From Gut Microbiomes to Infectious Pathogens: Neurological Disease Game Changers.

Gut microbiota and infectious diseases affect neurological disorders, brain development, and function. Compounds generated in the gastrointestinal system by gut microbiota and infectious pathogens may mediate gut-brain interactions, which may circulate throughout the body and spread to numerous organs, including the brain. Studies shown that gut bacteria and disease-causing organisms may pass molecular signals to the brain, affecting neurological function, neurodevelopment, and neurodegenerative diseases. This article discusses microorganism-producing metabolites with neuromodulator activity, signaling routes from microbial flora to the brain, and the potential direct effects of gut bacteria and infectious pathogens on brain cells. The review also considered the neurological aspects of infectious diseases. The infectious diseases affecting neurological functions and the disease modifications have been discussed thoroughly. Recent discoveries and unique insights in this perspective need further validation. Research on the complex molecular interactions between gut bacteria, infectious pathogens, and the CNS provides valuable insights into the pathogenesis of neurodegenerative, behavioral, and psychiatric illnesses. This study may provide insights into advanced drug discovery processes for neurological disorders by considering the influence of microbial communities inside the human body.

A microbiome-directed therapeutic food for children recovering from severe acute malnutrition.

Severe acute malnutrition (SAM), defined anthropometrically as a weight-for-length z-score more than 3 standard deviations below the mean (WLZ<-3), affects 19 million children under 5-years-old worldwide. Complete anthropometric recovery after standard inventions is rare with children often left with moderate acute malnutrition (MAM; WLZ -2 to -3). Here we conduct a randomized controlled trial (RCT), involving 12-18-month-old Bangladeshi children from urban and rural sites, who after hospital-based treatment for SAM received a 3-month intervention with a microbiota-directed complementary food (MDCF-2) or a ready-to-use supplementary food (RUSF) as they transitioned to MAM. The rate of WLZ improvement was significantly greater with MDCF-2 than the more calorically-dense RUSF, as we observed in a previous RCT of Bangladeshi children with MAM without antecedent SAM. A correlated meta-analysis of aptamer-based measurements of 4,520 plasma proteins in this and the prior RCT revealed 215 proteins positively-associated with WLZ (prominently those involved in musculoskeletal and CNS development) and 44 negatively-associated proteins (related to immune activation), with a significant enrichment in levels of the positively WLZ-associated proteins in the MDCF-2 arm. Characterizing changes in 754 bacterial metagenome-assembled genomes in serially collected fecal samples disclosed the effects of acute rehabilitation for SAM on the microbiome, its transition as each child achieves a state of MAM, and how specific strains of Prevotella copri function at the intersection between MDCF-2 glycan metabolism and the rescue of growth faltering. These results provide a rationale for further testing the generalizability of the efficacy of MDCF and identify biomarkers for defining treatment responses.

Mechanical and biological behavior of double network hydrogels reinforced with alginate versus gellan gum.

Alginate and gellan gum have both been used by researchers as reinforcing networks to create tough and biocompatible polyethylene glycol (PEG) based double network (DN) hydrogels; however, the relative advantages and disadvantages of each approach are not understood. This study directly compares the mechanical and biological properties of polyethylene glycol di-methacrylate (PEGDMA) hybrid DN hydrogels reinforced with either gellan gum or sodium alginate using PEGDMA concentrations from 10 to 20 wt% and reinforcing network concentrations of 1 and 2 wt%. The findings demonstrate that gellan gum reinforcement is more effective at increasing the strength, stiffness, and toughness of PEGDMA DN hydrogels. In contrast, alginate reinforcement yields DN hydrogels with greater stretchability compared to gellan gum reinforced PEGDMA. Furthermore, separate measurements of toughness via unnotched work of rupture testing and notched fracture toughness testing showed a strong correlation of these two properties for a single reinforcing network type, but not across the two types of reinforcing networks. This suggests that additional notched fracture toughness experiments are important for understanding the full mechanical response when comparing different tough DN hydrogel systems. Regarding the biological response, after conjugation of matrix protein to the surface of both materials robust cell attachment and spreading was supported with higher yes associated protein (YAP) nuclear expression observed in populations adhering to the stiffer gellan gum-PEGDMA material. This study provides valuable insights regarding how to design double network hydrogels for specific property requirements, e.g., for use in biomedical devices, as scaffolding for tissue engineering, or in soft robotic applications.

