nanoparticles (ZnS-T NPs) on rice seeds as nanobiostimulant to improve physiological parameters by triggering antioxidative defense system, whose mechanism was investigated at transcriptional level by differential expression of genes in germinated seedlings. Nanopriming of healthy rice seeds with ZnS-T NPs (50 µg/ml), considerably intensified the seed vitality factors, including germination percentage, seedling length, dry weight and overall vigor index. Differential activation of antioxidant enzymes, viz. SOD (35.47%), APX (33.80%) and CAT (45.94%), in ZnS-T NPs treated seedlings reduced the probability of redox imbalance and promoted the vitality of rice seedlings. In gene expression profiling by reverse transcription quantitative real time PCR (qRT-PCR), the notable up-regulation of target antioxidant genes (CuZn SOD, APX and CAT) and plant growth specific genes (CKX and GRF) in ZnS-T NPs treated rice seedlings substantiates their molecular role in stimulating both antioxidant defenses and plant growth mechanisms. The improved physiological quality parameters of ZnS-T NPs treated rice seeds under pot house conditions corresponded well with in vitro findings, which validated the beneficial boosted impact of ZnS-T NPs on rice seed development. Inclusively, the study on ZnS-T NPs offers fresh perspectives into biochemical and molecular reactions of rice, potentially positioning them as nanobiostimulant capable of eliciting broad-spectrum immune and growth-enhancing responses.

Deciphering the Genomic Landscape and Virulence Mechanisms of the Wheat Powdery Mildew Pathogen Blumeria graminis f. sp. tritici Wtn1: Insights from Integrated Genome Assembly and Conidial Transcriptomics.

A high-quality genome sequence from an Indian isolate of Blumeria graminis f. sp. tritici Wtn1, a persistent threat in wheat farming, was obtained using a hybrid method. The assembly of over 9.24 million DNA-sequence reads resulted in 93 contigs, totaling a 140.61 Mb genome size, potentially encoding 8480 genes. Notably, more than 73.80% of the genome, spanning approximately 102.14 Mb, comprises retro-elements, LTR elements, and P elements, influencing evolution and adaptation significantly. The phylogenomic analysis placed B. graminis f. sp. tritici Wtn1 in a distinct monocot-infecting clade. A total of 583 tRNA anticodon sequences were identified from the whole genome of the native virulent strain B. graminis f. sp. tritici, which comprises distinct genome features with high counts of tRNA anticodons for leucine (70), cysteine (61), alanine (58), and arginine (45), with only two stop codons (Opal and Ochre) present and the absence of the Amber stop codon. Comparative InterProScan analysis unveiled "shared and unique" proteins in B. graminis f. sp. tritici Wtn1. Identified were 7707 protein-encoding genes, annotated to different categories such as 805 effectors, 156 CAZymes, 6102 orthologous proteins, and 3180 distinct protein families (PFAMs). Among the effectors, genes like Avra10, Avrk1, Bcg-7, BEC1005, CSEP0105, CSEP0162, BEC1016, BEC1040, and HopI1 closely linked to pathogenesis and virulence were recognized. Transcriptome analysis highlighted abundant proteins associated with RNA processing and modification, post-translational modification, protein turnover, chaperones, and signal transduction. Examining the Environmental Information Processing Pathways in B. graminis f. sp. tritici Wtn1 revealed 393 genes across 33 signal transduction pathways. The key pathways included yeast MAPK signaling (53 genes), mTOR signaling (38 genes), PI3K-Akt signaling (23 genes), and AMPK signaling (21 genes). Additionally, pathways like FoxO, Phosphatidylinositol, the two-component system, and Ras signaling showed significant gene representation, each with 15-16 genes, key SNPs, and Indels in specific chromosomes highlighting their relevance to environmental responses and pathotype evolution. The SNP and InDel analysis resulted in about 3.56 million variants, including 3.45 million SNPs, 5050 insertions, and 5651 deletions within the whole genome of B. graminis f. sp. tritici Wtn1. These comprehensive genome and transcriptome datasets serve as crucial resources for understanding the pathogenicity, virulence effectors, retro-elements, and evolutionary origins of B. graminis f. sp. tritici Wtn1, aiding in developing robust strategies for the effective management of wheat powdery mildew.

