Ibuprofen is a propionic acid derivative, a non-steroidal anti-inflammatory drug with a very effective anti-inflammatory and analgesic action and an significant antipyretic effect. It is a non-selective strong inhibitor of both Cyclooxygenase isoforms, Cyclo-oxygenase-1 (COX-1) and Cyclooxygenase-2 (COX-2). The main purpose of this research was to establish the accuracy of the official Ibuprofen amount calculated on the pharmaceutical tablet, according to the Rules of Good Pharmaceutical Practice provided by the European and Romanian Pharmacopoeias. Ibuprofen real concentrations can also be exactly found from various unknown pharmaceutical samples, according to the Official Pharmacopoeias Rules. Color reaction occurred quantitatively between Ibuprofen with alpha-naphthylamine ethanolic solution 0.1% , sodium nitrite NaNO₂ aqueous solution 4-5% , by heating at a high temperature of 70-75ºC , which led to the quantitative synthesis of an intense orange-yellowish azo dye that was spectrophotometrically analyzed at its maximum assigned absorption wavelength, λ = 460 nm. The pure Ibuprofen amount on the pharmaceutical tablet was calculated at λ.=460 nm for both pharmaceuticals and was found to be 611,750 mg, assigned to a mean percentage content of 101,958% active substance for the first pharmaceutical product, very close to the officially declared amount (600 mg), with a average percentage error of only 1,958 % in addition to the stated active substance content. For the second pharmaceutical product, pure Ibuprofen content was calculated and assigned to 394,774 mg for a mean percentage content of 98,694% , close enough to the official declared amount (400 mg). The average percentage error was only 1,307 % less than the officially declared active substance content. The Ibuprofen amounts found in both pharmaceuticals were within the normal range of values, located below ± 5%, which is the official maximum percentage deviation mentioned in European and Romanian Pharmacopoeias. Ultimately, both spectrophotometric methods were statistically validated.

Background: Wound management is crucial in healthcare with effective, natural healing agents. Lantana montevidensis is known for its pharmacological properties, particularly in wound healing. This involves creating an extract from L. montevidensis flowers using water as solvent and putting it into a biocompatible matrix to form a patch.

Aim and Objective: This study aims to formulate a herbal wound healing patch using a Lantana montevidensis extract and evaluate its efficacy in promoting wound healing through in vitro studies.

Methodology: Herbal wound healing patches were prepared using the solvent casting method, with starch, sodium alginate, and glycerin as plasticizers. The plant extract was obtained by decoction, with water as the solvent. The patches' mechanical strength was assessed via folding endurance tests, and their compatibility was evaluated via an FTIR spectral analysis. In vitro evaluations included anti-inflammatory, antimicrobial, and hemocompatibility assessments, with stability tests conducted at accelerated temperatures.

Results and Discussion: The chemical analysis revealed the yield of active constituents. The IR spectral analysis identified no incompatibility in the distinctive wavenumber regions of the starch and sodium alginate films. The patches' swelling index and stability at room temperature were investigated. Their biocompatibility was confirmed via hemolysis ratio and pH measurements. The albumin denaturation inhibition of the herbal patches was assessed, indicating their potential wound healing properties.

Conclusion: The herbal wound healing patch developed using an L. montevidensis flower extract demonstrated promising efficacy in wound healing, suggesting its potential as natural remedy in wound management.

This study presents a systematic approach to developing novel antidiabetic dosage forms utilizing Vigna mungo (VM) polymer microbeads fabricated via the ionotropic gelation method. The Vigna mungo (VM) polymer was extracted and isolated from the seeds of Vigna mungo using a non-solvent induced precipitation method. The VM biopolymer's intrinsic biodegradability and widespread availability render it an appealing candidate for sustainable pharmaceutical development. The formulation and characterization of the VM polymer in various proportions are delineated, alongside the preparation of uniform microbeads through ionotropic gelation. A 2:1 proportion of sodium alginate and VM was the initial concentration for the microbeads' formulation. Characterization via scanning electron microscopy and Fourier transform infrared spectroscopy ensured their uniformity and structural integrity. Incorporating Vildagliptin, a model antidiabetic drug, into these microbeads enabled assessment of their morphological characterization, drug loading efficiency, release kinetics, and stability under simulated physiological conditions. In vitro drug release studies exhibited 12 hours of drug release, which is appropriate for maintaining extended drug release and meeting the therapeutic objectives for diabetes. Evaluation through in vitro studies, alongside the biocompatibility and biodegradability assessments, underscored the safety and sustainability of the VM polymer microbeads. Overall, this study underscores the potential of VM biopolymers as versatile excipients in antidiabetic formulations, offering promising avenues for addressing the global diabetes burden through innovative and sustainable therapeutic interventions.

