A state-of-the-art review of metal oxide nanoflowers for wastewater treatment: Dye removal.

Dye wastewater consists of high solids concentrations, heavy metals, minor contaminants, dissolved chemical oxygen demand, and microorganisms. Nanoflowers are nanoparticles that resemble flowers when viewed at a microscopic level. Inorganic metal oxide nanoflowers have been discovered to be a potential source for overcoming this situation. Their flower-like features give them a higher surface area to volume ratio and porosity structure, which can absorb a significant amount of dye. The metal oxide nanoflower synthesized from different synthesis methods is used to compare which one is cost-effective and capable of generating a large scale of nanoflower. This review has demonstrated outstanding dye removal efficiency by applying inorganic nanoflowers to dye removal. Since both adsorption and photocatalytic reactions enhance the dye degradation process, complete dye degradation could be achieved. Meanwhile, the inorganic metal oxide nanoflowers' exemplary reusability characteristics with negligible performance drop further prove that this approach is highly sustainable and may help to save costs. This review has proven the momentum of obtaining high dye removal efficiency in wastewater treatment to conclude that the metal oxide nanoflower study is worth researching.

Advanced oxidation and biological integrated processes for pharmaceutical wastewater treatment: A review.

The stress of pharmaceutical and personal care products (PPCPs) discharging to water bodies and the environment due to increased industrialization has reduced the availability of clean water. This poses a potential health hazard to animals and human life because water contamination is a great issue to the climate, plants, humans, and aquatic habitats. Pharmaceutical compounds are quantified in concentrations ranging from ng/Lto µg/L in aquatic environments worldwide. According to (Alsubih et al., 2022), the concentrations of carbamazepine, sulfamethoxazole, Lutvastatin, ciprofloxacin, and lorazepam were 616-906 ng/L, 16,532-21635 ng/L, 694-2068 ng/L, 734-1178 ng/L, and 2742-3775 ng/L respectively. Protecting and preserving our environment must be well-driven by all sectors to sustain development. Various methods have been utilized to eliminate the emerging pollutants, such as adsorption and biological and advanced oxidation processes. These methods have their benefits and drawbacks in the removal of pharmaceuticals. Successful wastewater treatment can save the water bodies; integrating green initiatives into the main purposes of actor firms, combined with continually periodic awareness of the current and potential implications of environmental/water pollution, will play a major role in water conservation. This article reviews key publications on the adsorption, biological, and advanced oxidation processes used to remove pharmaceutical products from the aquatic environment. It also sheds light on the pharmaceutical adsorption capability of adsorption, biological and advanced oxidation methods, and their efficacy in pharmaceutical concentration removal. A research gap has been identified for researchers to explore in order to eliminate the problem associated with pharmaceutical wastes. Therefore, future study should focus on combining advanced oxidation and adsorption processes for an excellent way to eliminate pharmaceutical products, even at low concentrations. Biological processes should focus on ideal circumstances and microbial processes that enable the simultaneous removal of pharmaceutical compounds and the effects of diverse environments on removal efficiency.

Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases.

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Rapid adsorptive removal of eosin yellow and methyl orange using zeolite Y.

In this study, zeolite Y was synthesised using a novel method. The heat generated from the reaction of H2SO4 with metakaolin was used as a heat source instead of applying external heat for the dealuminated process. The synthesised zeolite Y produced was analysed by scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier-infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDS) and Brunauer-Emmett-Teller (BET). Zeolite Y synthesis was mesoporous because of its pore diameter (30.53 nm), as shown in the BET results. Surface area and pore size decrease after adsorption due to dye deposition on the adsorbent's surface. FTIR has bonds like O-H, C-H, -CH3, and -COOH responsible for adsorption. The maximum adsorption capacity of eosin yellow (EY) and methyl orange (MO) on to zeolite Y by the Langmuir isotherm was 52.91 mg/g and 20.62 mg/g respectively, at pH 2.5 and 8 for EY and MO dye. The batch adsorption studies were conducted, and the influence of different parameters (i.e., adsorbent dose, adsorption time, initial dye concentration, pH and temperature) was investigated. Experimental data were analysed by two linear model equations (Langmuir and Freundlich isotherms), and it was found that the Langmuir isotherm model best describes the adsorption data for methyl orange and Freundlich isotherm for eosin yellow, respectively. Adsorption rate constants were determined using linear pseudo-first-order and pseudo-second-order. The results showed that MO and EY dye adsorption onto zeolite Y followed a pseudo-second-order kinetic model. Thermodynamic studies show that adsorption was an exothermic reaction (enthalpy < 0) and feasible ([Formula: see text]) at various temperatures under investigation.

