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.

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.

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.

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.

Tailoring Mott-Schottky RuO2/MgFe-LDH Heterojunctions in Electrospun Microfibers: A Bifunctional Electrocatalyst for Water Electrolysis.

Hydrogen is a fuel of the future that has the potential to replace conventional fossil fuels in several applications. The quickest and most effective method of producing pure hydrogen with no carbon emissions is water electrolysis. Developing highly active electrocatalysts is crucial due to the slow kinetics of oxygen and hydrogen evolution, which limit the usage of precious metals in water splitting. Interfacial engineering of heterostructures has sparked widespread interest in improving charge transfer efficiency and optimizing adsorption/desorption energetics. The emergence of a built-in-electric field between RuO2 and MgFe-LDH improves the catalytic efficiency toward water splitting reaction. However, LDH-based materials suffer from poor conductivity, necessitating the design of 1D materials by integration of RuO2/ MgFe-LDH to enhance catalytic properties through large surface areas and high electronic conductivity. Experimental results demonstrate lower overpotentials (273 and 122 mV at 10 mA cm-2) and remarkable stability (60 h) for the RuO2/MgFe-LDH/Fiber heterostructure in OER (1 m KOH) and HER (0.5 m H2SO4) reactions. Density functional theory (DFT) unveils a synergistic mechanism at the RuO2/MgFe-LDH interface, leading to enhanced catalytic activity in OER and improved adsorption energy for hydrogen atoms, thereby facilitating HER catalysis.

Paddy insect identification using deep features with lion optimization algorithm.

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

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.

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.

Symptom response and episodic disability of long COVID in people with spinal cord injury: A case-control study.

BACKGROUND: Spinal cord injury (SCI) is a consequence of significant disability and health issues globally, and long COVID represents the symptoms of neuro-musculoskeletal, cardiovascular and respiratory complications. PURPOSE: This study aimed to identify the symptom responses and disease burden of long COVID in individuals with spinal cord injury. METHODS: This case-control study was conducted on patients with SCI residing at a specialised rehabilitation centre in Bangladesh. Forty patients with SCI with and without long COVID symptoms (LCS) were enrolled in this study at a 1:1 ratio according to WHO criteria. RESULT: Twelve LCS were observed in patients with SCI, including fatigue, musculoskeletal pain, memory loss, headache, respiratory problems, anxiety, depression, insomnia, problem in ADL problem in work, palpitation, and weakness. The predictors of developing long COVID include increasing age (p<0.002), increasing BMI (p<0.03), and longer duration of spinal cord injury (p<0.004). A significant difference (p<0.01) in overall years of healthy life lost due to disability (YLD) for non-long COVID cases was 2.04±0.596 compared to long COVID (LC) cases 1.22±2.09 was observed. CONCLUSION: Bangladeshi patients of SCI presented 12 long COVID symptoms and have a significant disease burden compared to non long COVID cases.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

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.

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.

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.

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system's ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system's classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field's capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach.

This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administration Training Centre (BPATC), the apex training institute for civil servants. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural network (ANN) techniques within Kirkpatrick's four-stage model framework, data were collected from a self-administered questionnaire survey of 71 in-service civil servants who participated in the APA training program. This study employs an asymmetric, non-linear model analyzed through a configurational approach and ANN to explore interrelationships among the four Kirkpatrick levels namely, reaction, learning, behavior, and results. Findings indicate that trainees were satisfied across all levels, identifying a non-linear relationship among these levels in post-training evaluation process. The research highlights that "learning skills" are most significant in the APA post-training evaluation, followed by behavior, results, and reaction. Theoretically, this research advances Kirkpatrick's model and adds to the literature on public service post-training evaluation. Practically, it recommends prioritizing strategies that address cognitive barriers to enhance training effectiveness. This study's innovative approach lies in its concurrent use of fsQCA and ANN methods to analyze the success or failure of APA-related trainees, offering alternative pathways to desired outcomes and contrasting traditional quantitative methods that provide a single solution. The findings have practical implications for public service training institutions and bureaucratic policymakers involved in capacity development, guiding the creation of more effective in-service training courses for public officials. The methodology and analysis can be applied in other contexts, allowing bureaucratic policymakers to replicate these findings in their learning institutes to identify unique configurations that lead to successful or unsuccessful training outcomes, adopt effective strategies, and avoid detrimental ones.

