Deazaflavin metabolite produced by endosymbiotic bacteria controls fungal host reproduction.

The endosymbiosis between the pathogenic fungus Rhizopus microsporus and the toxin-producing bacterium Mycetohabitans rhizoxinica represents a unique example of host control by an endosymbiont. Fungal sporulation strictly depends on the presence of endosymbionts as well as bacterially produced secondary metabolites. However, an influence of primary metabolites on host control remained unexplored. Recently, we discovered that M. rhizoxinica produces FO and 3PG-F420, a derivative of the specialized redox cofactor F420. Whether FO/3PG-F420 plays a role in the symbiosis has yet to be investigated. Here, we report that FO, the precursor of 3PG-F420, is essential to the establishment of a stable symbiosis. Bioinformatic analysis revealed that the genetic inventory to produce cofactor 3PG-F420 is conserved in the genomes of eight endofungal Mycetohabitans strains. By developing a CRISPR/Cas-assisted base editing strategy for M. rhizoxinica, we generated mutant strains deficient in 3PG-F420 (M. rhizoxinica ΔcofC) and in both FO and 3PG-F420 (M. rhizoxinica ΔfbiC). Co-culture experiments demonstrated that the sporulating phenotype of apo-symbiotic R. microsporus is maintained upon reinfection with wild-type M. rhizoxinica or M. rhizoxinica ΔcofC. In contrast, R. microsporus is unable to sporulate when co-cultivated with M. rhizoxinica ΔfbiC, even though the fungus was observed by super-resolution fluorescence microscopy to be successfully colonized. Genetic and chemical complementation of the FO deficiency of M. rhizoxinica ΔfbiC led to restoration of fungal sporulation, signifying that FO is indispensable for establishing a functional symbiosis. Even though FO is known for its light-harvesting properties, our data illustrate an important role of FO in inter-kingdom communication.

Keratin 17 modulates the immune topography of pancreatic cancer.

BACKGROUND: The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. METHODS: Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. RESULTS: K17 expression had profound effects on the exclusion of intratumoral CD8+ T cells and was also associated with decreased numbers of peritumoral CD8+ T cells, CD16+ macrophages, and CD163+ macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8+ T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. CONCLUSIONS: Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.

Temperature-Induced Conversion of 2D Vanadium-Doped MoSe2 Nanosheets to 1D V2MoO8 Rods: Enhanced Performance in Electrochemical Antibiotic Detection in Biological and Environmental Samples.

In this work, new strategies were developed to prepare 1D-V2MoO8 (VMO) rods from 2D V-doped MoSe2 nanosheets (VMoSe2) with good control over morphology and crystallinity by a facile hydrothermal and calcination process. The morphological changes from 2D to 1D rods were controlled by changing the calcination temperature from 300 to 600 °C. The elimination of Se and the incorporation of O into the V-Mo structure were evaluated by TGA, p-XRD, Raman, FE-SEM, EDAX, FE-TEM, and XPS analyses. These results prove that the optimization of the physical parameters leads to changes in the crystal phase and textural properties of the prepared material. The VMoSe2 and its calcined products were investigated as electrochemical sensors for the detection of the antibacterial drug nitrofurantoin (NFT). At a calcination temperature of 500 °C, the modified screen-printed carbon electrodes (SPCE) proved to be an excellent electrochemical sensor for the detection of NFT in neutral media. Under the optimized conditions, VMO-500 °C/SPCE exhibits low detection limit (LOD) (0.015 µM), wide linear ranges (0.1-31, 47-1802 µM), good sensitivity, and selectivity. The proposed sensor was successfully used for the analysis of NFT in real samples with good recovery results. Moreover, the reduction potential of NFT agreed well with the theoretical analysis using quantum chemical calculations, with the B3LYP with 6-31G(d,p) basis set predicting an E0 value of -0.45 V. The interaction between the electrode surface and NFT via the LUMO diagram and the electrostatic potential surface is also discussed.

Holistic perilous index-based environmental appraisal of Metal(oid)s in the sole coral-bearing island of northeastern bay of Bengal.