Introduction: Osteoarthritis (OA) is a major cause of disability, often necessitating knee replacement surgery. The longevity and effectiveness of knee implants are crucial, with loosening being a primary cause of joint replacement failure, often induced by osteolysis from wear debris on polyethylene articulating surfaces. This research focuses on enhancing the durability and performance of knee liners, critical components in knee prosthetics. This study evaluates the durability of twenty retrieved knee liners made of cross-linked, ultra-high-molecular-weight polyethylene (UHMWPE) and correlates damage patterns with stress development. Additionally, an upscaled knee liner design is introduced. Methods: The retrieved UHMWPE knee liners underwent rigorous assessment using four in vivo damage assessment methods. Optical and confocal microscopy techniques were employed to quantify wear characteristics. Computer-aided drawing (CAD) facilitated finite element analysis (FEA), correlating FEA outcomes with surface evaluations. An upscaled knee liner design was introduced and evaluated using ANSYS and fatigue life prediction models, optimizing design parameters in SolidWorks. Results: This study shows advancements in the structural integrity, performance, and optimization of knee liners. Correlations between FEA outcomes, surface evaluations, and gait analysis provide comprehensive insights. The integrated approach, combining in vivo damage assessment and computational simulation, proves effective in advancing knee prosthesis design. Conclusion: Linking damage patterns with stress development is crucial, with computational simulations playing a key role in validating techniques. The upscaled knee liner design and advanced fatigue life prediction models demonstrate potential for enhancing knee prosthesis durability and performance, ultimately benefiting patients undergoing knee replacement surgeries.

Molecular Diversity Analysis of Sweet Sorghum [Sorghum Bicolor (L.) Moench] Collections of Ethiopia as revealed by Microsatellite Markers

Melkamu Genet¹*

¹Department of Plant Biotechnology, Jimma University, Jimma, Ethiopia,

^*Corresponding author e-mail: melkamugenet3@gmail.com

ABSTRACT

Sweet sorghum (Sorghum bicolor (L.) Moench) is the only grain and stalk crop that can be used for multipurpose. The assessment of genetic diversity and population structure is relevant to exploiting the genetic potential of the crop. This study was aimed to assess the genetic diversity and population structure of selected 82 Ethiopian sweet sorghum accessions using 10 simple sequence repeat (SSR) markers that represent seven geographic regions of Ethiopia. The study revealed a total of 116 alleles with a mean of 11.6 alleles per locus. All used microsatellite loci were highly polymorphic with polymorphic information content (PIC) ranging from 0.75 to 0.90 with an average of 0.82. They showed high gene diversity ranging from 0.59 to 0.81 with an overall mean of 0.70. There was a moderate genetic differentiation (FST=0.21) showing the presence of high gene flow (Nm= 5.033) where 91% of the total variation was accounted for within populations genetic. The clustering, principal coordinate analysis (PCoA) and population structure did not cluster the studied populations into a separate group according to their geographical origin. In conclusion, the highest intra-population diversity was observed among populations of North Wollo (He= 0.81) and South Wollo (He= 0.79), and hence these areas can be considered as hot spots for the identification of novel traits. Therefore, the present study has generated baseline information for breeders to improve Ethiopian sweet sorghum through breeding, management, and conservation of the available genetic resource.

Objective: Deep learning has significantly transformed time series analysis, particularly for long and complex datasets. While traditional methods suffice for simpler and shorter time series, advanced deep learning algorithms excel in handling intricate patterns. Our study focuses on evaluating the performance of these models in analysing complex time series patterns, using vital signs during sleep as a compelling example. Monitoring vital signs with time series forecasting enables early detection of sleep disorders, leading to faster intervention and better treatment outcomes.