Altered serum interleukin-17A and interleukin-23A levels may be associated with the pathophysiology and development of generalized anxiety disorder.

In recent times, the pathogenesis of generalized anxiety disorder (GAD) and the influence of pro- and anti-inflammatory cytokines on it have garnered considerable interest. Cytokine research, especially Th-17 cytokine research on GAD patients, is limited. Here, we aim to assess the role of interleukin-17A (IL-17A) and interleukin-23A (IL-23A) in the pathophysiology and development of GAD. This investigation included 50 GAD patients and 38 age-sex-matched healthy controls (HCs). A psychiatrist diagnosed patients with GAD and assessed symptom severity using the DSM-5 and the GAD-7 scales. The serum concentrations of IL-17A and IL-23A were determined using commercially available ELISA kits. GAD patients exhibited elevated levels of IL-17A (77.14 ± 58.30 pg/ml) and IL-23A (644.90 ± 296.70 pg/ml) compared to HCs (43.50 ± 25.54 pg/ml and 334.40 ± 176.0 pg/ml). We observed a positive correlation between disease severity and cytokine changes (IL-23A: r = 0.359, p = 0.039; IL-17A: r = 0.397, p = 0.032). These findings indicate that IL-17A and IL-23A may be associated with the pathophysiology of GAD. ROC analysis revealed moderately higher AUC values (IL-23A: 0.824 and IL-17A: 0.710), demonstrating their potential to discriminate between patients and HCs. Also, the sensitivity values of both cytokines were relatively higher (IL-23A: 80.49% and IL-17A: 77.27%). According to the present findings, there may be an association between peripheral serum levels of IL-17A and IL-23A and the pathophysiology and development of GAD. These altered serum IL-17A and IL-23A levels may play a role in directing the early risk of developing GAD. We recommend further research to ascertain their exact role in the pathophysiology and their performance as risk assessment markers of GAD.

Synthesis, Antimicrobial, Molecular Docking Against Bacterial and Fungal Proteins and In Silico Studies of Glucopyranoside Derivatives as Potent Antimicrobial Agents.

Carbohydrate derivatives play a crucial role in biochemical and medicinal research. Therefore, the present study was designed to explore the synthesis of methyl α-D-glucopyranoside derivatives (1, MDG), focusing on their efficacy against bacterial and fungal infections. The structure of the synthesized compounds was ascertained using spectral and elemental analyses. Antimicrobial screening revealed strong antifungal properties and exhibited MIC values of 16-32 µg/L and MBC 64-128 µg/L. Structure-activity relationship (SAR) analysis indicated that adding nonanoyl and decanoyl groups to ribose moiety enhanced potency against both bacterial and fungal strains. Compounds 6 and 7, presented nonanoyl and decanoyl substituents and demonstrated greater efficacy. In addition, DFT studies identified compound 8 as possessing ideal electronic properties. Molecular docking revealed that compound 8 exhibits exceptional binding affinities to bacterial proteins, conferring potent antibacterial and antifungal activities. In addition, pharmacokinetic optimization via POM analysis highlighted compounds 1 and 2 as promising bioavailable drugs with minimal toxicity. Molecular dynamics simulations confirmed the stability of the 2-S. aureus complex, revealing the therapeutic potential of compounds 2 and 8. The integration of in vitro and in silico methods, including DFT anchoring dynamics and molecular dynamics simulations, provides a solid framework for the advancement of effective anti-infective drugs.

Paramagnetic Properties of [AnIV(NO3)6]2- Complexes (An = U, Np, Pu) Probed by NMR Spectroscopy and Quantum Chemical Calculations.