This study aimed to evaluate the concentration, distribution, along with the environmental and human health impact of eight heavy metals-Pb, Cr, Cu, Cd, Zn, Mn, Ni, and As-on St. Martin's Island in the northeastern Bay of Bengal, and in doing so to help implement new legislations to protect the island. Focusing on the island's significance as a tourist destination, with seafood being a prominent dietary component, three sample types (sediment, seawater, and crustaceans) were selected for a comprehensive assessment, considering seasonal variations. Concentration of metals was observed to be lower than the established standards in sediment samples, but in seawater samples, Pb, Cr, Cd and Zn were higher than US-EPA values for natural marine water. The metals displayed a decreasing trend of Zn > Ni > Pb > Cu > Mn > As > Cd > Cr in crustacean samples for both seasons. Crustacean samples displayed higher metal concentrations in winter than in monsoon. Pb exceeded the maximum allowable limit for crustaceans with a concentration of about 3 and 4 mg kg-1 in monsoon and winter respectively; being more than 6-8 times the standard for Bangladesh which is only about 0.5 mg kg-1. Health indices displayed that although adults may suffer less from carcinogenic/non-carcinogenic health effects, the risks are far greater for children. For both age groups, As and Ni displayed possibilities of developing cancer. Principal Component Analysis (PCA)shed light on the sources of metals and showed that most of them were from anthropogenic sources. Overall, this study found that the quality of the environment of the island was better in comparison to previous studies made before the pandemic, and so, if the trend continues, it may lead to a better environment for the organisms around the island and help to keep the negative physiological impacts from the consumption of these organisms to a minimal.

Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.

Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.

Ti3C2Tx Filled in EMIMBF4 Semi-Solid Polymer Electrolytes for the Zinc-Metal Battery.

Zinc-ion batteries (ZIBs) are promising candidates for safe energy storage applications. However, undesirable parasitic reactions such as dendrite growth, gas evaluation, anode corrosion, and structural damage to the cathode under an acidic microenvironment severely affected cell performance. To resolve these issues, an MXene entrapped in an ionic liquid semi-solid gel polymer electrolyte (GPE) composite was explored. The molecular-level mixing of poly(vinylidene fluoride-co-hexafluoropropylene) (PVHF), zinc trifluoromethanesulfonate (Zn(OTF)2), 1-ethyl-3-methylimidazolium tetrafluoroborate (EMIBF4) ionic liquid, and Ti3C2Tx MXene provided a controlled Zn2+ shuttle toward the anode/cathode. Ti3C2Tx/EMIBF4/Zn(OTF)2/PVHF exhibited a breaking strength of 0.36 MPa with an associated extension of 23%. The Zn//Ti3C2Tx/EMIBF4/Zn(OTF)2/PVHF//Zn symmetric cell with continuous zinc plating/stripping exhibited excellent Zn2+ ion mobility toward the anode and cathode without undesired reactions. This was confirmed by post-mortem analysis after a symmetric cell compatibility test. The as-prepared GPE with a Na3V2(PO4)3 (NVP) cathode exhibited a high chemical diffusion coefficient of 1.14 × 10-7. It also showed an outstanding reversible capacity of 89 mAh g-1 at C/10 with an average discharge plateau voltage of 1.45 V, cycle durability, and controlled self-discharge. These results suggested that the Zn2+ ions in the Ti3C2Tx/EMIBF4/Zn(OTF)2/PVHF composite are reversibly labile in the anode and cathode directions.

Keratin 17 modulates the immune topography of pancreatic cancer.

Background: The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. Methods: Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. Results: K17 expression had profound effects on the exclusion of intratumoral CD8 + T cells and was also associated with decreased numbers of peritumoral CD8 + T cells, CD16 + macrophages, and CD163 + macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8 + T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. Conclusions: Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.

Desalting Plasma Protein Solutions by Membrane Capacitive Deionization.

Plasma protein therapies are used by millions of people across the globe to treat a litany of diseases and serious medical conditions. One challenge in the manufacture of plasma protein therapies is the removal of salt ions (e.g., sodium, phosphate, and chloride) from the protein solution. The conventional approach to remove salt ions is the use of diafiltration membranes (e.g., tangential flow filtration) and ion-exchange chromatography. However, the ion-exchange resins within the chromatographic column as well as filtration membranes are subject to fouling by the plasma protein. In this work, we investigate the membrane capacitive deionization (MCDI) as an alternative separation platform for removing ions from plasma protein solutions with negligible protein loss. MCDI has been previously deployed for brackish water desalination, nutrient recovery, mineral recovery, and removal of pollutants from water. However, this is the first time this technique has been applied for removing 28% of ions (sodium, chloride, and phosphate) from human serum albumin solutions with less than 3% protein loss from the process stream. Furthermore, the MCDI experiments utilized highly conductive poly(phenylene alkylene)-based ion exchange membranes (IEMs). These IEMs combined with ionomer-coated nylon meshes in the spacer channel ameliorate Ohmic resistances in MCDI improving the energy efficiency. Overall, we envision MCDI as an effective separation platform in biopharmaceutical manufacturing for deionizing plasma protein solutions and other pharmaceutical formulations without a loss of active pharmaceutical ingredients.