Methods: We evaluated three forecasting models: ARIMA (Autoregressive Integrated Moving Average), a statistical method used for forecasting time series; LSTM (Long Short-Term Memory), a recurrent neural network architecture well suited for handling sequential data; and TFT (Temporal Fusion Transformer), a state-of-the-art deep learning model utilizing attention mechanisms. Our dataset consisted of nocturnal ECGs of 35 individuals, from the Physionet Apnea-ECG database. We used the Pan--Tompkins Algorithm to extract the heart rate from the ECGs and interpolated the results for evenly spaced time series forecasting.

Results: The ARIMA, LSTM, and TFT models were compared in forecasting heart rate data derived from ECG signals during sleep. Our evaluation focused on forecasting the next two minutes of heart rate data based on the past 30 minutes of observations. The ARIMA model achieved a mean absolute error (MAE) of 6.1 beats per minute (bpm) and a root mean squared error (RMSE) of 7.8 bpm. The LSTM model outperformed ARIMA, demonstrating a lower MAE of 4.3 bpm and RMSE of 5.9 bpm. The TFT model, leveraging attention mechanisms and deep learning, showcased the best performance with an MAE of 3.8 bpm and RMSE of 4.7 bpm.

Conclusion: Comparatively, the TFT model exhibited superior forecasting accuracy over both ARIMA and LSTM models, indicating its efficacy in capturing the complex dynamics. These results underscore the potential of advanced deep learning techniques in enhancing time series forecasting.

Osteoarthritis (OA) significantly alters the microenvironment of the knee, increasing inflammation and reducing lubricity. Consequently, OA can result in debilitating pain that necessitates a total joint replacement. Here, we developed super-lubricious hydrogel microspheres to reduce friction and inflammation within the synovium. Hydrogel microspheres were fabricated using polyethylene glycol and subsequently coated with a custom-synthesized copolymer. This copolymer consisted of monomers of dopamine methacrylate (DMA), which provides the microspheres with strong attachment properties, and sulfobetaine methacrylate (SBMA), which provides lubrication due to its zwitterionic nature. The optimization of DMA:SBMA ratios, as well as the copolymers' arrangement (block vs. random copolymer), is currently underway to balance microsphere adhesion and lubricity. Microsphere lubricity was tested using a custom-built tribo-rheometer that enables the quantification of friction coefficients for small sample volumes (140 µL compared to 500 µL for standard tribology set-ups). Copolymer-coated microspheres demonstrated lower friction compared to uncoated microspheres, as well as the synovial fluid derived from patients with varying degrees of osteoarthritis or knee ligament tears. Additionally, the coated microspheres were shown to be injectable, allowing for facile in situ delivery. When injected intra-articularly into the knee capsule of healthy mice, the microspheres persisted for over 10 days and did not cause pain or interfere with daily mice activities. Our current work is focused on loading the microspheres with disease-modifying therapeutic molecules, such as platelet-rich plasma (PRP), and testing those in mice models of OA.

Introduction: PMA1 H⁺-ATPase is the key enzyme of yeast metabolism. It belongs to P-ATPases, also including mammalian K⁺,Na⁺-, H⁺, K⁺-, Ca²⁺- and other metal-transporting ATPases. This pump couples ATP hydrolysis to transport H⁺ ions across the plasma membrane, thus generating electrochemical proton gradient. The enzyme is anchored in the lipid bilayer by ten hydrophobic segments (M1-M10), which form a membrane domain that carries H⁺-translocating sites. This work aimed to study the role of M6 amino acid residues in the structure-function relationships in PMA1 ATPase.

Methods: We used Ala-scanning mutagenesis to examine the functional role of amino acid residues throughout M6 of the PMA1 H⁺-ATPase. The yeast strains SY4 and NY13 were employed for the enzyme expression in secretory vesicles (SV) and plasma membranes (PM), respectively. In SV, mutant proteins were expressed from plasmid pma1 gene under the heat shock-inducible promoter at 39^oC and in PM, from the chromosomal PMA1 gene at permissive temperature. SV and PM were isolated to measure the expression and ATPase activity.