Validity of nomograms for predicting cancer risk in oral leukoplakia and oral lichen planus.

OBJECTIVES: This study assessed the validity of nomograms for predicting malignant transformation (MT) among patients with oral leukoplakia (OL) and oral lichen planus (OLP). MATERIALS AND METHODS: Two nomograms were identified following a systematic search. Variables to interrogate both nomograms were obtained for a retrospective OL/OLP cohort. Then, the nomograms were applied to estimate MT probabilities twice and their average was used to calculate the discriminatory performance, calibration, and potential net benefit of the models. Subgroup analyses were performed for patients with OL, OLP, and oral epithelial dysplasia. RESULTS: Predicted probabilities were mostly significantly higher among OL/OLP patients who developed MT compared to those who did not (p = <0.001-0.034). AUC values and Brier scores of the nomograms were 0.644-0.844 and 0.040-0.088 among OL patients and 0.580-0.743 and 0.008-0.072 among OLP patients. Decision curve analysis suggested that the nomograms had some net benefit for risk stratification. However, the models did not best binary dysplasia grading in discriminatory validity and net benefit among patients with OL and oral epithelial dysplasia. CONCLUSION: Nomograms for predicting MT may have satisfactory validity among patients with OL than OLP, but they do not outperform binary dysplasia grading in risk stratification of OL.

Bayesian Disease Mapping to Identify High-Risk Population for Oral Cancer: A Retrospective Spatiotemporal Analysis.

Objectives: Bayesian mapping is an effective spatiotemporal approach to identify high-risk geographic areas for diseases and has not been used to identify oral cancer hotspots in Australia previously. This retrospective disease mapping study was undertaken to identify the oral cancer trends and patterns within the Queensland state in Australia. Methods: This study included data obtained from Queensland state Cancer Registry from 1982 to 2018. Domains mapped included the oral cancer incidence and mortality in Queensland (QLD). Local government areas (LGAs) and suburbs were utilized as geographical units for the estimation using Bayesian mapping approach. Results: Of the 78 LGAs, 21 showed high-oral cancer incidence as measured using higher median smoothed incidence risk (SIR), above the state average. Specifically, nine LGAs within predominantly rural areas had SIR above 100% of the state average. Of these, only one LGA (Mount Isa City) had a median smoothed SIR and 95% CI of 2.61 (2.14-3.15) which was constantly above 100% of the state average. Furthermore, mortality risk estimated using smoothed mortality risk (SMR), were significantly higher than the state average in 31 LGAs. Seventeen LGAs had a median SMR above 100% of the state average while three LGAs had the highest overall, 3- and 5-year mortality risks. Considering the 95% credible interval which is indicative of the uncertainty around the estimates, three LGAs had the highest overall mortality risks-Yarrabah Aboriginal Shire (3.80 (2.16-6.39)), Cook Shire (3.37 (2.21-5.06)), and Mount Isa City (3.04 (2.40-3.80)). Conclusion: Bayesian disease mapping approach identified multiple incidence and mortality hotspots within regional areas of the Queensland. Findings from our study can aid in designing targeted public health screening and interventions for primary prevention of oral cancer in regional and remote communities.

The quorum-sensing peptidic inhibitor rescues host immune system eradication: A novel infectivity mechanism.

Subverting the host immune system is a major task for any given pathogen to assure its survival and proliferation. For the opportunistic human pathogen Bacillus cereus (Bc), immune evasion enables the establishment of potent infections. In various species of the Bc group, the pleiotropic regulator PlcR and its cognate cell-cell signaling peptide PapR7 regulate virulence gene expression in response to fluctuations in population density, i.e., a quorum-sensing (QS) system. However, how QS exerts its effects during infections and whether PlcR confers the immune evading ability remain unclear. Herein, we report how interception of the QS communication in Bc obliterates the ability to affect the host immune system. Here, we designed a peptide-based QS inhibitor that suppresses PlcR-dependent virulence factor expression and attenuates Bc infectivity in mouse models. We demonstrate that the QS peptidic inhibitor blocks host immune system-mediated eradication by reducing the expression of PlcR-regulated major toxins similarly to the profile that was observed for isogenic strains. Our findings provide evidence that Bc infectivity is regulated by QS circuit-mediated destruction of host immunity, thus reveal a interesting strategy to limit Bc virulence and enhance host defense. This peptidic quorum-quenching agent constitutes a readily accessible chemical tool for studying how other pathogen QS systems modulate host immunity and forms a basis for development of anti-infective therapeutics.