Nutrient and heavy metal dynamics in the coastal waters of St. Martin's island in the Bay of Bengal.

Seasonal variation observations were conducted in the coastal waters of St. Martin's Island in the Bay of Bengal to examine the influence of physical processes and the distribution pattern of nutrients in the ocean water. Pollution evaluation indices, health index and statistical techniques were incorporated to assess the heavy metal contamination. Two seasons, cool dry winter and pre-monsoon hot, were considered for sampling from 12 stations around the island. The Cool dry winter season has higher nutrient concentrations than the Pre-monsoon Hot season. The concentration of nutrients appeared as follows: Silicate > Nitrate > Ammonia > Phosphate > Nitrite. PCA and Pearson's Correlation showed that fresh water from nearby rivers, deep water upwelling, and, in some situations, modest anthropogenic sources are crucial. Hence, low DO and phosphate levels during the pre-monsoon hot season indicate there is a planktonic process like photosynthesis prevailing. The island's north-western and south-eastern regions have higher nutrient concentrations, which may be seasonal and due to wind action. Pb, Cu, As, Cr, Cd, and Zn were also considered to comprehend the island's geo-chemical perspectives and ecological and human health risks. The Pre-monsoon Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI) demonstrated that some places are much higher than the threshold limit, even though no significantly higher value was detected in the cool winter season. The Nemerow Index, the Total Ecological Risk Index (TERI), indicated that heavy metal contamination was severe to moderate and low to moderate. Finally, Pearson's correlation showed the association between physical and chemical characteristics, similar to PCA and Pearson's correlation for nutrients and heavy metals. Thus, this research may help shed light on the state of the seas around St. Martin's Island. This study may also provide explicit insights for the authority to take the necessary measures to preserve marine ecology and the associated terrestrial ecosystem.

Mass-selected ion-molecule cluster beam apparatus for ultrafast photofragmentation studies.

We describe an apparatus for investigating the excited-state dissociation dynamics of mass-selected ion-molecule clusters by mass-resolving and detecting photofragment-ions and neutrals, in coincidence, using an ultrafast laser operating at high repetition rates. The apparatus comprises a source that generates ion-molecule clusters, a time-of-flight spectrometer, and a mass filter that selects the desired anions, and a linear-plus-quadratic reflectron mass spectrometer that discriminates the fragment anions after the femtosecond laser excites the clusters. The fragment neutrals and anions are then captured by two channeltron detectors. The apparatus performance is tested by measuring the photofragments: I-, CF3I-, and neutrals from photoexcitation of the ion-molecule cluster CF3I·I- using femtosecond UV laser pulses with a wavelength of 266 nm. The experimental results are compared with our ground state and excited state electronic structure calculations as well as the existing results and calculations, with particular attention to the generation mechanism of the anion fragments and dissociation channels of the ion-molecule cluster CF3I·I- in the charge-transfer excited state.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation.

Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world's inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R 2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R 2 for Aus; 0.92 MSE, 90% R 2 for Aman; 0.246 MSE, 99% R 2 for Boro; 0.062 MSE, 99% R 2 for wheat; and 0.016 MSE, 99% R 2 for potato production prediction. The Diebold-Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

Immunoinformatics for Novel Multi-Epitope Vaccine Development in Canine Parvovirus Infections.

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate.

Electron Scattering from 1-Methyl-5-Nitroimidazole: Cross-Sections for Modeling Electron Transport through Potential Radiosensitizers.

In this study, we present a complete set of electron scattering cross-sections from 1-Methyl-5-Nitroimidazole (1M5NI) molecules for impact energies ranging from 0.1 to 1000 eV. This information is relevant to evaluate the potential role of 1M5NI as a molecular radiosensitizers. The total electron scattering cross-sections (TCS) that we previously measured with a magnetically confined electron transmission apparatus were considered as the reference values for the present analysis. Elastic scattering cross-sections were calculated by means of two different schemes: The Schwinger multichannel (SMC) method for the lower energies (below 15 eV) and the independent atom model-based screening-corrected additivity rule with interferences (IAM-SCARI) for higher energies (above 15 eV). The latter was also applied to calculate the total ionization cross-sections, which were complemented with experimental values of the induced cationic fragmentation by electron impact. Double differential ionization cross-sections were measured with a reaction microscope multi-particle coincidence spectrometer. Using a momentum imaging spectrometer, direct measurements of the anion fragment yields and kinetic energies by the dissociative electron attachment are also presented. Cross-sections for the other inelastic channels were derived with a self-consistent procedure by sampling their values at a given energy to ensure that the sum of the cross-sections of all the scattering processes available at that energy coincides with the corresponding TCS. This cross-section data set is ready to be used for modelling electron-induced radiation damage at the molecular level to biologically relevant media containing 1M5NI as a potential radiosensitizer. Nonetheless, a proper evaluation of its radiosensitizing effects would require further radiobiological experiments.