Actinide +IV complexes with six nitrates [AnIV(NO3)6]2- (An = Th, U, Np, and Pu) have been studied by 15N and 17O NMR spectroscopy in solution and first-principles calculations. Magnetic susceptibilities were evaluated experimentally using the Evans method and are in good agreement with the ab initio values. The evolution in the series of the crystal field parameters deduced from ab initio calculations is discussed. The NMR paramagnetic shifts are analyzed based on ab initio calculations. Because the cubic symmetry of the complex quenches the dipolar contribution, they are only of Fermi contact origin. They are evaluated from first-principles based on a complete active space/density functional theory (DFT) strategy, in good accordance with the experimental one. The ligand hyperfine coupling constants are deduced from paramagnetic shifts and calculated using unrestricted DFT. The latter are decomposed in terms of the contribution of molecular orbitals. It highlights two pathways for the delocalization of the spin density from the metallic open-shell 5f orbitals to the NMR active nuclei, either through the valence 5f hybridized with 6d to the valence 2p molecular orbitals of the ligands, or by spin polarization of the metallic 6p orbitals which interact with the 2s-based molecular orbitals of the ligands.

Efficiency and Mechanism of Catalytic Siloxane Exchange in Vitrimer Polymers: Modeling and Density Functional Theory Investigations.

Of late, siloxane-containing vitrimers have gained significant interest due to their fast dynamic characteristics over a reasonable temperature range (180-220 °C), making them well-suited for diverse applications. The exchange reaction pathway in the siloxane vitrimers is accountable for the covalent adaptive network, with the reaction's effectiveness being regulated by either organic or organometallic catalysts. However, directly studying the exchange reaction pathway in the bulk phase using experimental approaches is challenging because of the intricate and interconnected structure of these vitrimers. Here, we perform comprehensive density functional theory (DFT) and experimental investigations to discover the detailed catalytic efficacy of siloxane exchange and provide direction for the reaction process using a 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) catalyst. The calculated transition barrier energy and catalytic efficiency of hexamethyldisiloxane and dihydroxy-dimethylsilane exchange derived from the nudged elastic band with transition-state calculations strongly agree with the experimental findings. In addition, Fukui indices, along with partial charges, are employed to evaluate the nucleophilic and electrophilic behaviors of silanol and siloxane molecules. Our analysis revealed that by utilizing the Fukui indices of both the acid and the base, we can make an approximate estimation of the respective kinetics of the SN2 process in the siloxane exchange reaction mechanism. These findings establish a foundation for comprehending a crucial aspect of the exchange mechanism in siloxane vitrimer systems and could aid in the development of novel catalysts.

Bamboo stumps that are artificially in use put pressure on dengue and chikungunya vector control in Dhaka city, Bangladesh.

BACKGROUND OBJECTIVES: Aedes aegypti and Ae. albopictus mosquitoes breed in natural and artificial containers, and they transmit dengue and chikungunya. A study was conducted to identify the contribution of bamboo stumps to these disease vectors that were used in the flower garden as pillars to hold the bamboo flex fence. METHODS: Two sizes of whole bamboo were used to hold fences around gardens at Dhaka University, Bangladesh, and were painted red and green. Mosquito larvae and pupae were collected from bamboo stumps between July and August, and vectors were identified up to the species level. The data were analyzed using the STATA/MP 14.2 version. RESULTS: 83.5% and 0.2% were Ae. albopictus and Ae. aegypti, respectively, and the remaining were Culex and Ar-migeres species. Ae. albopictus, Ae. aegypti, and both species-positive bamboo stumps were 46.9, 0.7, and 47.1%, respectively. 54.5% of the bamboo stumps had at least one mosquito species. The average stump depth for Aedes positive stumps (mean=11.7 cm, SE = 0.5) was significantly (p <0.001) higher than the Aedes negative stumps (mean = 9.5 cm, SE = 0.4). 53.8% and 38.0% stumps were found Aedes positive on the ground and upper sides of fences, respectively, and found significant (p<0.01) differences between both sides. A zero-inflated negative binomial count model is significant at a 5% level of significance, χ2(4) = 11.8, p = 0.019 (<0.05) for Ae. albopictus. Stump depth is found to have a significant positive effect on the number of Aedes-positive stumps. INTERPRETATION CONCLUSION: Artificially used natural containers are adding pressure to current mosquito control activities as mosquitoes are breeding on them, which needs additional attention.

Prevention policies for the marine ecological environment in the South China Sea as a consequence of excessive plastic compound use in Vietnam.