Antimicrobial-Resistant Listeria monocytogenes in Ready-to-Eat Foods: Implications for Food Safety and Risk Assessment.

Antimicrobial resistance is an existential threat to the health sector, with far-reaching consequences in managing microbial infections. In this study, one hundred and ninety-four Listeria monocytogenes isolates were profiled for susceptibility using disc diffusion techniques. Possible foodborne listeriosis risk associated with ready-to-eat (RTE) foods (RTEF) and the risk of empirical treatment (EMPT) of L. monocytogenes infections, using multiple antimicrobial resistance indices (MARI) and antimicrobial resistance indices (ARI), respectively, were investigated. Twelve European Committee on Antimicrobial Susceptibility Testing (EUCAST) prescribed/recommended antimicrobials (EPAS) for the treatment of listeriosis and ten non-prescribed antimicrobials (non-PAS)] were evaluated. Antimicrobial resistance > 50% against PAs including sulfamethoxazole (61.86%), trimethoprim (56.19%), amoxicillin (42.27%), penicillin (41.24%), and erythromycin (40.21%) was observed. Resistance > 50% against non-PAS, including oxytetracycline (60.89%), cefotetan (59.28%), ceftriaxone (53.09%), and streptomycin (40.21%) was also observed. About 55.67% and 65.46% of the isolates had MARI scores ranging from 0.25-0.92 and 0.30-0.70 for EPAs and non-PAs, respectively. There was a significant difference (p < 0.01) between the MARI scores of the isolates for EPAs and non-PAs (means of 0.27 ± 0.21 and 0.31 ± 0.14, respectively). MARI/ARI scores above the Krumperman permissible threshold (>0.2) suggested a high risk/level of antimicrobial-resistant L. monocytogenes. The MARI risks of the non-success of empirical treatment (EMPT) attributed to EPAs and non-PAs were generally high (55.67% and 65.463%, respectively) due to the antimicrobial resistance of the isolates. MARI-based estimated success and non-success of EMPT if EUCAST-prescribed antimicrobials were administered for the treatment of listeriosis were 44.329% and 55.67%, respectively. The EMPT if non-prescribed antimicrobials were administered for the treatment of listeriosis was 34.53% and 65.46%, respectively. This indicates a potentially high risk with PAs and non-PAs for the treatment of L. monocytogenes infection. Furthermore, ARI scores ≤ 0.2 for EPAs were observed in polony, potato chips, muffins, and assorted sandwiches, whereas ARI scores for non-PAs were >0.2 across all the RTE food types. The ARI-based estimate identified potential risks associated with some RTE foods, including fried fish, red Vienna sausage, Russian sausage, fruit salad, bread, meat pies, fried chicken, cupcakes, and vetkoek. This investigation identified a high risk of EMPT due to the presence of antimicrobial-resistant L. monocytogenes in RTE foods, which could result in severe health consequences.

Explainable ensemble learning model improves identification of candidates for oral cancer screening.

OBJECTIVES: Artificial intelligence could enhance the use of disparate risk factors (crude method) for better stratification of patients to be screened for oral cancer. This study aims to construct a meta-classifier that considers diverse risk factors to identify patients at risk of oral cancer and other suspicious oral diseases for targeted screening. MATERIALS AND METHODS: A retrospective dataset from a community oral cancer screening program was used to construct and train the novel voting meta-classifier. Comprehensive risk factor information from this dataset was used as input features for eleven supervised learning algorithms which served as base learners and provided predicted probabilities that are weighted and aggregated by the meta-classifier. Training dataset was augmented using SMOTE-ENN. Additionally, Shapley additive explanations (SHAP) values were generated to implement the explainability of the model and display the important risk factors. RESULTS: Our meta-classifier had an internal validation recall, specificity, and AUROC of 0.83, 0.86, and 0.85 for identifying the risk of oral cancer and 0.92, 0.60, and 0.76 for identifying suspicious oral mucosal disease respectively. Upon external validation, the meta-classifier had a significantly higher AUROC than the crude/current method used for identifying the risk of oral cancer (0.78 vs 0.46; p = 0.001) Also, the meta-classifier had better recall than the crude method for predicting the risk of suspicious oral mucosal diseases (0.78 vs 0.47). CONCLUSION: Overall, these findings showcase that our approach optimizes the use of risk factors in identifying patients for oral screening which suggests potential clinical application.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma.

