Development and characterization of polyethylene oxide and guar gum-based hydrogel; a detailed in-vitro analysis of degradation and drug release kinetics.

Herein, we synthesized hydrogel films from crosslinked polyethylene oxide (PEO) and guar gum (GG) which can offer hydrophilicity, antibacterial efficacy, and neovascularization. This study focuses on synthesis and material/biological characterization of rosemary (RM) and citric acid (CA) loaded PEO/GG hydrogel films. Scanning Electron Microscopy images confirmed the porous structure of the developed hydrogel film matrix (PEO/GG) and the dispersion of RM and CA within it. This porous structure promotes moisture adsorption, cell attachment, proliferation, and tissue layer formation. Fourier Transform Infrared Spectroscopy (FTIR) further validated the crosslinking of the PEO/GG matrix, as confirmed by the appearance of C-O-C linkage in the FTIR spectrum. PEO/GG and PEO/GG/RM/CA revealed similar degradation and release kinetics in Dulbecco's Modified Eagle Medium, Simulated Body Fluid, and Phosphate Buffer Saline (degradation of ∼55 % and release of ∼60 % RM in 168 h.). The developed hydrogel film exhibited a zone of inhibition against Escherichia. coli (2 mm) and Staphylococcus. aureus (9 mm), which can be attributed to the presence of RM in the hydrogel film. Furthermore, incorporating CA in the hydrogel film promoted neovascularization, as confirmed by the Chorioallantoic Membrane Assay. The developed RM and CA-loaded PEO/GG-based hydrogel films offered suitable in-vitro properties that may aid in potential wound healing applications.

Correction to "Mitigation of Salinity Stress and Lead Toxicity in Maize by Exogenous Application of the Sorghum Water Extract".

[This corrects the article DOI: 10.1021/acsomega.3c09495.].

Multispectral breast image grayscale and quality enhancement by repeated pair image registration & accumulation method.

Nowadays, for detecting breast cancer in its early stages, the focus is on multispectral transmission imaging. Frame accumulation is a promising technique to enhance the grayscale level of the multispectral transmission images. Still, during the image acquisition process, human respiration or camera jitter causes the displacement of the frame's sequence which leads to the loss of accuracy and image quality of the frame accumulated image is reduced. In this article, we have proposed a new method named "repeated pair image registration and accumulation "to resolve the issue. In this method first pair of images from the sequence is first registered and accumulated followed by the next pair to be registered and accumulated. Then these two accumulated frames are registered and accumulated again. This process is repeated until all the frames from the sequence are processed and the final image is obtained. This method is tested on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of quality, accuracy, and grayscale by various mathematical assessments. Furthermore, the processing time of our proposed method is very low because descent gradient optimization algorithm is used here for image registration purpose. This optimization algorithm has high speed as compared to other methods and is verified by registering a single image of each wavelength by three different methods. It has laid the foundations of early detection of breast cancer using multispectral transmission imaging.

Fabrication of photo-induced molecular superoxide radical generator for highly efficient therapy against bacterial wound infection.

The pressing need for highly efficient antibacterial strategies arises from the prevalence of microbial biofilm infections and the emergence of rapidly evolving antibiotic-resistant strains of pathogenic bacteria. Photodynamic therapy represents a highly efficient and compelling antibacterial approach, offering promising prospects for effective control of the development of bacterial resistance. However, the effectiveness of many photosensitizers is limited due to the reduced generation of reactive oxygen species (ROS) in hypoxic microenvironment, which commonly occur in pathological conditions such as inflammatory and bacteria-infected wounds. Herein, we designed and prepared two phenothiazine-derived photosensitizers (NB-1 and NB-2), which can effectively generate superoxide anion radicals (O2●-) through the type I process. Both photosensitizers demonstrate significant efficacy in vitro for the eradication of broad-spectrum bacteria. Moreover, NB-2 possesses distinct advantages including strong membrane binding and strong generation of O2●-, rendering it an exceptionally efficient antibacterial agent against mature biofilms. In addition, laser activated NB-2 could be applied to treat MRSA-infected wound in vivo, which offers new opportunities for potential practical applications.

