Human DDX6 regulates translation and decay of inefficiently translated mRNAs.

Recent findings indicate that the translation elongation rate influences mRNA stability. One of the factors that has been implicated in this link between mRNA decay and translation speed is the yeast DEAD-box helicase Dhh1p. Here, we demonstrated that the human ortholog of Dhh1p, DDX6, triggers the deadenylation-dependent decay of inefficiently translated mRNAs in human cells. DDX6 interacts with the ribosome through the Phe-Asp-Phe (FDF) motif in its RecA2 domain. Furthermore, RecA2-mediated interactions and ATPase activity are both required for DDX6 to destabilize inefficiently translated mRNAs. Using ribosome profiling and RNA sequencing, we identified two classes of endogenous mRNAs that are regulated in a DDX6-dependent manner. The identified targets are either translationally regulated or regulated at the steady-state-level and either exhibit signatures of poor overall translation or of locally reduced ribosome translocation rates. Transferring the identified sequence stretches into a reporter mRNA caused translation- and DDX6-dependent degradation of the reporter mRNA. In summary, these results identify DDX6 as a crucial regulator of mRNA translation and decay triggered by slow ribosome movement and provide insights into the mechanism by which DDX6 destabilizes inefficiently translated mRNAs.

Hybrid YSGOA and neural networks based software failure prediction in cloud systems.

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing.

The Optimal Model for Copy-Move Forgery Detection in Medical Images.

Background: Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery. Methods: The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. Results: The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. Conclusions: It concluded that our method for CMFD in the medical images was more accurate.

Guidelines for efficient nitrogen preservation in sewage sludge-based fertilizers.

This study explores sustainable methods to mitigate nitrogen (N) loss in agriculture amid rising food demands and limited arable land. It examines sewage sludge (SS) as an alternative to synthetic N fertilizers. SS is rich in nitrogen (4.21 ± 0.42 %) and phosphorus (3.60 ± 0.72 %), making it suitable for nutrient recovery and soil enhancement. Unfavorable sludge management methods result in the loss of 950,000 tons of nitrogen, meeting almost 10 % of the EU's nitrogen fertilization demand. This research evaluates SS treatment methods, including chemical conversion, thermal treatment, and biological composting, focusing on nitrogen conservation efficiency. Results show nitrogen loss during hydrolysis is minimized at pH 4 to 8 but increases significantly as ammonia (NH3) at pH 9 to 11, ranging from 4.2 % to 9 %. Neutralizing the hydrolysate is crucial; using solid KOH resulted in 13.5 % nitrogen loss, 11 times more than using slightly alkaline ash (1.22 %). Adding ash during drying reduced nitrogen emissions by 30 % compared to traditional drying at 105 °C. Improving the C/N ratio with food residues reduced nitrogen losses by 46.3 % during composting. These findings highlight the importance of pH control in chemical processes and temperature regulation in thermal treatments. Adding residues from other processes, such as biomass combustion waste, enhances SS processing conditions. Understanding nitrogen retention mechanisms is crucial for the environmental sustainability of SS usage. Efficient nitrogen retention strategies improve the fertilization value of SS and reduce its environmental footprint by lowering greenhouse gas emissions, particularly ammonia. Reducing nitrogen loss during SS treatment significantly lowers ammonia emissions, a major contributor to greenhouse gas emissions. These results help determine optimal methods for managing and processing SS to minimize emissions and increase agricultural usability.

Anticandidal applications of selenium nanoparticles biosynthesized with Limosilactobacillus fermentum (OR553490).

