Effects of dietary powdered Ficus deltoidea on the growth and health performance of African catfish, Clarias gariepinus production.

Intensive aquaculture causes a decline in the health status of fish, resulting in an increased disease incidence. To counteract this, feed additives have been utilized to improve the growth performance and health of aquaculture species. This work specifically investigates the impact of powdered Ficus deltoidea (FD) on various parameters related to growth, blood parameters, liver and intestine morphology, body proximate analysis, digestive enzymes, antioxidant capacity, and disease resistance to motile Aeromonad Septicemia (MAS) caused by Aeromonas hydrophila infection in African catfish, Clarias gariepinus. Four formulated diets were prepared: T1 (0% FD), T2 (0.5% FD), T3 (0.75% FD), and T4 (1% FD). After 8 weeks, the African catfish's growth performance fed with the T2 diet exhibited a substantial improvement (p < 0.05), along with a remarkably lower (p < 0.05) feed conversion ratio (FCR) when compared to the other treatment groups. Blood parameter analysis revealed notably higher (p < 0.05) levels of white blood cell (WBC), lymphocytosis (LYM), hemoglobin (HGB), albumin (ALB), globulin (GLOB), as well as total protein (TP) in the T2 diet group. While all treatment groups displayed normal intestinal morphology, liver deterioration was observed in groups supplemented with higher FD. The T2 diet group recorded the highest villus length, width, and crypt depth. Protease and lipase levels were also notably improved in the T2 diet group compared to other treatment groups. Additionally, catalase (CAT), glutathione peroxidase (GPx), and superoxide dismutase (SOD) were remarkably elevated in all FD diet groups than in the control group. The expression of immune-related genes, including transforming growth factor beta 1, heat shock protein 90, nuclear factor kappa-B gene, and lysozyme G, was upregulated in all treatments. Overall, the results of this study indicate that incorporating dietary FD at 0.5% concentration in the diet of African catfish may enhance their productivity in intensive farming.

Analysis of child development facts and myths using text mining techniques and classification models.

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 µs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Exploring the impact of word prediction assistive features on smartphone keyboards for blind users.

Assistive technologies have been developed to enhance blind users' typing performance, focusing on speed, accuracy, and effort reduction. One such technology is word prediction software, designed to minimize keystrokes required for text input. This study investigates the impact of word prediction on typing performance among blind users using an on-screen QWERTY keyboard. We conducted a comparative study involving eleven blind participants, evaluating both standard QWERTY input and word prediction-assisted typing. Our findings reveal that while word prediction slightly improves typing speed, it does not enhance typing accuracy and increases both physical and temporal workload compared to the default keyboard. We conclude with recommendations for improving word prediction systems, including more efficient editing methods and the integration of voice pitch variations to aid error recognition.

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

Exploring beneficial effects of phytobiotics in marine shrimp farming: A review.

Marine shrimp farming, mainly Penaeus monodon and Litopenaeus vannamei, is an important component of the aquaculture industry. Marine shrimp farming helps produce a protein source for humans, provides job opportunities, and generates lucrative profits for investors. Intensification farming practices can lead to poor water quality, stress, and malnutrition among the farmed marine shrimp, resulting in disease outbreaks and poor production, impeding the development of marine shrimp farming. Antibiotics are the common short-term solution to treat diseases in marine shrimp farming. Moreover, the negative impacts of using antibiotics on public health and the environment erode consumer confidence in aquaculture products. Recently, research on using phytobiotics as a prophylactic agent in aquaculture has become a hot topic. Various phytobiotics have been explored to reveal their beneficial effects on aquaculture species. In this review paper, the sources and modes of action of phytobiotics are presented. The roles of phytobiotics in improving growth performance, increasing antioxidant capacity, enhancing the immune system, stimulating disease resistance, and mitigating stress due to abiotic factors in marine shrimp culture are recapitulated and discussed.

Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter.

The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named 'X'). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.

Examining characteristics of those who receive pedorthic services: A clinical audit.