Enhancing the selectivity for light olefins through catalytic cracking of n-hexane by phosphorus doping on lanthanum-modified ZSM-5.

Naphtha, as the primary raw material in the production of light olefins, could well accommodate their increasing demand through the energy-efficient process of catalytic cracking with ZSM-5. In the current work, different amounts of lanthanum and phosphorous were loaded on ZSM-5 using the wet impregnation method to tune the acidic properties of ZSM-5 for selective catalytic cracking of n-hexane to produce light olefins. Various characterization techniques such as X-ray diffraction (XRD), Al nuclear magnetic resonance (NMR), temperature-programmed desorption of NH3 (NH3-TPD), Py-Fourier transform infra-red (Py-FTIR), inductively coupled plasma optical emission spectroscopy (ICP-OES), N2 adsorption-desorption, X-ray photoelectron spectra (XPS), and scanning electron microscopy were adopted to investigate the modified zeolites. It was found that adding La to ZSM-5 (0.25 wt% to 1 wt%) improved the catalytic life and increased the n-hexane conversion (to 99.7%), while the further addition had a negative impact, reducing the conversion rate and deviating the product selectivity towards a substantial, undesired benzene, toluene, and xylene (BTX) fraction (33%). On the other hand, a 64% selectivity for light olefins was achieved on phosphorous-doped ZSM-5 (at a loading amount of 1 wt%) while reducing the BTX fraction (2.3%) and converting 69% of the n-hexane. A dual metal-modified ZSM-5 with optimal loading amount, 1P0.25LaZ5 (phosphorus 1 wt% and La 0.25 wt%), helped boost the light olefin selectivity to 62% in the tuned Lewis acid sites at an n-hexane conversion of about 77% while decreasing the undesired BTX selectivity to 3% by reducing the number of Brønsted sites. Thus, the current study reveals that tuning the acidic sites of ZMS-5 by dual metal augmentation with P.La is an effective way of controlling the amount of undesirable BTX produced at a stable n-hexane conversion rate and substantial olefin selectivity.

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA.

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Geospatial quality assessment of locally available ice for heavy metals and metalloids and their potential risks for human health in Karachi, Pakistan.

Extreme hot conditions during summers, high poverty rate and continuous electricity load shedding affect commercial manufacturing and sale of ice in many countries. The vendors prepared ice using untreated piped water, tanker water and ground water. These waters may contain hazardous pollutants and ice made from them will pose a potential human health risk. Thus, it is important to regularly monitor the chemical composition of water sources and the quality of the manufactured ice. A contemporary examination was carried out to evaluate the physico-chemical properties and heavy metals and metalloids in the ice sold in all the districts of Karachi, Pakistan. This pioneering study was an innovative effort to assess the ice quality in relation to potential pollutant hazards to human health; with concomitant geospatial information. The geospatial distribution of ice quality and major constituents were among the measured parameters; carefully associated with further geospatial information, determined using GIS (Geographic Information Systems) and PCA (Principal Component Analysis) techniques. Interestingly, the physico-chemical analyses revealed that the ice quality was marginally adequate and the total mean metal-metalloid contents were in the sequence of Pb > Ni > Zn > Fe > Cr > As. The concentrations of these metals were above the upper allowable limits with reference to the recommended WHO guidelines. We observed that 57.1% and 35.7% ice samples had good physico-chemical properties assessed using the Ice Quality Index (IQI). Conversely, the IQI for metals showed that the ice was unsafe for human consumption. In terms of health risk assessment, the overall mean CDI (Chronic Daily Intake) and HQ (Hazard Quotient) values were in the order of Pb () > Ni (3.2) > Zn (2.3) > Fe (2.1) > Cr (1.6) > As (0.5) and Pb (7.4) > As (1.7) > Cr (0.5) > Ni (0.4 > Zn (0.008) > Fe (0.003), respectively. This study highlighted that routine monitoring of the water supplies available for making ice is required to protect public health.

