Nipah virus (NiV) is a highly pathogenic, recently emerged paramyxovirus that has been responsible for sporadic outbreaks of respiratory and encephalitic disease in Southeast Asia. High case fatality rates have also been associated with recent outbreaks in Malaysia and Bangladesh. Although over two billion people currently live in regions in which NiV is endemic or in which the Pteropus fruit bat reservoir is commonly found, there is no approved vaccine to protect against NiV disease. This report examines the feasibility and current efforts to develop a NiV vaccine including potential hurdles for technical and regulatory assessment of candidate vaccines and the likelihood for financing.
Hendra and Nipah viruses are henipaviruses that have caused lethal human disease in Australia and Malaysia, Bangladesh, India, and the Philippines, respectively. These viruses are considered Category C pathogens by the US Centers for Disease Control. Nipah virus was recently placed on the World Health Organization Research and Development Blueprint Roadmaps for vaccine and therapeutic development. Given the infrequent and unpredictable nature of henipavirus outbreaks licensure of vaccines and therapeutics will likely require an animal model to demonstrate protective efficacy against henipavirus disease. Studies have shown that nonhuman primates are the most accurate model of human henipavirus disease and would be an important component of any application for licensure of a vaccine or antiviral drug under the US FDA Animal Rule. Nonhuman primate model selection and dosing are discussed regarding vaccine and therapeutic studies against henipaviruses.
Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.
Nipah virus (NiV) is a zoonotic pathogen that causes high case-fatality rates (CFRs) in humans. Two NiV strains have caused outbreaks: the Malaysia strain (NiVM), discovered in 1998-1999 in Malaysia and Singapore (≈40% CFR); and the Bangladesh strain (NiVB), discovered in Bangladesh and India in 2001 (≈80% CFR). Recently, NiVB in African green monkeys resulted in a more severe and lethal disease than NiVM. No NiV vaccines or treatments are licensed for human use. We assessed replication-restricted single-injection recombinant vesicular stomatitis vaccine NiV vaccine vectors expressing the NiV glycoproteins against NiVB challenge in African green monkeys. All vaccinated animals survived to the study endpoint without signs of NiV disease; all showed development of NiV F Ig, NiV G IgG, or both, as well as neutralizing antibody titers. These data show protective efficacy against a stringent and relevant NiVB model of human infection.
Nipah virus (NiV) is a paramyxovirus that causes severe disease in humans and animals. There are two distinct strains of NiV, Malaysia (NiVM) and Bangladesh (NiVB). Differences in transmission patterns and mortality rates suggest that NiVB may be more pathogenic than NiVM. To investigate pathogenic differences between strains, 4 African green monkeys (AGM) were exposed to NiVM and 4 AGMs were exposed to NiVB. While NiVB was uniformly lethal, only 50% of NiVM-infected animals succumbed to infection. Histopathology of lungs and spleens from NiVB-infected AGMs was significantly more severe than NiVM-infected animals. Importantly, a second study utilizing 11 AGMs showed that the therapeutic window for human monoclonal antibody m102.4, previously shown to rescue AGMs from NiVM infection, was much shorter in NiVB-infected AGMs. Together, these data show that NiVB is more pathogenic in AGMs under identical experimental conditions and suggests that postexposure treatments may need to be NiV strain specific for optimal efficacy.
The scientific literature dealing with alcohol and alcoholic beverages revealed that these drinks possess an adverse impact on periodontal tissues. Additionally, other principal risk factors include tobacco, smoking, poor oral hygiene, etc. It has been observed that among chronic alcoholics, there are further issues, such as mental, social, and physical effects, that promote alcoholism. These people may have weak immunity for defense against pathogenic organisms and bacteria. Thus, chances of gingival bleeding, swollen gums, bad breath, and increased bone loss are there. Different alcoholic beverages in the market cause less salivation; these beverages contain sugars that promote acid production in the oral cavity by pathogens that demineralize the enamel and damage gum and teeth. This chronic alcohol consumption can progress into different types of oral disorders, including cancer, halitosis, and caries, and is also associated with tobacco and smoking. Chronic alcohol consumption can cause alteration of the oral microbiome and increase oral pathogens, which lead to periodontal disease and an environment of inflammation created in the body due to malnutrition, diminished immunity, altered liver condition, brain damage, and gut microbiota alteration. Heavily colored alcoholic beverages produce staining on teeth and, due to less saliva, may cause other toxic effects on the periodontium. Over-dependency on alcohol leads to necrotizing lesions such as necrotizing gingivitis, necrotizing periodontitis, and necrotizing stomatitis. These pathological impairments instigate severe damage to oral structures. Therefore, proper counseling by the attending dental surgeon and related health professionals is urgently required for the patient on the basis that the individual case needs to go away from the regular heavy consumption of alcohol.
From January 2008 to December 2009, 433 Streptococcus pneumoniae strains were examined to determine the serotype distribution and susceptibility to selected antibiotics. About 50% of them were invasive isolates. The strains were isolated from patients of all age groups and 33.55% were isolated from children below 5 years. The majority was isolated from blood (48.53%) and other sterile specimens (6.30%). Community acquired pneumonia (41.70%) is the most common diagnosis followed by sepsis (9.54%). Serotyping was done using Pneumotest Plus-Kit and antibiotic susceptibility pattern was determined by modified Kirby-Bauer disk diffusion method and measurement of minimum inhibitory concentration (MIC) using E-test strip. Ten most common serotypes were 19F (15.02%), 6B (10.62%), 19A (6.93%), 14 (6.70%), 1 (5.08%), 6A (5.08%), 23F (4.85%), 18C (3.93%), 3 (2.08%) and 5 (1.85%). Penicillin MIC ranged between ≤ 0.012-4 μg/ml with MIC₉₀ of 1 μg/ml. Penicillin resistant rate is 31.78%. The majority of penicillin less-susceptible strains belonged to serotype 19F followed by 19A and 6B. Based on the serotypes distribution 22 (44.00%), 28 (56.00%) and 39 (78.00%) of the invasive isolates from children ≤ 2 years were belonged to serotypes included in the PCV7, PCV10 and PCV13, respectively.
Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.
Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS 269 ). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS 269 . Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS 269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS 269 constructed with fine-mapping.
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.