Diabetes-related foot complications, including neuropathic plantar forefoot ulcers, are a significant contributor to morbidity and increased healthcare costs. This retrospective clinical audit examines the characteristics of people accessing pedorthics services who are at risk of neuropathic plantar forefoot ulcer (re)occurrence and the pathways and funding models used to access these services. A clinical record audit was conducted on all patients accessing a pedorthics service who had diabetes and neuropathy with a history of plantar forefoot ulceration. The data included demographics, diabetes and neuropathy duration, main forefoot pathology and other comorbidity, footwear and insole interventions, and health fund access status. A total of 70 patient records were accessed, and relevant data was extracted. The mean age of participants was 64.69 (standard deviation (SD) 11.78) years; 61% were male and 39% female. Duration of diabetes ranged from one to 35 years, with a mean of 14.09 years (SD 6.58). The mean duration of neuropathy was 8.56 (SD 4.16) years. The most common forefoot conditions were bony prominences at 71% (n = 50), rigid flat foot and limited joint mobility (53%, n = 37), and hallux abductovalgus at 47% (n = 33). All participants had hyperkeratosis; 34% (n = 24) had forefoot amputation, and around 34% (n = 24) had a history of digital amputation. Various publicly funded packages and private health insurance were accessed. This study investigates the sociodemographic and medical profiles of individuals with diabetes-related foot complexities prone to neuropathic plantar forefoot ulcers. It is the first to examine patients receiving pedorthic services, informing practitioner surveys and preventive care strategies. Understanding patient characteristics aids in optimising multidisciplinary care and reducing ulcer incidence. Further studies are warranted to explore the field to establish an effective multidisciplinary care approach between medical professionals, podiatrists and pedorthists to optimise patient outcomes.

Screening of CIMMYT and South Asian Bread Wheat Germplasm Reveals Marker-Trait Associations for Seedling Resistance to Septoria Nodorum Blotch.

Wheat (Triticum aestivum L.) production is adversely impacted by Septoria nodorum blotch (SNB), a fungal disease caused by Parastagonospora nodorum. Wheat breeders are constantly up against this biotic challenge as they try to create resistant cultivars. The genome-wide association study (GWAS) has become an efficient tool for identifying molecular markers linked with SNB resistance. This technique is used to acquire an understanding of the genetic basis of resistance and to facilitate marker-assisted selection. In the current study, a total of 174 bread wheat accessions from South Asia and CIMMYT were assessed for SNB reactions at the seedling stage in three greenhouse experiments at CIMMYT, Mexico. The results indicated that 129 genotypes were resistant to SNB, 39 were moderately resistant, and only 6 were moderately susceptible. The Genotyping Illumina Infinium 15K Bead Chip was used, and 11,184 SNP markers were utilized to identify marker-trait associations (MTAs) after filtering. Multiple tests confirmed the existence of significant MTAs on chromosomes 5B, 5A, and 3D, and the ones at Tsn1 on 5B were the most stable and conferred the highest phenotypic variation. The resistant genotypes identified in this study could be cultivated in South Asian countries as a preventative measure against the spread of SNB. This work also identified molecular markers of SNB resistance that could be used in future wheat breeding projects.

A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.

Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both [Formula: see text] and [Formula: see text] datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals.

Investigating the emigration intention of health care workers: A cross-sectional study.

AIMS: To (1) explore the intramigration experience of HCWs within Nigeria, (2) explore the migration intention of health care workers (HCWs) in Nigeria and (3) identify the predictors of migration intention among HCWs in Nigeria. DESIGN: Cross-sectional study. METHODS: The online survey was used to collect data from 513 HCWs in Nigeria between May and June 2023. Crude and adjusted logistic regression were used to identify factors associated with emigration intention. Analyses were performed on SPSS version 26 at a 95% confidence interval. RESULTS: The study found that 34.4% had intramigration experience, and the rate of intention to emigrate to work in another country was 80.1%. The United Kingdom was the most preferred destination (109 HCWs), followed by Canada (92 HCWs) and the United States (82 HCWs). At the multivariate level, emigration intention was associated with the experience of burnout and duration of practice as a HCW. Nurses had higher emigration intentions than medical doctors. CONCLUSIONS: Many HCWs in Nigeria appear to have emigration intent, and nurses are more likely to be willing to migrate than doctors. The Nigerian government may want to explore strategies to reverse the emigration intent of the HCWs in Nigeria.

Applying a Random Encounter Model to Estimate the Asiatic Black Bear (Ursus thibetanus) Density from Camera Traps in the Hindu Raj Mountains, Pakistan.