Salicylic Acid and Gemma-Aminobutyric Acid Mediated Regulation of Growth, Metabolites, Antioxidant Defense System and Nutrient Uptake in Sunflower (Helianthus annuus L.) Under Arsenic Stress.

Background: Arsenic (As) is a highly toxic and carcinogenic pollutant commonly found in soil and water, posing significant risks to human health and plant growth. Objective: The objectives of this study to evaluate morphological, biochemical, and physiological markers, as well as ion homeostasis, to alleviate the toxic effects of As in sunflowers through the exogenous application of salicylic acid (SA), γ-aminobutyric acid (GABA), and their combination. Methods: A pot experiment was conducted using two sunflower genotypes, FH-779 and FH-773, subjected to As stress (60 mg kg-1) to evaluate the effects of SA at 100 mg L-1, GABA at 200 mg L-1, and their combination on growth and related physiological and biochemical attributes under As stress. Results: The study revealed that As toxicity had a detrimental effect on various growth parameters, chlorophyll pigments, relative water content, total proteins, and nutrient uptake in sunflower plants. It also led to increased oxidative stress, as indicated by higher levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2), along with As accumulation in the roots and leaves. However, the application of SA and GABA protected against As-induced damage by enhancing the enzymatic antioxidant defense system. This was achieved through the activation of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities, as well as an increase in osmolytes. They also improved nutrient acquisition and plant growth under As toxicity. Conclusions: We investigated the regulatory roles of SA and GABA in mitigating arsenic-induced phytotoxic effects on sunflower. Our results revealed a significant interaction between SA and GABA in regulating growth, photosynthesis, metabolites, antioxidant defense systems, and nutrient uptake in sunflower under As stress. These findings provide valuable insights into plant defense mechanisms and strategies to enhance stress tolerance in contaminated environments. In the future, SA and GABA could be valuable tools for managing stress in other important crops facing abiotic stress conditions.

Cu-MOF loaded chitosan based freeze-dried highly porous dressings with anti-biofilm and pro-angiogenic activities accelerated Pseudomonas aeruginosa infected wounds healing in rats.

Metal-organic frameworks (MOFs)-based therapy opens a new area for antibiotic-drug free infections treatment. In the present study, chitosan membranes (CS) loaded with two concentrations of copper-MOF 10 mg/20 ml (Cu-MOF10/CS) & 20 mg/20 ml (Cu-MOF20/CS) were prepared by a simple lyophilization procedure. FTIR spectra of Cu-MOF10/CS and Cu-MOF20/CS dressings confirmed absence of any undesirable chemical changes after loading Cu-MOF. The SEM images of the synthesized materials (CS, Cu-MOF10/CS & Cu-MOF20/CS) showed interconnected porous structures. Cytocompatibility of the materials was confirmed by fibroblasts cells culturing and the materials were hemocompatible, with blood clotting index <5 %. Cu-MOF20/CS showed comparatively higher effective antibacterial activity against the tested strains; E. coli (149.2 %), P. aeruginosa (165 %) S. aureus (117.8 %) and MRSA (142 %) as compared to Amikacin, CS and Cu-MOF10/CS membranes. Similarly, Cu-MOF20/CS dressing significantly eradicated the biofilms; P. aeruginosa (37 %) and MRSA (52 %) respectively. In full thickness infected wound rat model, on day 23, Cu-MOF10/CS and Cu-MOF20/CS promoted wound healing up to 87.7 % and 82 % respectively. H&E staining of wounded tissues treated with Cu-MOF10/CS & Cu-MOF20/CS demonstrated enhanced neovascularization and re-epithelization along-with reduced inflammation, while trichrome staining exhibited increased collagen deposition. Overall, this study declares Cu-MOFs loaded chitosan dressings a multifunctional platform for the healing of infected wounds.

3D-printed polyether-ether ketone/carboxymethyl cellulose scaffolds coated with Zn-Mn doped mesoporous bioactive glass nanoparticles.