Candida albicans is one of the most dangerous pathogenic fungi in the world, according to the classification of the World Health Organization, due to the continued development of its resistance to currently available anticandidal agents. To overcome this problem, the current work provided a simple, one-step, cost-effective, and safe technique for the biosynthesis of new functionalized anticandidal selenium nanoparticles (Se NPs) against C. albicans ATCC10231 using the cell-free supernatant of Limosilactobacillus fermentum (OR553490) strain. The bacterial strain was isolated from yogurt samples available in supermarkets, in Damietta, Egypt. The mixing ratio of 1:9 v/v% between cell-free bacterial metabolites and sodium selenite (5 mM) for 72 h at 37 °C were the optimum conditions for Se NPs biosynthesis. Ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), X-ray diffraction (XRD), Zeta analyses, and elemental analysis system (EDS) were used to evaluate the optimized Se NPs. The Se NPs absorption peak appeared at 254 nm. Physicochemical analysis of Se NPs revealed the crystalline-shaped and well-dispersed formation of NPs with an average particle size of 17-30 nm. Se NPs have - 11.8 mV, as seen by the zeta potential graph. FT-IR spectrum displayed bands of symmetric and asymmetric amines at 3279.36 cm-1 and 2928.38 cm-1, aromatic and aliphatic (C-N) at 1393.32 cm-1 and 1237.11.37 cm-1 confirming the presence of proteins as stabilizing and capping agents. Se NPs acted as a superior inhibitor of C. albicans with an inhibition zone of 26 ± 0.03 mm and MIC value of 15 µg/mL compared to one of the traditional anticandidal agent, miconazole, which revealed 18 ± 0.14 mm and 75 µg/mL. The cytotoxicity test shows that Se NPs have a low toxic effect on the normal keratinocyte (IC50 ≈ 41.5 µg/mL). The results indicate that this green synthesis of Se NPs may have a promising potential to provide a new strategy for drug therapy.

Research on rural tourism environment perception based on grounded theory A case study of Beishan Village, Zhuhai City, Guangdong Province, China.

Drawing upon its regional culture, natural landscapes, and architectural features, Beishan Village in Zhuhai City, Guangdong Province, has forged a distinctive rural development model that combines an industrial park with tourism. While this approach has catalyzed rural progress, it also encounters numerous complex practical challenges. Utilizing the grounded theory method, this study employs participatory observation, in-depth interviews, and network data analysis to investigate the perspectives of three key stakeholder groups: villagers, tourists, and resident merchants. A model is constructed to capture their environmental perceptions of rural tourism. Based on an analysis of network and interview textual data, as well as the influencing factors identified through the theoretical model, this study proposes several optimization strategies. These include enhancing infrastructure development, cultivating a regional brand culture, strengthening the institutional management framework, establishing a rural sharing economy platform, and introducing digital tours. These strategies are designed to refine and enhance Beishan Village's development model, offering insights for similar villages and advancing the integration of beautiful China initiatives with urban and rural development efforts.

Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm.

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers.

Optimization of the extraction process for Shenshou Taiyi powder based on Box-Behnken experimental design, standard relation, and FAHP-CRITIC methods.

BACKGROUND: Ancient classic prescription play a crucial role in the preservation and advancement of traditional Chinese medicine (TCM) theories. They represent a significant milestone in the ongoing development and transmission of TCM knowledge and practices and are considered one of the breakthroughs in the development of TCM inheritance. In the process of developing ancient classic prescriptions, many problems may still arise in ensuring quality consistency between traditional methods and modern production processes, among which the extraction process poses major challenges. This paper introduces a practical approach extracting an ancient classic prescription using a modern extraction process. The technique is demonstrated through the study of the extraction process of Shenshou Taiyi powder (STP). METHODS: This study focuses on optimising the STP extraction process to ensure consistency in the quality of the product obtained through ancient and modern processes using the standard relation and fuzzy analytic hierarchical process (FAHP) and criteria importance through intercriteria correlation (CRITIC) method integrated weights combined with the Box-Behnken response surface test. Using the contents of rosmarinic acid, isoimperatorin, puerarin, as well as the extract yield and fingerprint similarity as evaluation indexes of STP, the Box-Behnken response surface method was employed to examine the varying extraction parameters, including water addition ratio, extraction duration, and number of extractions. The weighted coefficients for each parameter were calculated by combining the benchmark correlation and FAHP-CRITIC method, deriving a comprehensive score. RESULTS: The optimal extraction process for STP consisted of a two extractions, each using at a tenfold quantity of water, performed for one hour. Process verification across three separate batches yielded a comprehensive score of 94.7, with a relative standard deviation of 0.76%. CONCLUSIONS: The application of the Box-Behnken response surface method combined with standard relation and FAHP-CRITIC approach proved to be stable and feasible for optimising the extraction process of STP.