Estimating the population density of vulnerable species, such as the elusive and nocturnal Asiatic black bear (Ursus thibetanus), is essential for wildlife conservation and management. We used camera traps and a Random Encounter Model (REM) to estimate the population density of U. thibetanus during the autumn and winter seasons in the Hindu Raj Mountains. We installed 23 camera traps from October to December 2020 and acquired 66 independent pictures of Asiatic black bears over 428 trap nights. Our results showed that the bears preferred lowland areas with the presence of Quercus spp. We estimated, using the REM, a population density of U. thibetanus of 1.875 (standard error = 0.185) per square kilometer, which is significantly higher than that in other habitats. Our results showed that during autumn and winter, the bear population density tends to concentrate at lower elevations. Forest cover showed a positive correlation with the rates of bear encounters unlike the Euclidean distance to human settlements, altitude, and aspect variables. The approaches used here are cost-effective for estimating the population density of rare and vulnerable species such as U. thibetanus, and can be used to estimate their population density in Pakistan. Population density estimation can identify areas where the bears live and human-bear conflicts occurred and use this information in future wildlife management plans.

An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides.

Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.

The Potential of Fish Protein Hydrolysate Supplementation in Nile Tilapia Diets: Effects on Growth and Health Performance, Disease Resistance, and Farm Economic Analysis.

Fish protein hydrolysate (FPH) has shown immense potential as a dietary protein supplement and immunostimulant in aquaculture, especially in Nile tilapia production. Four isoproteic diets (30% crude protein) were prepared by including FPH at varying percentages (0%, 0.5%, 1%, and 2%). Nile tilapia fed with FPH diets for 90 days, and their growth performance, feed utilization, blood biochemistry, liver and gut morphology, and resistance against Streptococcus iniae were investigated. The findings revealed that diets physical attributes such as pellet durability index and water stability were remarkably (p < 0.05) varied between experimental diet groups. Furthermore, the test diets were more palatable when FPH was included at 1% and 2%. Fish that were fed with a 2% FPH-treated diet had significantly (p < 0.05) greater growth indices than other treatments. Additionally, their feed utilization was significantly (p < 0.05) improved. The experimental diets and intestinal total bacteria count (TBC) exhibited a rising trend with FPH levels, where the 2% FPH-treated diet recorded the highest TBC. Neutrophil (109/L), lymphocyte (109/L), eosinophil (109/L), and red blood cell(1012/L) counts were significantly (p < 0.05) higher in the 2% FPH-treated group, while the white blood cell (109/L), and basophil (109/L) counts were not influenced by the FPH inclusion. Moreover, the FPH-treated groups displayed lower creatinine, bilirubin, and urea levels than the control. The histological examination demonstrated that themid-intestine of 2% FPH-fed Nile tilapia had an unbroken epithelial wall, more villi with frequent distribution of goblet cells, wider tunica muscularis, and stronger stratum compactum bonding than other treatments. Additionally, this group exhibited more nuclei and erythrocytes and less vacuolar cytoplasm in liver than their counterparts. Nile tilapia that were given a diet containing 2% FPH had significantly (p < 0.05) higher resistance (83.33%) to S. iniae during the bacterial challenge test. A significant (p < 0.05) enhancement in farm economic efficiency was observed in the higher inclusion of FPH in diets. In summary, 2% FPH supplementation in Nile tilapia diets improved their growth performance, feed utilization, health status, disease resistance, and farm economic efficiency.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides.

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.

Comparative analysis of biometrical and reproductive indices, proximate composition, and hemato-biochemical variables of cuchia eel Monopterus cuchia (Hamilton, 1822) from six different localities of Bangladesh.