Results and Discussion: Nearly half of the SV mutants possessed sufficient activity and expression levels for further investigation. The majority of them exhibited abnormalities in kinetics and/or H⁺-transport. The rest of the mutations led to a loss of activity and/or blockage in biogenesis. Given that heat shock may affect PMA1 biogenesis, the inactive mutants were integrated into the chromosomal copy of the PMA1 gene. All but one mutant (F728A) were unable to support growth. F728A was expressed and exhibited activity close to the wild-type level. However, the F728A ATPase ability for glucose-dependent activation dropped almost twice.

Conclusion: M6 is a very important segment in maintaining enzyme structure-function relationships. Further studies of the substitutions' effect will help to reveal details of the mechanism of PMA1 H⁺-ATPase regulation/functioning.

Introduction. Chronic wounds are a serious public health problem. Infection by antibiotic-resistant pathogens poses the primary risk. In this study, an antibacterial dressing material was made from electroformed polycaprolactone (PCL) fibers that had zinc oxide nanoparticles (ZnO NPs) and chlorhexidine (CHG) attached to their surface.

Materials and methods. The series of dressings were obtained by electrospinning with the incorporation of ZnO NPs at different concentrations (1, 3, and 5%), followed by the immobilization of CHG on the surface. To increase the antipathogen activity, the samples were plasma-treated in a mixture of CO2/C2H4/Ar gas following CHG immobilization by carbodiimide chemistry. We analyzed the structure and chemical composition of the obtained materials using SEM, EDX, XPS, and FTIR spectroscopy. We evaluated the antipathogenic activity against several bacterial and fungal strains. We evaluated the biocompatibility of the samples with a human fibroblast cell line.

Results. The size of the produced ZnO NPs varies between 9 and 13 nm. The composite fibers have a size that varies between 100 nm and 500 nm. EDS analysis confirms that the main components of the fibers are carbon, oxygen, and zinc. The atomic concentration increases to 1.1%, 2.7%, and 3.9%, respectively, as the introduced wt% of ZnO increases. FTIR spectroscopy and XPS analysis confirmed the successful introduction of the ZnO NPs and the subsequent addition of CHG. Measurement of the wetting edge angles of the composite fibers reveals that the material's surface is hydrophilic. Further PCL modification with carboxyl groups and CHG leads to an improvement in the material's wettability. The results of the biological tests confirm that the developed dressings have promising bactericidal and proliferative activity.

Conclusions. We have created a series of new modified composite fiber materials with high potential for wound healing. This research was funded by the Russian Science Foundation (№24-79-10121).

Introduction: Spinal muscular atrophy (SMA) is a genetic disorder caused by mutations in the SMN1 gene, leading to a deficiency of survival motor neuron (SMN) protein. The human genome contains a paralog of the SMN1 gene, SMN2, which, due to a splicing defect, produces insufficient levels of functional SMN protein. Therapeutic strategies aiming to correct this splicing defect in SMN2 are supposed as treatments for SMA. The disadvantage of this therapy is intrathecal administration with associated side effects. Ternary oligonucleotide--peptide complexes coated by anionic polypeptide are designed to overcome the impossibility of systemic delivery antisense RNA oligonucleotide.

Methods: The study involved transfecting SMA fibroblast cell culture with the serum-stable oligonucleotide--peptide complexes, reverse transcription, semiquantitative PCR, and resazurin assay.

Results and Discussion: A significant increase was observed in the proportion of full-length SMN transcripts after therapeutic antisense RNA oligonucleotide delivery. Interpolyelectrolyte oligonucleotide--peptide complexes showed stability in the serum in contrast to cationic peptide complexes. The toxicity of the complexes remained within acceptable levels.

Conclusions: The delivery of antisense RNA oligonucleotides using interpolyelectrolyte complexes represents a promising strategy for the treatment of SMA. This strategy combines the specificity of antisense oligonucleotides with the protective and delivery-enhancing properties of interpolyelectrolyte complexes, potentially offering a more effective and sustained therapeutic option for SMA treatment.