Optimization, gene cloning, expression, and molecular docking insights for enhanced cellulase enzyme production by Bacillus amyloliquefaciens strain elh1.

BACKGROUND: In this study, we isolated a cellulase-producing bacterium, Bacillus amyloliquefaciens strain elh, from rice peel. We employed two optimization methods to enhance the yield of cellulase. Firstly, we utilized a one-variable-at-a-time (OVAT) approach to evaluate the impact of individual physical and chemical parameters. Subsequently, we employed response surface methodology (RSM) to investigate the interactions among these factors. We heterologously expressed the cellulase encoding gene using a cloning vectorin E. coli DH5α. Moreover, we conducted in silico molecular docking analysis to analyze the interaction between cellulase and carboxymethyl cellulose as a substrate. RESULTS: The bacterial isolate eh1 exhibited an initial cellulase activity of 0.141 ± 0.077 U/ml when cultured in a specific medium, namely Basic Liquid Media (BLM), with rice peel as a substrate. This strain was identified as Bacillus amyloliquefaciens strain elh1 through 16S rRNA sequencing, assigned the accession number OR920278 in GenBank. The optimal incubation time was found to be 72 h of fermentation. Urea was identified as the most suitable nitrogen source, and dextrose as the optimal sugar, resulting in a production increase to 5.04 ± 0.120 U/ml. The peak activity of cellulase reached 14.04 ± 0.42 U/ml utilizing statistical optimization using Response Surface Methodology (RSM). This process comprised an initial screening utilizing the Plackett-Burman design and further refinement employing the BOX -Behnken Design. The gene responsible for cellulase production, egl, was effectively cloned and expressed in E. coli DH5α. The transformed cells exhibited a cellulase activity of 22.3 ± 0.24 U/ml. The egl gene sequence was deposited in GenBank with the accession number PP194445. In silico molecular docking revealed that the two hydroxyl groups of carboxymethyl cellulose bind to the residues of Glu169 inside the binding pocket of the CMCase. This interaction forms two hydrogen bonds, with an affinity score of -5.71. CONCLUSIONS: Optimization of cultural conditions significantly enhances the yield of cellulase enzyme when compared to unoptimized culturing conditions. Additionally, heterologous expression of egl gene showed that the recombinant form of the cellulase is active and that a valid expression system can contribute to a better yield of the enzyme.

A scenario based approach to optimizing cost-effectiveness of physician-staffed Helicopter Emergency Medical Services compared to ground-based Emergency Medical Services in Finland.

OBJECTIVES: Since Helicopter Emergency Medical Services (HEMS) is an expensive resource in terms of unit price compared to ground-based Emergency Medical Service (EMS), it is important to further investigate which methods would allow for the optimization of these services. The aim of this study was to evaluate the cost-effectiveness of physician-staffed HEMS compared to ground-based EMS in developed scenarios with improvements in triage, aviation performance, and the inclusion of ischemic stroke patients. METHODS: Incremental cost-effectiveness ratio (ICER) was assessed by comparing health outcomes and costs of HEMS versus ground-based EMS across six different scenarios. Estimated 30-day mortality and quality-adjusted life years (QALYs) were used to measure health benefits. Quality-of-Life (QoL) was assessed with EuroQoL instrument, and a one-way sensitivity analysis was carried out across different patient groups. Survival estimates were evaluated from the national FinnHEMS database, with cost analysis based on the most recent financial reports. RESULTS: The best outcome was achieved in Scenario 3.1 which included a reduction in over-alerts, aviation performance enhancement, and assessment of ischemic stroke patients. This scenario yielded 1077.07-1436.09 additional QALYs with an ICER of 33,703-44,937 €/QALY. This represented a 27.72% increase in the additional QALYs and a 21.05% reduction in the ICER compared to the current practice. CONCLUSIONS: The cost-effectiveness of HEMS can be highly improved by adding stroke patients into the dispatch criteria, as the overall costs are fixed, and the cost-effectiveness is determined based on the utilization rate of capacity.