Patient-specific fabrication of scaffold/implant requires an engineering approach to manufacture the ideal scaffold. Herein, we design and 3D print scaffolds comprised of polyether-ether-ketone (PEEK) and sodium-carboxymethyl cellulose (Na-CMC). The fabricated scaffold was dip coated with Zn and Mn doped bioactive glass nanoparticles (Zn-Mn MBGNs). The synthesized ink exhibit suitable shear-thinning behavior for direct ink write (DIW) 3D printing. The scaffolds were crafted with precision, featuring 85% porosity, 0.3 mm layer height, and 1.5 mm/s printing speed at room temperature. Scanning electron microscopy images reveal a well-defined scaffold with an average pore size of 600 ± 30 µm. The energy dispersive X-ray spectroscopy analysis confirmed a well dispersed/uniform coating of Zn-Mn MBGNs on the PEEK/Na-CMC scaffold. Fourier transform infrared spectroscopy approved the presence of PEEK, CMC, and Zn-Mn MBGNs. The tensile test revealed a Young's modulus of 2.05 GPa. Antibacterial assays demonstrate inhibition zone against Staphylococcus aureus and Escherichia Coli strains. Chick Chorioallantoic Membrane assays also present significant angiogenesis potential, owing to the antigenic nature of Zn-Mn MBGNs. WST-8 cell viability assays depicted cell proliferation, with a 103% viability after 7 days of culture. This study suggests that the PEEK/Na-CMC scaffolds coated with Zn-Mn MBGNs are an excellent candidate for osteoporotic fracture treatment. Thus, the fabricated scaffold can offer multifaceted properties for enhanced patient outcomes in the bone tissue regeneration.

Impact of Limosilactobacillus fermentum probiotic treatment on gut microbiota composition in sahiwal calves with rotavirus diarrhea: A 16S metagenomic analysis study".

BACKGROUND: Diarrhea poses a major threat to bovine calves leading to mortality and economic losses. Among the causes of calf diarrhea, bovine rotavirus is a major etiological agent and may result in dysbiosis of gut microbiota. The current study was designed to investigate the effect of probiotic Limosilactobacillus fermentum (Accession No.OR504458) on the microbial composition of rotavirus-infected calves using 16S metagenomic analysis technique. Screening of rotavirus infection in calves below one month of age was done through clinical signs and Reverse Transcriptase PCR. The healthy calves (n = 10) were taken as control while the infected calves (n = 10) before treatment was designated as diarrheal group were treated with Probiotic for 5 days. All the calves were screened for the presence of rotavirus infection on each day and fecal scoring was done to assess the fecal consistency. Infected calves after treatment were designated as recovered group. Fecal samples from healthy, recovered and diarrheal (infected calves before sampling) were processed for DNA extraction while four samples from each group were processed for 16S metagenomic analysis using Illumina sequencing technique and analyzed via QIIME 2. RESULTS: The results show that Firmicutes were more abundant in the healthy and recovered group than in the diarrheal group. At the same time Proteobacteria was higher in abundance in the diarrheal group. Order Oscillospirales dominated healthy and recovered calves and Enterobacterials dominated the diarrheal group. Alpha diversity indices show that diversity indices based on richness were higher in the healthy group and lower in the diarrheal group while a mixed pattern of clustering between diarrheal and recovered groups samples in PCA plots based on beta diversity indices was observed. CONCLUSION: It is concluded that probiotic Limosilactobacillus Fermentum N-30 ameliorate the dysbiosis caused by rotavirus diarrhea and may be used to prevent diarrhea in pre-weaned calves after further exploration.

The inhibitory effects of endophytic metabolites on glycated proteins under non-communicable disease conditions: A review.