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.

Characterization of organelle DNA degradation mediated by DPD1 exonuclease in the rice genome-edited line.

Mitochondria and plastids, originated as ancestral endosymbiotic bacteria, contain their own DNA sequences. These organelle DNAs (orgDNAs) are, despite the limited genetic information they contain, an indispensable part of the genetic systems but exist as multiple copies, making up a substantial amount of total cellular DNA. Given this abundance, orgDNA is known to undergo tissue-specific degradation in plants. Previous studies have shown that the exonuclease DPD1, conserved among seed plants, degrades orgDNAs during pollen maturation and leaf senescence in Arabidopsis. However, tissue-specific orgDNA degradation was shown to differ among species. To extend our knowledge, we characterized DPD1 in rice in this study. We created a genome-edited (GE) mutant in which OsDPD1 and OsDPD1-like were inactivated. Characterization of this GE plant demonstrated that DPD1 was involved in pollen orgDNA degradation, whereas it had no significant effect on orgDNA degradation during leaf senescence. Comparison of transcriptomes from wild-type and GE plants with different phosphate supply levels indicated that orgDNA had little impact on the phosphate starvation response, but instead had a global impact in plant growth. In fact, the GE plant showed lower fitness with reduced grain filling rate and grain weight in natural light conditions. Taken together, the presented data reinforce the important physiological roles of orgDNA degradation mediated by DPD1.

Predominant Use of Bilateral Internal Mammary Arteries in Off-Pump Coronary Artery Bypass Surgery.

Bilateral internal mammary artery (BIMA) in coronary artery bypass grafting (CABG) has been shown to provide long-term clinical benefits over single internal mammary artery (SIMA) grafting. Nevertheless, the perceived technical complexity of the procedure and concerns about potential early postoperative complications, particularly, sternal wound infections, have led to a utilization rate of BIMA grafting of less than 5% in the United States. We systematically compared early (30-day) postoperative outcomes between the BIMA and SIMA cohorts in patients with similar baseline characteristics. A retrospective single-center study was conducted on matched patients, using stabilized inverse probability treatment weighting to mitigate bias between the 2 study cohorts. From 546 patients who underwent off-pump CABG initially identified, we examined 328 B.M. and 213 SIMA grafts from the matched samples. Despite using 60.4% BIMA grafts, we observed similar rates of 30-day overall and cardiac mortality between the BIMA and SIMA groups. The rates of 30-day postoperative complications, including superficial and deep sternal wound infections, stroke, sepsis, acute kidney injury, and cardiac arrest, were similar between the 2 groups. The rates of 30-day overall and cardiac readmission were also similar. In addition, the median length of hospital stays, intensive care unit stay, and ventilation times were similar between the 2 groups. In conclusion, our data suggest that a BIMA utilization rate of 60.4% in off-pump CABG procedures is achievable without causing any significant increment in early postoperative complications, including deep sternal wound infection.

DNA Delivery by Virus-Like Nanocarriers in Plant Cells.

Tobacco mild green mosaic virus (TMGMV)-like nanocarriers were designed for gene delivery to plant cells. High aspect ratio TMGMVs were coated with a polycationic biopolymer, poly(allylamine) hydrochloride (PAH), to generate highly charged nanomaterials (TMGMV-PAH; 56.20 ± 4.7 mV) that efficiently load (1:6 TMGMV:DNA mass ratio) and deliver single-stranded and plasmid DNA to plant cells. The TMGMV-PAH were taken up through energy-independent mechanisms in Arabidopsis protoplasts. TMGMV-PAH delivered a plasmid DNA encoding a green fluorescent protein (GFP) to the protoplast nucleus (70% viability), as evidenced by GFP expression using confocal microscopy and Western blot analysis. TMGMV-PAH were inactivated (iTMGMV-PAH) using UV cross-linking to prevent systemic infection in intact plants. Inactivated iTMGMV-PAH-mediated pDNA delivery and gene expression of GFP in vivo was determined using confocal microscopy and RT-qPCR. Virus-like nanocarrier-mediated gene delivery can act as a facile and biocompatible tool for advancing genetic engineering in plants.