Risk assessment of microplastic pollution in an industrial region of Bangladesh.

Despite the high potential for microplastics (MPs) pollution in Bangladesh, the presence of MPs in the industrial region has largely been unexplored in Bangladesh. So, this study was conducted to determine whether MP pollution is prevalent in the industrial soil of Bangladesh and the extent of its toxicity. To examine MPs, a total of 12 soil samples were collected from the industrial region of Narayanganj, and a stereoscopic microscope was used to visually identify the MPs. Prior to that the technique of density separation and sieving was applied to extract MPs from those 12 soil samples. Among the twelve investigated samples, a total of 151 MPs (Mean: 12.6 ± 7.9 particles kg-1) were identified, which were mostly white and ranged in size from 0.5 to 1 mm. Different types of MPs according to their shapes such as fibers (60.3%), fragments (19.2%), films (10.6%), and foam (9.9%) have been detected. 7 MPs (Mean: 0.58 ± 0.79) have been found in 3 urban farmland sites, 15 MPs (Mean: 1.87 ± 1.81) in two near metropolitan areas, and 129 MPs (Mean: 4.6 ± 4.39) in 7 industrial locations. Five polymers were identified by µ-FTIR, among which Polyamide predominated, followed by Polypropylene. According to risk assessments, the region falls under hazard categories II and III, suggesting a moderate to high risk. This paper gives thorough information on the toxicity of MP in an industrial location; therefore, it may be useful in the development of effective methods to address environmental issues.

Short Jute Fiber Preform Reinforced Polypropylene Thermoplastic Composite: Experimental Investigation and Its Theoretical Stiffness Prediction.

Natural-based lignocellulose fibrous materials can be used as a sustainable alternative to conventional fossil-based fibers such as glass fibers, in lightweight fiber-reinforced thermoplastic composites for marine, automotive, aerospace, or other advanced applications. However, one of the main challenges in using natural fiber-based thermoplastic composites is the low mechanical performance of composite structures. This can be improved significantly through the development of an optimized novel fiber architecture with enhanced fiber packing properties, following a low-cost production process. In this context, this study demonstrates a less energy-consuming and cheaper manufacturing process, for developing highly individualized short jute fiber-based dry fiber preform architecture, with an improved fiber packing property. Short jute fibers were chemically treated with alkali and PVA sizing treatments in the processing of new fiber preform architectures, and they were used in manufacturing of ultimate short jute fiber/polypropylene (PP) thermoplastic composites. The newly developed short fiber thermoplastic composites showed a significant improvement in mechanical properties (tensile, flexural, and impact) compared to any other natural fiber architecture-based (woven, knitted, nonwoven, unidirectional, etc.) composites found in the literature. Due to the use of new fiber architecture, the developed composites' fiber content was observed to increase. In addition, the compatibility of jute fibers with the polypropylene matrix was strengthened with the application of chemical treatments on highly individualized jute fibers. These reasons were responsible for the enhancement of mechanical properties of developed composites. Micromechanics of the fibers in composites were evaluated using the modified rule of the mixture and Halpin-Tsai equations for stiffness prediction of the composites in order to develop a theoretical understanding of newly developed composites' mechanics. It is thought that the improved mechanical performance of short jute fiber/PP thermoplastic composites can extend the use of these composites in many load-demanding applications, wherein normally synthetic fiber composites are used.

Advances in long-acting slow effective release antiretroviral therapies for treatment and prevention of HIV infection.

Adherence to daily oral antiretroviral therapy (ART) is a barrier to both treatment and prevention of human immunodeficiency virus (HIV) infection. To overcome limitations of life-long daily regimen adherence, long-acting (LA) injectable antiretroviral (ARV) drugs, nanoformulations, implants, vaginal rings, microarray patches, and ultra-long-acting (ULA) prodrugs are now available or in development. These medicines enable persons who are or at risk for HIV infection to be treated with simplified ART regimens. First-generation LA cabotegravir, rilpivirine, and lenacapavir injectables and a dapivirine vaginal ring are now in use. However, each remains limited by existing dosing intervals, ease of administration, or difficulties in finding drug partners. ULA ART regimens provide an answer, but to date, such next-generation formulations remain in development. Establishing the niche will require affirmation of extended dosing, improved access, reduced injection volumes, improved pharmacokinetic profiles, selections of combination treatments, and synchronization of healthcare support. Based on such needs, this review highlights recent pharmacological advances and a future treatment perspective. While first-generation LA ARTs are available for HIV care, they remain far from ideal in meeting patient needs. ULA medicines, now in advanced preclinical development, may close gaps toward broader usage and treatment options.