Evaluation of potential role of R-loop and G-quadruplex DNA in the fragility of c-MYC during chromosomal translocation associated with Burkitt's lymphoma.

t(8;14) translocation is the hallmark of Burkitt's lymphoma and results in c-MYC deregulation. During the translocation, c-MYC gene on chromosome 8 gets juxtaposed to the Ig switch regions on chromosome 14. Although the promoter of c-MYC has been investigated for its mechanism of fragility, little is known about other c-MYC breakpoint regions. We have analyzed the translocation break points at the exon 1/intron 1 of c-MYC locus from patients with Burkitt's lymphoma. Results showed that the breakpoint region, when present on a plasmid, could fold into an R-loop confirmation in a transcription-dependent manner. Sodium bisulfite modification assay revealed significant single-strandedness on chromosomal DNA of Burkitt's lymphoma cell line, Raji, and normal lymphocytes, revealing distinct R-loops covering up to 100 bp region. Besides, ChIP-DRIP analysis reveals that the R-loop antibody can bind to the breakpoint region. Further, we show the formation of stable parallel intramolecular G-quadruplex on non-template strand of the genome. Finally, incubation of purified AID in vitro or overexpression of AID within the cells led to enhanced mutation frequency at the c-MYC breakpoint region. Interestingly, anti-γH2AX can bind to DSBs generated at the c-MYC breakpoint region within the cells. The formation of R-loop and G-quadruplex was found to be mutually exclusive. Therefore, our results suggest that AID can bind to the single-stranded region of the R-loop and G4 DNA, leading to the deamination of cytosines to uracil and induction of DNA breaks in one of the DNA strands, leading to double-strand break, which could culminate in t(8;14) chromosomal translocation.

Autoimmune encephalitis: A diagnostic enigma.

Autoimmune encephalitis with anti-NMDA (N-methyl-D-aspartate) receptor encephalitis is the most common type. This condition can be associated with underlying malignancy, making appropriate screening essential. Prompt identification and intervention of autoimmune encephalitis is essential for a successful outcome and full recovery from this serious and potentially fatal condition. Immunotherapy is typically used to suppress the immune response and reduce brain inflammation. The choice of therapy depends on the type and severity of autoimmune encephalitis, as well as the presence of an underlying tumour.

Is Omicron really mild? - Comparative analysis of comorbidities and disease outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants.

PURPOSE: Multiple variants of SARS-CoV-2 from Alpha to Omicron have an estimated 6.1 million deaths globally till date. These variants have been found to vary in transmissibility and severity. The present study deals with comparison of morbidity and mortality with SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants. MATERIALS AND METHOD: An observational retrospective cohort study was conducted on a cohort of laboratory confirmed patients of SARS-CoV-2 diagnosed by qRT-PCR of nasopharyngeal swabs in periods; April-2021 and January-2022; that were sequenced and variants were recorded. Patients were invited for a telephonic interview after voluntary and informed consent was obtained from each participant wherein, the demographics, co-morbidities, oxygen requirement and mortality outcomes of the patients were enquired about. RESULTS: A total of 200 patients, with 100 from each period were included in the study. Major comorbidities in patients included hypertension, diabetes mellitus and pulmonary disease. Patients who succumbed to the Delta variant (26%) were higher as compared to the Omicron variant (10%); with the elderly (68 â€‹± â€‹9.7 â€‹years) having significant mortality during the Omicron variant. The mortality was increased in patients with comorbidities as with hypertension (53.8%, 70%), diabetes mellitus (26.9%, 40%), chronic pulmonary disease (30.8%, 20%), and smoking (15.4%, 40%) in the patients infected with both Delta and Omicron variants, respectively. CONCLUSION: The study concluded that the newer strains of SARS-CoV-2 have potential of high transmissibility and milder disease for the population by large, however, for patients with comorbidities have a higher proportion of adverse outcomes, irrespective of the variant.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

AndroPred: an artificial intelligence-based model for predicting androgen receptor inhibitors.

Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. Understanding AR molecular mechanisms has led to the development of newer drugs that inhibit androgen production enzymes or block ARs. The FDA has approved a small number of AR-inhibiting drugs for use in PCa thus far, as the identification of novel AR inhibitors is difficult, expensive, time-consuming, and labor-intensive. To accelerate the process, artificial intelligence (AI) algorithms were employed to predict AR inhibitors using a dataset of 2242 compounds. Four machine learning (ML) and deep learning (DL) algorithms were used to train different prediction models based on molecular descriptors (1D, 2D, and molecular fingerprints). The DL-based prediction model outperformed the other trained models with accuracies of 92.18% and 93.05% on the training and test datasets, respectively. Our findings highlight the potential of DL, particularly the DNN model, as an effective approach for predicting AR inhibitors, which could significantly streamline the process of identifying novel AR inhibitors in PCa drug discovery. Further validation of these models using experimental assays and prospective testing of newly designed compounds would be valuable to confirm their predictive power and applicability in practical drug discovery settings.Communicated by Ramaswamy H. Sarma.

Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

BACKGROUND: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. METHODS: Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. RESULTS: The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. CONCLUSION: The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.

Artificial intelligence in intestinal polyp and colorectal cancer prediction.

Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.

Exposure to endosulfan can cause long term effects on general biology, including the reproductive system of mice.

Increased infertility in humans is attributed to the increased use of environmental chemicals in the last several decades. Various studies have identified pesticides as one of the causes of reproductive toxicity. In a previous study, infertility was observed in male mice due to testicular atrophy and decreased sperm count when a sublethal dose of endosulfan (3 mg/kg) with a serum concentration of 23 µg/L was used. However, the serum concentration of endosulfan was much higher (up to 500 µg/L) in people living in endosulfan-exposed areas compared to the one used in the investigation. To mimic the situation in an experimental setup, mice were exposed to 5 mg/kg body weight of endosulfan, and reproductive toxicity and long-term impact on the general biology of animals were examined. HPLC analysis revealed a serum concentration of ∼50 µg/L of endosulfan after 24 h endosulfan exposure affected the normal physiology of mice. Histopathological studies suggest a persistent, severe effect on reproductive organs where vacuole degeneration of basal germinal epithelial cells and degradation of the interstitial matrix were observed in testes. Ovaries showed a reduction in the number of mature Graafian follicles. At the same time, mild vacuolation in liver hepatocytes and changes in the architecture of the lungs were observed. Endosulfan exposure induced DNA damage and mutations in germ cells at the molecular level. Interestingly, even after 8 months of endosulfan exposure, we observed increased DNA breaks in reproductive tissues. An increased DNA Ligase III expression was also observed, consistent with reported elevated levels of MMEJ-mediated repair. Further, we observed the generation of tumors in a few of the treated mice with time. Thus, the study not only explores the changes in the general biology of the mice upon exposure to endosulfan but also describes the molecular mechanism of its long-term effects.

Formulation and Characterization of Raloxifene Nanostructured Lipid Carriers for Permeability and Uptake Enhancement Applications.

Raloxifene (RLX), a biopharmaceutical classification system (BCS) class II drug, is a selective estrogen receptor modulator (SERM) having an estrogenic effect on the bone and an antiestrogenic effect on the endometrium and breast. Low solubility, high permeability, high metabolism, and low bioavailability are the characteristics of raloxifene. Although 60% is absorbed orally, raloxifene shows extremely poor bioavailability (2%) owing to its low solubility and extensive (>90%) intestinal/hepatic first-pass metabolism. Hence, it becomes important to increase the solubility of raloxifene to enhance its bioavailability. In this study, raloxifene nanostructured lipid carriers (RNLCs) were prepared using the melt dispersion ultrasonication method. The prepared RNLCs were characterized, and the in vitro studies were carried out in the human epithelial breast cancer cell line (MCF-7). The RNLCs had a size of 114.8 ± 0.98 nm and a zeta potential of +9.21 ± 0.58 mV. Transmission electron microscopy (TEM) images showed particle size ranging from 65 to 120 nm. With an entrapment efficiency of 75.04% ± 2.75%, the RNLCs showed sustained release over 7 days compared with the raloxifene drug solution. The prepared RNLCs were successfully taken up by the MCF-7 cells in a time-dependent manner, and the RNLCs showed increased cell cytotoxicity compared with the raloxifene drug. Using the parallel artificial membrane permeability assay (PAMPA), the permeability rate for raloxifene solution was calculated to be 8 × 10-6 cm/s, and for the RNLCs, it was calculated to be 17.8 × 10-6 cm/s. Hence, from the permeability rate calculated, we could conclude that raloxifene, when formulated as nanostructured lipid carriers, showed increased permeability. Overall, the prepared RNLCs were found to be superior to the raloxifene drug as such.

Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases.

There has been substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remote health monitoring using sensors and smartphones. A variety of AI-based prediction models are available for gastrointestinal, inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, hepatitis-associated fibrosis using electronic medical records, and pancreatic carcinoma utilizing endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patients' treatment employing multiple factors. Enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI-based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitations of AI techniques in such diseases' prognosis, risk assessment, and decision support are discussed.