BACKGROUND/AIM: Machine learning (ML) models are often modelled to predict cancer prognosis but rarely consider spatial factors in a region. Hence this study explored machine learning algorithms utilising Local Government Areas (LGAs) in Queensland, Australia to spatially predict 3- and 5-year prognosis of oral cancer patients and provide clinical interpretability of the predicted outcome made by the ML model. PATIENTS AND METHODS: Data from a total of 3,841 oral cancer patients were retrieved from the Queensland Cancer Registry (QCR). Synthesizing minority oversampling technique together with edited nearest neighbours (SMOTE-ENN) was used to pre-process unbalanced datasets. Five ML models: logistic regression, random forest classifier, XGBoost, Gaussian Naïve Bayes and Voting Classifier were trained. Predictive features were age, sex, LGAs, tumour site and differentiation. Outcomes were 3- and 5-year overall survival of patients. Model performances on test set were evaluated using area under the curve and F1 scores. SHapley Additive exPlanations (SHAP) method was applied to the best performing model for model interpretation of the predicted outcome. RESULTS: The Voting Classifier was the best performing model with F1 score of 0.58 and 0.64 for 3- and 5-year overall survival, respectively. Age was the most important feature in the Voting Classifier in 3- and 5-year prognosis prediction. LGAs at diagnosis was the top 3 predictive feature for both 3- and 5-year models. CONCLUSION: The Voting Classifier demonstrated the best overall performance in classifying both 3- and 5-year overall survival of oral cancer patients in Queensland. SHAP method provided clinical understanding of the predictive features of the Voting Classifier.

Assessment of the molecular epidemiology and genetic multiplicity of Listeria monocytogenes recovered from ready-to-eat foods following the South African listeriosis outbreak.

Following the recent listeriosis outbreak in South Africa, this study was carried out to assess the safety level of various common ready-to-eat foods (RTE) obtained from supermarkets and grocery stores in major towns and cities within the Amathole, Chris Hani and Sarah Baartman Districts Municipalities, Eastern Cape Province, South Africa. A sum of 239 food samples was collected from these locations, and Listeria monocytogenes (Lm) was isolated in line with the recommended techniques by the International Organization for Standardization EN ISO 11290:2017 parts 1 and 2. Identification of the pathogen and detection of various associated virulence genes was done using Polymerase Chain Reaction (PCR) techniques. From the RTE food samples processed, Lm was detected in 107 (44.77%) of the samples. Russian sausage was the most contaminated (78.57%), followed by sliced polony (61.90%), muffins (58.33%), polony (52.63%), and pies (52.38%), while all vetkoek samples examined were negative for Lm. Although the prevalence of Lm in the food samples was very high, concentrations were generally < 100 CFU/g. Strains of Lm recovered from the RTE foods were predominantly epidemiological strains belonging to serotypes 1/2a, 1/2b and 4b. The prevalence of 10 virulence genes including the inlA, InlC, inlJ, plcA, hlyA, plcB, prfA, mpl, inlB, and actA were detected among Lm isolates. Most of the isolates (69.07%) demonstrated the potential for biofilm formation and were categorized as weak (14.95%), moderate (13.40%) and strong (40.72) biofilm formers. Furthermore, molecular typing revealed high levels of genetic diversity among Lm isolates. The findings of this investigation suggested that the presence of Lm in the RTE foods may constitute potential threats to the food sector and could pose public health hazards to consumers, particularly the high-risk group of the population. We, therefore, recommend that adequate food monitoring for safety and proper regulation enforcement in the food sector must be ensured to avoid any future listeriosis outbreak that could be linked to RTE foods in South Africa.

Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis.

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring.

Antibiotic Resistance Profile of Listeria monocytogenes Recovered from Ready-to-Eat Foods Surveyed in South Africa.

ABSTRACT: In recent decades, there has been an increase in the reports of antimicrobial resistance of Listeria monocytogenes, which constitutes a serious threat to the therapeutic management of listeriosis infection. Our study profiled the antibiogram fingerprint of L. monocytogenes isolates (n = 194) recovered from common South African ready-to-eat foods. L. monocytogenes isolates recovered from foods were tested against a panel of 22 antibiotics using the disk diffusion method. Antimicrobial resistance (>50%) against ceftriaxone (53.1%), trimethoprim (56.2%), streptomycin, cefotetan (59.3%), sulfamethoxazole (61.9%), vancomycin, and oxytetracyclines (62.9%) were observed. Thirty of the isolates (15.5%) were resistant against only one or two antibiotics, whereas 162 (83.5%) exhibited phenotypic multiple antibiotic resistance. Only two (1%) of the isolates did not exhibit phenotypic resistance against any antibiotics screened. Multiple antibiotic phenotypes revealed high resistance patterns, and the multiple antibiotic indices were greater than the Krumperman permissible (>0.2) benchmark. Of the 44 genes screened, 22 antimicrobial resistance genes were detected among ready-to-eat food isolates, including resistance determinants that encode sulfonamides (n = 125, 64.4%), ß-lactams (n = 86, 44.3%), phenicols (n = 25, 12.9), and aminoglycosides (n = 93, 47.9%) resistance. We conclude that the presence of resistant L. monocytogenes isolates harboring corresponding antimicrobial resistance genes in foods could compromise safety and constitute severe health consequences if consumed.

Assessment of multidrug-resistant Listeria monocytogenes in milk and milk product and One Health perspective.

The occurrence and the antibiogram signatures of Listeria monocytogenes (Lm) recovered from 65 milk samples and its products within the Eastern Cape province were examined. The EN ISO 11290:2017 procedures Parts 1 and 2 described by the International Organization for Standardization for the enumeration and isolation of Lm was adopted for the study. Lm was detected in 18.46% of all the samples examined, and the strains recovered from the samples belong to serotypes 4b and 1/2b. The virulence determinants including prfA, plcA, plcB, inlA, inlC, hly, mpl, actA, inlJ and inlB were detected in all the isolates. About 95.24% of the studied Lm isolates demonstrated potential capacity for biofilm formation. The antibiogram profile revealed high resistance against sulfamethoxazole (71.43%), trimethoprim (52.86%); erythromycin, cefotetan and oxytetracycline (42.86% respectively). About 85.71% exhibited multiple antibiotic resistance phenotypes against the test antibiotics. The resistance determinants encoding resistance against the ß-lactamase antibiotics [such as the blaTEM, blaSHV, blaTEM variants (TEM-1 and TEM-2) and the blaZ], the tetracycline resistance genes (including tetA, tetD, tetG and tetM and tetK) were detected among resistant isolates. In addition, the aminoglycoside resistance gene aph (3)-IIa (aphA2)a was detected only in one isolate. Finally, the sulfonamide resistance genes including the sul2 and the sul1 genes were the most frequently observed among Lm isolates. Generally, 71.43% of all Lm isolates recovered from the samples investigated harboured one or more resistance genes encoding resistance against various antibiotics. The antibiogram signatures of Lm isolates observed in this study is an indication that empirical treatment of listeriosis may be challenging in the future as the pathogen may obliterate the success of antibiotics. We, therefore, advocate for the recognition of the One Health approach to ensuring food safety and curbing the spread of antimicrobial resistance in food.

Performance of a simplified scoring system for risk stratification in oral cancer and oral potentially malignant disorders screening.