Cuchia eel (Monopterus cuchia) is among the most sought-after freshwater fish, owing to its exceptional nutritional profile and high consumer demand. The current research aimed to establish baseline data by comparing the proximate composition, hematological, and plasma biochemical indices of Cuchia eel populations across six different geographical locations in Bangladesh: Bogra, Haluaghat, Jamalpur, Moktagacha, Sylhet, and Tangail. By examining these parameters, we aim to gain valuable insights into the nutritional benefits, physiological responses, and potential adaptations of this species to varying environments. The statistical analysis revealed no significant (P > 0.05) variances in the whole-body proximate composition of the fish captured from distinct areas. However, it was observed that different geographical regions had remarkable impacts on the variations of the majority of the hematological parameters, except for some cases. Additionally, there was a notable (P < 0.05) increase or decrease in most of the serum biochemical contents in certain localities as compared to others in this study. Light microscopic examination of Cuchia eel blood smears exhibited lower numbers but larger sizes of RBCs. The findings of this study lead to the conclusion that different localities had significant impacts on the hematology and blood biochemical indices of Cuchia eel, even though the whole-body proximate composition showed no significant variations. This research contributes to a deeper understanding of the physiological aspects of Cuchia eel.

Assessment of dietary polyvinylchloride, polypropylene and polyethylene terephthalate exposure in Nile tilapia, Oreochromis niloticus: Bioaccumulation, and effects on behaviour, growth, hematology and histology.

Microplastics (MPs) have been recognized as emerging aquatic pollutants receiving major concern due to their detrimental effects on aquatic life. Nile Tilapia, Oreochromis niloticus is a model species considered in toxicological studies to address the effects of pollutants in freshwater animals. However, comprehensive knowledge comparing the impacts on fish across various MPs polymers is scarce. Therefore, the overarching aim of the current study was to examine the bioconcentration of MPs polymers: polyvinylchloride (PVC), polypropylene (PP), and polyethylene terephthalate (PET), and their toxic effects on growth, and behavioral responses, hematology, and histology of gills, liver, and intestine in O. niloticus. Fishes were subjected to a 21-day dietary exposure to MPs by assigning them into six treatment groups: T1 (4% of PVC), T2 (4% of PP), T3 (4% of PET), T4 (8% of PVC), T5 (8% of PP), T6 (8% of PET), and control (0% of MPs), to assess the effects on fish across the polymers and dosage. Results showed several abnormalities in anatomical and behavioral parameters, lower growth, and high mortality in MPs-exposed fish, indicating a dose-dependent relationship. The elevated dosage of polymers raised the bioavailability of PVC, PP, and PET in gills and gut tissues. Noteworthy erythrocyte degeneration referred to cytotoxicity and stress imposed by MPs, whereas the alterations in hematological parameters were possibly due to blood cell damage, also indicating mechanisms of defense against MPs toxicity. Histopathological changes in the gills, liver, and intestine confirmed the degree of toxicity and associated dysfunctions in fish. A higher sensitivity of O. niloticus to PET-MPs compared to other polymers is likely due to its chemical properties and species-specific morphological and physiological characteristics. Overall, the present study reveals valuable insights into the emerging threat of MPs toxicity in freshwater species, which could be supportive of future toxicological research.

Resveratrol impacts on aquatic animals: a review.

Aquaculture has intensified tremendously with the increasing demand for protein sources as the global population grows. However, this industry is plagued with major challenges such as poor growth performance, the lack of a proper environment, and immune system impairment, thus creating stress for the aquaculture species and risking disease outbreaks. Currently, prophylactics such as antibiotics, vaccines, prebiotics, probiotics, and phytobiotics are utilized to minimize the negative impacts of high-density farming. One of the promising prophylactic agents incorporated in fish feed is resveratrol, a commercial phytophenol derived via the methanol extraction method. Recent studies have revealed many beneficial effects of resveratrol in aquatic animals. Therefore, this review discusses and summarizes the roles of resveratrol in improving growth performance, flesh quality, immune system, antioxidant capacity, disease resistance, stress mitigation, and potential combination with other prophylactic agents for aquatic animals.

The Impact of Social and Broadcast Media on Public Health Initiatives: Study of the COVID-19 Infodemics.

The COVID-19 outbreak, declared a pandemic in March 2020, lacked specific treatments until vaccine development. Medication misinformation via media caused panic, self-prescription, and drug resistance. False propaganda led to shortages. This study analyzes Google Trends for hydroxychloroquine (HCQ), azithromycin, and BCG vaccine searches across six countries. US, Brazil, and India showed interest in HCQ, while Taiwan, Japan, and South Korea focused on BCG vaccine. This article aims to raise awareness of adverse drug reactions, cautioning against self-prescription, political assumptions, and social media during future emergencies.