In silico identification of therapeutic compounds against microRNA targets in drug-resistant pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a major health issue that has been eluding efforts to identify viable therapeutic treatment options. Besides having the lowest survival rate among all types of cancer, almost all conventional methods of treatment are futile against this condition, leaving patients to succumb to this ailment faster than ever. As it is increasingly becoming difficult to come up with new compounds for the treatment of various diseases, alternative solutions are required for tackling these problems. In this study, publically available miRNA and gene expression data were used to identify common elements that were present in gemcitabine-resistant PDAC cell lines. By selecting overexpressed genes involved in pancreatic cancer and cancer pathways in general, potential drug candidates for the treatment of PDAC were identified. In this study, 21 differentially expressed miRNAs were identified from PANC-1 cell line treated with gemcitabine. Pathway analysis revealed that MET and PPARG were overexpressed in cancer-related pathways, including pancreatic cancer, and could be targeted for PDAC treatment. Using CMap, fisetin was identified a likely candidate drug for the treatment of PDAC. Docking studies indicated that fisetin was bound to c-Met and PPARG with an XP G score of -12.819 and -7.021 kcal/mol, respectively. As miRNAs have increasingly been shown to part take in important cancer-related processes and pathways, researching drug development methods based on miRNA targets could be beneficial for pharmaceutical industries. Communicated by Ramaswamy H. Sarma.

Fluoride and endosulfan together potentiate cytogenetic effects in Swiss albino mice bone marrow cells.

In this study, the cytotoxic potential of fluoride and endosulfan in combination was investigated in Swiss albino mice bone marrow cells using the chromosomal aberration (CA) and micronucleus (MN) test systems. Fluoride (25.1 mg kg-1 body weight [bw] in water) and endosulfan (1.8 mg kg-1 bw by oral intubation) were administered orally alone and in combination (fluoride 25.1 mg kg-1 bw + endosulfan 1.8 mg kg-1 bw) to male Swiss albino mice daily for 30 days. A significant (p < 0.01) increase in micronuclei (MNs) induction and decreased ratio (p < 0.01) of polychromatic to normonochromatic erythrocytes (indicators of cytotoxicity) were observed compared with saline controls when animals were given the combination of fluoride and endosulfan. A significant (p < 0.01) increase in MNs induction and no change in the polychromatic erythrocytes to erythrocyte ratio were also observed when endosulfan was given alone. CAs such as gaps, breaks, fragments, rings, exchanges, and polyploidy were recorded in the bone marrow cells. The mean percent frequency of CAs was increased (p < 0.01) in all the treated groups compared with the control saline group. In the combination group (F + E), the percent frequencies of CAs were significantly higher (13.875%) compared with those in the individual treatment groups of fluoride (4.375%) and endosulfan (6.25%). The mitotic index was calculated as percentage of dividing cells. A significant (p < 0.01) decrease in mitotic index was observed in all treated groups compared with controls. In the combination group (F + E), mitotic index was significantly less than (p < 0.01; 4.1 ± 0.49) the saline control (10.8 ± 0.98). These results indicated that repeated intake of endosulfan through various sources in fluoride affected areas resulted in increased cytotoxic effects. The greater effect in the combination group indicated additive interaction of fluoride and endosulfan in inducing cytotoxicity in Swiss albino mice.

Elaeagnus angustifolia Plant Extract Inhibits Epithelial-Mesenchymal Transition and Induces Apoptosis via HER2 Inactivation and JNK Pathway in HER2-Positive Breast Cancer Cells.

Elaeagnus angustifolia (EA) is a medicinal plant used for treating several human diseases in the Middle East. Meanwhile, the outcome of EA extract on HER2-positive breast cancer remains nascent. Thus, we herein investigated the effects of the aqueous EA extract obtained from the flowers of EA on two HER2-positive breast cancer cell lines, SKBR3 and ZR75-1. Our data revealed that EA extract inhibits cell proliferation and deregulates cell-cycle progression of these two cancer cell lines. EA extract also prevents the progression of epithelial-mesenchymal transition (EMT), an important event for cancer invasion and metastasis; this is accompanied by upregulations of E-cadherin and ß-catenin, in addition to downregulations of vimentin and fascin, which are major markers of EMT. Thus, EA extract causes a drastic decrease in cell invasion ability of SKBR3 and ZR75-1 cancer cells. Additionally, we found that EA extract inhibits colony formation of both cell lines in comparison with their matched control. The molecular pathway analysis of HER2 and JNK1/2/3 of EA extract exposed cells revealed that it can block HER2 and JNK1/2/3 activities, which could be the major molecular pathway behind these events. Our findings implicate that EA extract may possess chemo-preventive effects against HER2-positive breast cancer via HER2 inactivation and specifically JNK1/2/3 signaling pathways.

Co-prevalence of human Papillomaviruses (HPV) and Epstein-Barr virus (EBV) in healthy blood donors from diverse nationalities in Qatar.