Protein glycation in human body is closely linked to the onset/progression of diabetes associated complications. These glycated proteins are commonly known as advanced glycation end products (AGEs). Recent literature has also highlighted the involvement of AGEs in other non-communicable diseases (NCDs) such as cardiovascular, cancer, and Alzheimer's diseases and explored the impact of plant metabolites on AGEs formation. However, the significance of endophytic metabolites against AGEs has recently garnered attention but has not been thoroughly summarized thus far. Therefore, the objective of this review is to provide a comprehensive overview of the importance of endophytic metabolites in combating AGEs under NCDs conditions. Additionally, this review aims to elucidate the processes of AGEs formation, absorption, metabolism, and their harmful effects. Collectively, endophytic metabolites play a crucial role in modulating signaling pathways and enhancing the digestibility properties of gut microbiota (GM) by targeting on AGEs/RAGE (receptor for AGEs) axis. Furthermore, these metabolites exhibit anti-AGEs activities similar to those derived from host plants, but at a lower cost and higher production rate. The use of endophytes as a source of such metabolites offers a risk-free and sustainable approach that holds substantial potential for the treatment and management of NCDs.

Milk microbiota of Holstein Friesian cattle reared in Lahore: Association with mastitis.

The dairy industry is reshaping itself and becoming commercialized in Pakistan due to the increased demand for milk to overcome the shortage. Exotic breeds such as Holstein Friesian, a high milk producing breed have started being reared more on farms in Pakistan. Along with other issues, mastitis does affects the milk production of this breed. The objective of this study was to evaluate the milk composition in terms of bacterial communities in Holstein Friesian reared in Punjab, Pakistan and alteration in microbial composition with healthy and mastitic udder. Milk samples (n = 36) from farms rearing Holstein Friesian were collected. Among these samples, 05 samples from each three groups, HHF(healthy), CHF (clinical mastitis) and SHF (subclinical mastitis), based on their udder health condition, were processed using the 16 S r=RNA gene based technique. Diversity assessment as carried out by alpha diversity indices showed that milk samples from the udder infected with clinical mastitis were the least diverse and those from the healthy udder were more diverse. Beta diversity across samples showed a scattered pattern suggesting overlap amongst bacterial communities across different groups samples as depicted by PCA plots of beta diversity indices. The taxonomic profile revealed that Proteobacteria Firmicutes, Bacteroidota and Actinobacteriota were the major phyla detected across all groups. Proteobacteria dominated the HHF and SHF group while abundance of Firmicutes was higher in CHF group. Differences at other levels including order, genus and species were also recorded. The overall picture concludes that diverse microbiota is associated with different udder health conditions.

Toxicodynamic of combined mycotoxins: MicroRNAs and acute-phase proteins as diagnostic biomarkers.

Mycotoxins, ubiquitous contaminants in food, present a global threat to human health and well-being. Mitigation efforts, such as the implementation of sound agricultural practices, thorough food processing, and the advancement of mycotoxin control technologies, have been instrumental in reducing mycotoxin exposure and associated toxicity. To comprehensively assess mycotoxins and their toxicodynamic implications, the deployment of effective and predictive strategies is imperative. Understanding the manner of action, transformation, and cumulative toxic effects of mycotoxins, moreover, their interactions with food matrices can be gleaned through gene expression and transcriptome analyses at cellular and molecular levels. MicroRNAs (miRNAs) govern the expression of target genes and enzymes that play pivotal roles in physiological, pathological, and toxicological responses, whereas acute phase proteins (APPs) exert regulatory control over the metabolism of therapeutic agents, both endogenously and posttranscriptionally. Consequently, this review aims to consolidate current knowledge concerning the regulatory role of miRNAs in the initiation of toxicological pathways by mycotoxins and explores the potential of APPs as biomarkers following mycotoxin exposure. The findings of this research highlight the potential utility of miRNAs and APPs as indicators for the detection and management of mycotoxins in food through biological processes. These markers offer promising avenues for enhancing the safety and quality of food products.

Comparison of STONE Score, Guy's Stone Score, CROES Nomogram, and Seoul National University Renal Stone Complexity Score in Prognosticating Outcomes of Multiple-Tract Mini-Percutaneous Nephrolithotomy: A Retrospective Study.