Depression among Bangladeshi diabetic patients: a cross-sectional, systematic review, and meta-analysis study.

AIM: This study aims to assess the prevalence and associated factors of depression among diabetic patients in a cross-sectional sample and perform a systematic review and meta-analysis of the extant studies to date. METHODS: A face-to-face semi-structured interview of established diabetic patients was conducted in four districts of Bangladesh between May 24 to June 24, 2022, and the Patient Health Questionnaire (PHQ-2) was used to detect depression. PRISMA guidelines were followed to conduct a systematic review and meta-analysis, with Bangladeshi articles published until 3rd February 2023. RESULTS: The prevalence of depression among 390 diabetic patients was 25.9%. Having secondary education and using both insulin and medication increased the likelihood of depression, whereas being a business professional and being physically active reduced the likelihood of depression. The systematic review and meta-analysis indicated that the pooled estimated prevalence of depression was 42% (95% CI 32-52%). Females had a 1.12-times higher risk of depression than males (OR = 1.12, 95% CI: 0.99 to 1.25, p < 0.001). CONCLUSIONS: Two-fifths of diabetic patients were depressed, with females at higher risk. Since depression among diabetic patients increases adverse outcomes, improved awareness and screening methods should be implemented to detect and treat depression in diabetic patients.

Phafins Are More Than Phosphoinositide-Binding Proteins.

Phafins are PH (Pleckstrin Homology) and FYVE (Fab1, YOTB, Vac1, and EEA1) domain-containing proteins. The Phafin protein family is classified into two groups based on their sequence homology and functional similarity: Phafin1 and Phafin2. This protein family is unique because both the PH and FYVE domains bind to phosphatidylinositol 3-phosphate [PtdIns(3)P], a phosphoinositide primarily found in endosomal and lysosomal membranes. Phafin proteins act as PtdIns(3)P effectors in apoptosis, endocytic cargo trafficking, and autophagy. Additionally, Phafin2 is recruited to macropinocytic compartments through coincidence detection of PtdIns(3)P and PtdIns(4)P. Membrane-associated Phafins serve as adaptor proteins that recruit other binding partners. In addition to the phosphoinositide-binding domains, Phafin proteins present a poly aspartic acid motif that regulates membrane binding specificity. In this review, we summarize the involvement of Phafins in several cellular pathways and their potential physiological functions while highlighting the similarities and differences between Phafin1 and Phafin2. Besides, we discuss research perspectives for Phafins.

Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An In Silico Approach.

As an omnipresent opportunistic bacterium, Pseudomonas aeruginosa PAO1 is responsible for acute and chronic infection in immunocompromised individuals. Currently, this bacterium is on WHO's red list where new antibiotics are urgently required for the treatment. Finding essential genes and essential hypothetical proteins (EHP) can be crucial in identifying novel druggable targets and therapeutics. This study is aimed at characterizing these EHPs and analyzing subcellular and physiochemical properties, PPI network, nonhomologous analysis against humans, virulence factor and novel drug target prediction, and finally structural analysis of the identified target employing around 42 robust bioinformatics tools/databases, the output of which was evaluated using the ROC analysis. The study discovered 18 EHPs from 336 essential genes, with domain and functional annotation revealing that 50% of these proteins belong to the enzyme category. The majority are cytoplasmic and cytoplasmic membrane proteins, with half being stable proteins subjected to PPIs network analysis. The network contains 261 nodes and 269 edges for 9 proteins of interest, with 11 hubs containing at least three nodes each. Finally, a pipeline builder predicts 7 proteins with novel drug targets, 5 nonhomologous proteins against human proteome, human antitargets, and human gut flora, and 3 virulent proteins. Among these, homology modeling of NP_249450 and NP_251676 was done, and the Ramachandran plot analysis revealed that more than 94% of the residues were in the preferred region. By analyzing functional attributes and virulence characteristics, the findings of this study may facilitate the development of innovative antibacterial drug targets and drugs of Pseudomonas aeruginosa PAO1.