Vietnam suffers from a distressing predicament: It ranks among the most heavily contaminated nations on earth. Its coastal and marine domains are plagued by an excess of plastic waste. Vietnam has consistently discharged a substantial amount of waste into the oceans, ranging from 0.28 to 0.73 million metric tons annually. Numerous areas have emerged as focal points of plastic pollution throughout its extensive seashore and marine areas. The escalating presence of marine litter poses an increasingly grave threat to the intricate equilibrium of Vietnam's marine ecosystems. This comprehensive policy study reveals that the mounting problem of ocean plastic pollution, characterized by the abundance of floating plastic debris, imperils both plant and animal life, placing various marine species such as seabirds, fish, turtles, and cetaceans at risk. The consumption of minuscule plastic particles and the harmful impact of chemical pollutants from plastic waste in the ocean not only endangers the vitality of marine life but also poses a substantial hazard to human well-being because plastic waste infiltrates the food chain. This research reveals that, despite the existence of numerous laws and policies-including the Law on Environmental Protection 2020, the Marine Plastic Waste Management Initiative for the Fisheries Sector 2020-2030, and the National Action Plan for Management of Marine Plastic Litter-a significant amount of plastic waste is infiltrating the river network and eventually infiltrating oceans as a result of improper monitoring and ineffective enforcement of these legislations. Relying primarily on existing data released by the government and other sources and a wide range of gray literature retrieved from reputable databases, this study aims to evaluate the role of Vietnam's legal framework for combating the critical issue of marine plastic pollution in the South China Sea. Integr Environ Assess Manag 2024;00:1-19. © 2024 SETAC.

Unraveling cEMG-wet sEMG Correlation Dynamics: Investigating Influential Factors.

The electromyography (EMG) signal provides insight into neuromuscular activity which is used in medical and technological fields. Traditional needle electrodes and surface electrodes have several drawbacks making them less suitable for portable and long-term use. In contrast, emerging capacitive electrodes offer promising features over the existing electrodes. Yet, the full potential of capacitive electrodes remains untapped due to the lack of comprehensive design optimization for consistently reliable signal quality. This study highlights the complex interplay of factors influencing correlation in capacitive EMG (cEMG) and wet surface EMG (wet sEMG) signals. The study emphasizes the importance of the surface area of capacitive electrodes, muscle force, preprocessing, and sampling frequency in understanding and improving the correlation between cEMG and wet sEMG signals, providing valuable insights for future research and applications in the field. The study reveals that the electrode area has no significant effect on the correlation. However, the correlation significantly depends on the muscle force. In addition, removing artifacts from the cEMG signal increases the correlation, especially for lower force where artifacts are significant. Again, oversampling the EMG signal above 800 Hz does not have any impact on increasing the correlation but the correlation decreases with higher inter-electrode distance (IED). In this research, the highest correlation of 82.89% (normalized-91.62%) between cEMG and sEMG has been achieved for high muscle force with a plate area of 4 cm2. Therefore, the capacitive electrode can be an alternative for EMG signal acquisition.

Mental health in children conceived by Assisted Reproductive Technologies (ARTs): Insights from a longitudinal study of Australian children.

BACKGROUND: The mental health of children conceived using Assisted Reproductive Technologies (ARTs) such as In-Vitro-Fertilization (IVF) is a subject of significant controversy. Existing studies suggest children conceived through ART meet physical and cognitive developmental milestones at similar rates to their spontaneously conceived peers, however, a significant number of studies have connected ART conception with mental health conditions, particularly depression and attention-deficit hyperactivity disorder (ADHD) in adolescence. This study, therefore, aimed to determine whether maternal use of ARTs to achieve pregnancy is associated with an increased risk of mental disorders in these children, and whether these effects are sex-dependent or confounded by known covariates in the ART population. METHODS: Secondary data analysis was performed using Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) data; a nationally representative population-based cross-sequential cohort study. Multivariate logistic regression models examined the impact of ART (including IVF and other fertility drugs, from LSAC wave-1 and wave-2 conducted in 2004 and 2006, respectively) on mental health outcomes (i.e., autism, ADHD, anxiety and/or depression, from LSAC waves 8 conducted in 2018) in Australian adolescents aged 18-19 years in 2018 (n = 1735). Known sociological and obstetric covariates including maternal age, birth weight, smoking and drinking alcohol during pregnancy, maternal gestational diabetes, postnatal depression, hypertension, and socioeconomic status were considered to generate an adjusted logistic model. Variables with a p-value of <0.05 in the regression models were considered statistically significant. RESULTS: Of the 1735 mother-child dyads analysed, the maternal mean age was 35.6 years (Standard deviation = ±4.75), approximately 5% of mothers (n = 89) used ART to become pregnant, and 22% of adolescents (n = 384) had a mental disorder. Longitudinal analysis revealed no relationship between ART and children developing a mental disorder in the LSAC population. CONCLUSION: These results should reassure parents considering ART that there is no increased risk of psychological or neurodevelopmental problems in their ART conceived offspring.