BACKGROUND: Infections by both human oncoviruses, human Papillomaviruses (HPV) and Epstein-Barr virus (EBV) are very common in the adult human population and are associated with various malignancies. While HPV is generally transmitted sexually or via skin-to-skin contact, EBV is frequently transmitted by oral secretions, blood transfusions and organ transplants. This study aims to determine the prevalence and circulating genotypes of HPV and EBV in healthy blood donors in Qatar. METHODS: We explored the co-prevalence of high-risk HPVs and EBV in 378 males and only 7 females blood donors of different nationalities (mainly from Qatar, Egypt, Syria, Jordan, Pakistan, and India) residing in Qatar, using polymerase chain reaction (PCR). DNA was extracted from the buffy coat and genotyping was performed using PCR and nested-PCR targeting E6 and E7 as well as LMP-1 of HPV and EBV, respectively. RESULTS: We found that from the total number of 385 cases of healthy blood donors studied, 54.8% and 61% of the samples are HPVs and EBV positive, respectively. Additionally, our data revealed that the co-presence of both high-risk HPVs and EBV is 40.4% of the total samples. More significantly, this study pointed out for the first time that the most frequent high-risk HPV types in Qatar are 59 (54.8%), 31 (53.7%), 52 (49.1%), 51 (48.6%), 58 (47%) and 35 (45.5%), while the most commonly expressed low-risk HPV types are 53 (50.6%), 11 (45.5), 73 (41.7%) and 6 (41.3%), with all the cases showing multiple HPVs infection. CONCLUSION: In this study, we demonstrated for the first time that HPV and EBV are commonly co-present in healthy blood donors in Qatar. On the other hand, it is important to highlight that these oncoviruses can also be co-present in several types of human cancers where they can cooperate in the initiation and/or progression of these cancers. Therefore, more studies regarding the co-presence of these oncoviruses and their interaction are necessary to understand their cooperative role in human diseases.

Therapeutic Promises of Chlorogenic Acid with Special Emphasis on its Anti-Obesity Property.

BACKGROUND: Chlorogenic acid (CGA) is a quinic acid conjugate of caffeic acid. It is an ester formed between caffeic acid and the 3-hydroxyl of L-quinic acid. This polyphenol is naturally present in substantial amount in the green coffee beans. Minor quantities of CGA are also reported in apples, eggplant, blueberries, tomatoes, strawberries and potatoes. CGA is reported to be beneficial in hypertension, hyperglycemia, antimicrobial, antitumor, memory enhancer, weight management etc. Further, it is also reported to have anticancer, antioxidant and anti-inflammatory activities. Since the last decade, CGA drew public attention for its widely recommended use as a medicine or natural food additive supplement for the management of obesity. OBJECTIVE: The current review explores the medicinal promises of CGA and emphasizes on its antiobese property as reported by various scientific reports and publication. CONCLUSION: CGA shows promises as an antioxidant, glycemic control agent, anti-hypertensive, antiinflammatory, antimicrobial, neuro-protective and anti-obesity agent. It primarily activates the AMPactivated protein kinase, inhibits 3-hydroxy 3-methylglutaryl coenzyme-A reductase and strengthens the activity of carnitine palmitoyltransferase to control the obesity.

Planning, implementation, and evaluation of multicomponent, case-based learning for first-year Indian medical undergraduates.

Didactic lecture is an effective method to quickly pass on a high volume of information to a large number of students. However, if not well designed, lectures can be monotonous and provide only passive learning, with little scope for higher order learning skills. To address this drawback of lectures, we supplemented it with case-based learning (CBL), which has been shown to promote self-learning. After giving an overview of gastrointestinal physiology through lectures, CBL on peptic ulcer disease was implemented for first-year Bachelor of Medicine, Bachelor of Surgery students. The present study aimed to evaluate the students' and teachers' opinions on the notion of supplementing lectures with CBL. In previous reports, discussion using clinical cases was primarily employed as the solitary component for conducting CBL. In the present study, three different but mutually exclusive components, such as case discussion, concept map, and critical thinking exercise on a specific topic in gastrointestinal pathophysiology, were integrated to form the multicomponent CBL (MC-CBL). Students reported that MC-CBL could promote application of the knowledge learned in lectures in a more appropriate context (92.42% positive response), enhance their learning efficiency (98.46% positive response), promote their active participation in the learning process (98.48% positive response), and help them in integrating physiological concepts with clinical science (98.46% positive response). Teachers observed that MC-CBL could promote active learning, analytic, and problem-solving skills of students. In conclusion, MC-CBL appeared to be an effective supplement for the lectures, providing an opportunity for the students to relate the knowledge learned during lectures.