BACKGROUND: Impact and efficiency of oral cancer and oral potentially malignant disorders screening are most realized in "at-risk" individuals. However, tools that can provide essential knowledge on individuals' risks are not applied in risk-based screening. This study aims to optimize a simplified risk scoring system for risk stratification in organized oral cancer and oral potentially malignant disorders screening. METHODS: Participants were invited to attend a community-based oral cancer and oral potentially malignant disorders screening program in Hong Kong. Visual oral examination was performed for all attendees and information on sociodemographic characteristics as well as habitual, lifestyle, familial, and comorbidity risk factors were obtained. Individuals' status of those found to have suspicious lesions following biopsy and histopathology were classified as positive/negative and this outcome was used in a multiple logistic regression analysis with variables collected during screening. Odds ratio weightings were then used to develop a simplified risk scoring system which was validated in an external cohort. RESULTS: Of 979 participants, 4.5% had positive status following confirmatory diagnosis. A 12-variable simplified risk scoring system with weightings was generated with an AUC, sensitivity, and specificity of 0.82, 0.71, and 0.78 for delineating high-risk cases. Further optimization on the validation cohort of 491 participants yielded a sensitivity and specificity of 0.75 and 0.87 respectively. CONCLUSIONS: The simplified risk scoring system was able to stratify oral cancer and oral potentially malignant disorders risk with satisfactory sensitivity and specificity and can be applied in risk-based disease screening.

Incidence and genetic diversity of multi-drug resistant Listeria monocytogenes isolates recovered from fruits and vegetables in the Eastern Cape Province, South Africa.

We investigated the prevalence, genetic diversity and antibiogram profiles of Listeria monocytogenes (Lm) recovered from fruits and vegetables sourced from three District Municipalities in the Eastern Cape Province, South Africa after the recent listeriosis outbreak in the country. The procedure outlined by the International Organization for Standardization EN ISO 11290:2017 Parts 1 and 2 was adopted for the isolation of Lm from 140 vegetable samples. Molecular detection of the pathogen and the presence of 10 virulence-associated markers were assessed. Lm was detected in 42.86% of all the vegetable samples tested. Highest prevalence was recorded in tomato (65.52%) followed by spinach (56.67%), cabbage (38.10%), apple (36.84%), mushroom (29.41%) and carrot (10%). The virulence determinants including the inlA, inlC, prfA and plcA, hly, plcB genes were detected in all Lm isolates whereas, inlJ (88.35%), inlB (86.41%), mpl (92.23%) and actA (84.55%) respectively. High susceptibility (> 50) was observed to all antibiotics tested except for sulfamethoxazole (17.48%), streptomycin (38.84%), amoxicillin (41.75%) and erythromycin (43.69%). However, high resistance against sulfamethoxazole (80.58%), amoxicillin (58.25%) and erythromycin (49.52%) were observed. About 85.44% of Lm isolates showed multidrug-resistance phenotypes against the test antibiotics. Furthermore, twenty (20) resistance genes encoding tetracyclines, sulphonamides, phenicols, aminoglycosides, ß-lactamases, and variants of the extended-spectrum of ß-lactamases (ESBLs) resistance were detected among the Lm isolates. The sul2 (90.81), tetM (68.42%) sul1 (45.98%) were more prevalent among the resistant strains. The dendrogram signatures generating seven clades is an indication of the high genetic diversity among the isolates. We conclude that the presence of Lm in fruits and vegetables is a potential threat to the consumers and a potential public health hazard, particularly to the high-risk group of the population.

Oral Cancer Awareness and Individuals' Inclination to Its Screening and Risk Prediction in Hong Kong.

Assessing the baseline knowledge status and expectations of the target population of any health promotion and secondary prevention program is essential to the success of such intervention. To obtain this information about the Hong Kong population a priori to implementing these preventive strategies for oral cancer in addition to determining the willingness of potential screening participants to take risk-profiling assessments, a cross-sectional survey was conducted between November 2019 and March 2020. A total of 964 residents between the ages 18 and 86 years were invited to participate in this study across the three geographical areas in Hong Kong. Most participants self-reported being aware of oral cancer (86.3%), although the proportion of those with substantial knowledge on salient risk factors and early identifiable signs were very low (2.9%). Age and level of education were the only demographic characteristics associated with the knowledge status. The proportion of participants willing to attend community screening and partake in risk profiling assessment was high (83.9% and 80.9% respectively). Willingness to attend community screening was directly associated with respondents' self-reported oral cancer awareness status (OR: 1.9, 95% CI: 1.22-2.96). Also, we observed that those participants who were willing to attend screening are more inclined to take risk prediction assessments that those not willing to attend. These findings have showcased the need to intensify health promotion via personal skills development to encourage early disease presentation and will assist in the planning of these programs accordingly in the Hong Kong population.