Objective  The objective of this study was to compare the STONE score (Size of the stone, Topography or location, degree of Obstruction of the urinary system, Number of stones, and Evaluation of Hounsfield units), Guy's stone score (GSS), Clinical Research Office of the Endourological Society (CROES) nomogram, and Seoul National University Renal Stone Complexity Score (RSCS) in prognosticating multiple tract mini-percutaneous nephrolithotomy (mPCNL) outcome. Methodology This descriptive retrospective analysis was carried out at the Urology Department, Lady Reading Hospital, Peshawar, Pakistan. Male and female patients in the age range of 18-70 years who underwent multiple tract mPCNL for renal stones from July 1, 2021, to June 30, 2023, were included in the analysis. Results  A total of 110 patients were registered. Stone-free status (SFS) was achieved in 78.2% (n=86), and complications were observed in 13.6% (n=15) patients. The odds ratio for STONE score, GSS, CROES scoring system, and RSCS for predicting the SFS was 7.093 (95%CI 2.40-20.89), 9.333 (95%CI 2.92-29.81), 11.70 (95%CI 2.56-53.38) and 3.450 (95%CI 1.25-9.53), respectively. Conclusion  Multiple tract mPCNL is a safe and effective technique for the management of renal stones, producing a high stone-free rate. This study demonstrated adequate efficacy and dependability of the four scoring systems in predicting SFS.

Interface frictional anisotropy of dilative sand.

Understanding direction-dependent friction anisotropy is necessary to optimize interface shear resistance across soil-structure. Previous studies estimated interface frictional anisotropy quantitatively using contractive sands. However, no studies have explored how sand with a high dilative tendency around the structural surface affects the interface shear response. In this study, a series of interface direct shear tests are conducted with selected French standard sand and snakeskin-inspired surfaces under three vertical stresses (50, 100, and 200 kPa) and two shearing directions (cranial → caudal or caudal → cranial). First, the sand-sand test observes a higher dilative response, and a significant difference between the peak and residual friction angles (ϕpeak - ϕres = 8°) is obtained at even a lower initial relative density Dr = 40%. In addition, the interface test results show that (1) shearing against the scales (cranial shearing) mobilizes a larger shear resistance and produces a dilative response than shearing along the scales (caudal shearing), (2) a higher scale height or shorter scale length exhibits a higher dilative tendency and produces a higher interface friction angle, and (3) the interface anisotropy response is more pronounced during cranial shearing in all cases. Further analysis reveals that the interface friction angle and dilation angle are decreased with the scale geometry ratio (L/H). For L/H values between 16.67 and 60, the interface dilation angle varies between 9° and 4° for cranial first shearing and 3.9°-2.6° for caudal first shearing. However, the difference in dilation angle within the same shearing direction is less than 1°.

Revolutionizing Treatment Strategies for Autoimmune and Inflammatory Disorders: The Impact of Dipeptidyl-Peptidase 4 Inhibitors.

DPP4 (Dipeptidyl-peptidase 4) a versatile protease, emerges as a prominent player in soluble and membrane-bound forms. Its heightened expression has been intimately linked to the initiation and severity of diverse autoimmune diseases, spanning rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis (SSc), inflammatory bowel disease, autoimmune diabetes, and even SARS-CoV-2 infection. Operating as a co-stimulator of T cell activity, DPP4 propels T cell proliferation by binding adenosine deaminase (ADA), thereby augmenting the breakdown of adenosine-an influential inhibitor of T cell proliferation. However, the discovery of a wide range of DPP4 inhibitors has shown promise in alleviating these diseases' signs, symptoms, and severity. The available DPP4 inhibitors have demonstrated significant effectiveness in blocking DPP4 activity. Based on the characterization of their binding mechanisms, three distinct groups of DPP4 inhibitors have been identified: saxagliptin, alogliptin, and sitagliptin, each representing a different class. Elevated levels of angiotensin-converting enzyme 2 (ACE2) expression are associated with producing various coronavirus peptidases. With its anti-inflammatory properties, Sitagliptin may assist COVID-19 patients in preventing and managing cytokine storms. This comprehensive review delves into the burgeoning realm of DPP4 inhibitors as therapeutic interventions for diverse autoimmune diseases. With a discerning focus on their efficacy, the investigation sheds light on their remarkable capacity to alleviate the burdensome signs and symptoms intricately linked to these conditions.

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling.

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.