Microplastic Contamination of Non-Mulched Agricultural Soils in Bangladesh: Detection, Characterization, Source Apportionment and Probabilistic Health Risk Assessment.

Microplastic contamination in agricultural soil is an emerging problem worldwide as it contaminates the food chain. Therefore, this research investigated the distribution of microplastics (MPs) in agricultural soils without mulch at various depths (0-5, 5-10, and 10-15 cm) across different zones: rural, local market, industrial, coastal, and research areas. The detection of MP types and morphology was conducted using FTIR and fluorescence microscopy, respectively. Eight types of MPs were identified, including high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), polyvinyl fluoride (PVF), polyvinyl alcohol (PVA), and polytetrafluoroethylene (PTFE), with concentrations ranging from 0.6 ± 0.21 to 3.71 ± 2.36 MPs/g of soil. The study found no significant trends in MP concentration, with ranges of 0-2.1 ± 0.38, 0-2.87 ± 0.55, and 0-2.0 ± 0.34 MPs/g of soil at depths of 0-5 cm, 5-10 cm, and 10-15 cm, respectively. The highest MP quantity was recorded at 8.67 in coastal area, while the lowest was 6.44 in the local market area. Various MP shapes, e.g., fiber, film, pellet, fragment, and irregular, were observed across all layers. PCA suggested irrigation and organic manure as potential sources of MPs. The estimated concentrations of MPs possessed low non-carcinogenic and carcinogenic risks to the farming community of Bangladesh.

Exploring the role of interleukin-1ß and interleukin-6 in the pathophysiology of obsessive-compulsive disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a highly prevalent neuropsychiatric disorder. Recently, there has been a growing interest in investigating the association between pro-inflammatory cytokines and the pathogenesis of OCD. However, studies targeting interleukin-1ß (IL-1ß) and interleukin-6 (IL-6) in OCD are limited. Therefore, the present study aimed to explore the potential role of pro-inflammatory cytokines IL-1ß and IL-6 in the pathophysiology and development of OCD. METHODS: This study recruited 58 OCD patients and 30 age-sex-matched healthy controls (HCs). A qualified psychiatrist diagnosed OCD patients and assessed HCs based on the Diagnostic and Statistical Manual for Mental Health Disorders, 5th edition (DSM-5) criteria. We measured the severity of OCD using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS). Serum IL-1ß and IL-6 levels were measured using ELISA kits following the appropriate methods. RESULTS: The results showed that serum IL-1ß levels were significantly elevated in OCD patients compared to HCs (23.68±1.65 pg/ml vs. 15.75±1.02 pg/ml; p = 0.002). Similarly, OCD patients exhibited significantly higher serum IL-6 levels than HCs (44.97±0.73 pg/ml vs. 37.04±0.35 pg/ml; p<0.001). We observed both cytokines were positively correlated with the Y-BOCS scores in OCD patients (IL-1ß: r = 0.380, p = 0.015; IL-6: r = 0.324, p = 0.026) which indicates their role in disease pathophysiology. CONCLUSION: These results suggest that serum IL-1ß and IL-6 levels may be associated with the pathophysiology of OCD. Also, these cytokines levels in blood samples can serve as early risk assessment tools for the development of OCD. We recommend further studies in a large and homogeneous population to support these findings.

A stacked ensemble machine learning approach for the prediction of diabetes.

Objectives: Diabetes has become a leading cause of mortality in both developed and developing countries, impacting a growing number of individuals worldwide. As the prevalence of the disease continues to rise, researchers have diligently worked towards developing accurate diabetes prediction models. The primary aim of this study is to utilize a diverse set of machine learning algorithms to detect the presence of diabetes, particularly in females, at an early stage. By leveraging these methods, this research seeks to provide physicians with valuable tools to identify the disease early, enabling timely interventions and improving patient outcomes. Methods: In this study, some state-of-the-art machine learning techniques, such as random forest classifiers with gridsearchCV, XGBoost, NGBoost, Bagging, LightGBM, and AdaBoost classifiers, were employed. These models were chosen as the base layer of our proposed stacked ensemble model because of their high accuracy. Before feeding the data into the models, the dataset was preprocessed to ensure optimal performance and obtain improved results. Results: The accuracy achieved in this study was 92.91%, which demonstrates its competitiveness with the existing approaches. Moreover, the utilization of the Shapley additive explanation (SHAP) facilitated the interpretation of machine learning models. Conclusion: We anticipate that these findings will be beneficial to healthcare providers, stakeholders, students, and researchers involved in diabetes prediction research and development.

Anti-Inflammatory, Wound Healing, and Anti-Diabetic Effects of Pure Active Compounds Present in the Ryudai Gold Variety of Curcuma longa.

Turmeric (Curcuma longa) contains curcumin, demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC). Nevertheless, curcumin is the most researched active ingredient for its numerous pharmacological effects. We investigated the impact of these curcuminoids found in Ryudai gold, an approved cultivar of Curcuma longa, on wound healing, inflammation, and diabetes. Sub-planter injections of carrageenan induced acute paw inflammation in rats. The wound-healing ability of 1% curcuminoids was examined by making a 6 mm round wound on the shaved dorsum of the mice with a biopsy punch. A single intraperitoneal injection of streptozotocin (50 mg/kg) was used to induce diabetes in mice. Curcuminoids at a dose rate of 100 mg/kg body weight were used with feed and as a gastric gavage to treat diabetes and inflammation in experimental animals. Paw thickness was measured at 1, 3, and 6 h following carrageenan injection. After three hours, mean paw volume was 58% in carrageenan-injected mice, which was 35%, 37%, and 31% in the curcumin, DMC, and BDMC groups, respectively. Histopathology of the paw tissue demonstrated severe infiltration of inflammatory cells and thickening of the dermis, which were remarkably improved by the curcuminoids. The wound-healing abilities were significantly higher in the curcumin- (95.0%), DMC- (93.17%), and BDMC-treated (89.0%) groups, in comparison to that of the control (65.09%) group at day nine. There were no significant differences in wound-healing activity among the groups treated with 1% curcuminoids throughout the study. Streptozotocin-induced diabetes was characterized by an increased blood glucose (552.2 mg/dL) and decreased body weight (31.2 g), compared to that of the control rats (145.6 mg/dL and 46.8 g blood glucose and body weight, respectively). It also caused an increase in serum alanine aminotransferase (ALT; 44.2 U/L) and aspartate aminotransferase (AST; 55.8 U/L) compared to that of the control group (18.6 U/L and 20.1 U/L, respectively). Histopathological examination of the liver showed that diabetes caused hepatic cellular necrosis, congestion of the central vein, and parenchymatous degeneration. However, all three curcuminoids significantly decreased blood glucose levels, ALT, and AST and improved the histopathological score of the liver. These results evidenced that not only curcumin but also DMC and BDMC have potent anti-inflammatory, wound healing, and anti-diabetic efficacy, and the Ryudai gold variety of turmeric could be used as a functional food supplement.

Unveiling the biofloc culture potential: Harnessing immune functions for resilience of shrimp and resistance against AHPND -causing Vibrio parahaemolyticus infection.

In shrimp aquaculture, disease mitigation may be accomplished by reducing the virulence of the pathogen or by boosting the shrimp's immunity. Biofloc technology is an innovative system that improves the health and resistance of shrimp to microbial infections while providing a viable option for maintaining the quality of culture water through efficient nutrient recycling. This review aimed at demonstrating the efficacy of the biofloc system in boosting the immune responses and protective processes of shrimp against Vibrio parahaemolyticus infection, which is known to cause Acute Hepatopancreatic Necrosis Disease (AHPND). Numerous studies have revealed that the biofloc system promotes the immunological capability of shrimp by raising multiple immune -related genes e.g. prophenoloxidase, serine proteinase gene, ras-related nuclear gene and penaeidinexpression and cellular and humoral responses such as hyperaemia, prophenoloxidase activity, superoxide dismutase activity, phagocytic activity; the protection and survival of shrimp when faced with a challenge from the V. parahaemolyticus strain have been enhanced. Furthermore, the use of the biofloc system improves water quality parameters and potentially bolstering their immune and overall health to effectively resist diseases; hence, promotes the growth of shrimp. The present review suggests that biofloc can serve as an effective therapy for both preventing and supporting the management of probable AHPND infection in shrimp culture. This approach exhibits potential for the progress of sustainable shrimp farming, higher productivity, and improved shrimp health.

Electronic medical records imputation by temporal Generative Adversarial Network.

The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

Assessment of knowledge, attitudes, and practice regarding air pollution and health effects among general people: A multi-divisional cross-sectional study in Bangladesh.

BACKGROUND: Bangladesh is one of the most densely populated countries in the world, with more than one-third of its people living in cities, and its air quality is among the worst in the world. The present study aimed to measure knowledge, attitudes and practice (KAP) towards air pollution and health effects among the general population living in the large cities in Bangladesh. METHODS: A cross-sectional e-survey was conducted between May and July 2022 among eight divisions in Bangladesh. A convenience sampling technique was utilized to recruit a total of 1,603 participants (55.58% males; mean age: 23.84 ± 5.93 years). A semi-structured questionnaire including informed consent, socio-demographic information, as well as questions regarding knowledge (11-item), attitudes (7-item) and practice (11-item) towards air pollution, was used to conduct the survey. All analyses (descriptive statistics and regression analyses) were performed using STATA (Version 15.0) and SPSS (Version 26.0). RESULTS: The mean scores of the knowledge, attitudes, and practice were 8.51 ± 2.01 (out of 11), 19.24 ± 1.56 (out of 21), and 12.65 ±5.93 (out of 22), respectively. The higher scores of knowledge, attitudes, and practice were significantly associated with several socio-demographic factors, including educational qualification, family type, residential division, cooking fuel type, etc. CONCLUSIONS: The present study found a fair level of knowledge and attitudes towards air pollution; however, the level of practice is not particularly noteworthy. The finding suggests the need to create more awareness among the general population to increase healthy practice to reduce the health effects of air pollution.

Design and simulation of a high performance Ag3CuS2 jalpaite-based photodetector.

This work provides a comprehensive investigation by using simulations and performance analysis of a high performance and narrowband Ag3CuS2 photodetector (PD) that operates in the near-infrared (NIR) region and is built using WS2 and BaSi2 semiconductors. Across its operational wavelength range, a comprehensive assessment of the device's electrical and optical properties such as photocurrent, open-circuit voltage, quantum efficiency, responsivity and detectivity is methodically carried out. Furthermore, a thorough investigation has been conducted into the impact of many parameters, including width, carrier density and defects of various layers. Also, the intricate interactions between WS2/Ag3CuS2 and Ag3CuS2/BaSi2 interface properties of the photodetector are explored. The Ag3CuS2-based PD remarkably produces the best outcomes with an open-circuit voltage of 0.74 V, current of 43.79 mA/cm2, responsivity of 0.79 AW-1 and detectivity of 4.73 × 1014 Jones and over 90 % QE in the NIR range for the Ag3CuS2 PD. The results showcase this jalpaite material as a promising one in the field of PD.

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation. For different amplitudes (A) within a sequence of values (A = 0.1 to 0.5), the highest number of infected mosquitoes occurs in July. However, simulations project that when ßM = 0.5 and A = 0.1, the peak of human infections occurs in late September. Not only the next-generation matrix approach along with the stability of disease-free and endemic equilibrium points are observed, but also a cutting-edge Machine learning (ML) approach such as the Prophet model is explored for forecasting future Dengue outbreaks in Bangladesh. Remarkably, we have fitted our solution curve of infection with the reported data by the government of Bangladesh. We can predict the outcome of 2024 based on the ML Prophet model situation of Dengue will be detrimental and proliferate 25 % compared to 2023. Finally, the study marks a significant milestone in understanding and managing Dengue outbreaks in Bangladesh.