Chitosan extraction from Amanita phalloides: yield, crystallinity, degree of deacetylation, azo dye removal and antibacterial properties.

Chitosan, a biopolymer obtained from chitin, is known for its remarkable adsorption abilities for dyes, drugs, and fats, and its diverse array of antibacterial characteristics. This study explores the extraction and characterization of chitosan from the mycelium of Amanita phalloides. The moisture content, ash content, water binding capacity, fat binding capacity, and degree of deacetylation of the extracted chitosan were determined. The chitosan exhibited a high yield of 70%, crystallinity of 49.07%, a degree of deacetylation of 86%, and potent antimicrobial properties against both Gram-negative and Gram-positive bacteria. The study also examined the adsorption capabilities of chitosan to remove methylene blue (MB) dye by analysing specific factors like pH, reaction time, and MB concentration using the response surface model. The highest degree of MB dye removal was 91.6% at a pH of 6, a reaction time of around 60 min and an initial dye concentration of 16 ppm. This experimental design can be applied for chitosan adsorption of other organic compounds such as dyes, proteins, drugs, and fats.

Advancing gasoline desulfurization: Multi-objective fuzzy optimization in systems technology.

Ultrasonic-assisted oxidative desulfurization (UAOD) is utilized to lessen environmental problems due to sulfur emissions. The process uses immiscible polar solvents and ultrasonic waves to enhance desulfurization efficiency. Prior research focused on comparing the effectiveness of UAOD for gasoline using response surface methodology. This study evaluates the desulfurization efficiency and operating costs, including ultrasonic power, irradiation time, and oxidant amount to determine optimal conditions. The study used a multi-objective fuzzy optimization (MOFO) approach to evaluate the economic viability of UAOD for gasoline. It identified upper and lower boundaries and then optimized the desulfurization efficiency and operating costs while considering uncertainty errors. The fuzzy model employed max-min aggregation to optimize the degree of satisfaction on a scale from 0 (unsatisfied) to 1 (satisfied). Optimal conditions for gasoline UAOD were found at 445.43 W ultrasonic power, 4.74 min irradiation time, and 6.73 mL oxidant, resulting in a 66.79 % satisfaction level. This yielded a 78.64 % desulfurization efficiency (YA) at an operating cost of 13.49 USD/L. Compared to existing literature, gasoline desulfurization was less efficient and less costly. The solutions provided by MOFO demonstrate not only economic viability through decreased overall operating costs and simplified process conditions, but also offer valuable insights for optimizing prospective future industrial-scale UAOD processes.

Utilizing AI and IoT technologies for identifying risk factors in sports.

A dynamic cooperation is poised to redefine the limits of athlete safety and performance optimization in the dynamic field of sports science. A new age in sports analysis is promised by the combination of artificial intelligence (AI) and the internet of things (IoT), one in which data-driven insights not only improve our comprehension of athletic performance but also aid to reduce hazards. This academic work explores the complex interactions between AI and IoT in the context of sports. The IoT and AI integration appear to be a strong mix that has the potential to redefine the standards for athlete safety and performance improvement. This study explores the complex interactions between AI and IoT in the field of sports, emphasizing their combined potential for identifying risk factors in a variety of fields. There is a chance to proactively solve sports-related difficulties by utilizing the data-driven capabilities of IoT and the analytical power of AI, opening the door for better informed tactics and decision-making. Through an exploration of this symbiotic relationship, this paper seeks to underline the transformative potential of these technologies in fostering a safer and more performance-oriented sports environment.

Lipoprotein (a) and lipid-lowering treatment from the perspective of a cardiac surgeon. An impact on the prognosis in patients with aortic valve replacement and after heart transplantation.

Lipoprotein(a) is a recognized risk factor for ASCVD. There is still no targeted therapy for Lp(a), however, drugs such as pelacarsen, olpasiran, zerlasiran, lepodisiran and muvalaplin are in clinical trials and have been shown to be effective in significantly reducing Lp(a) levels. Moreover, elevated Lp(a) levels significantly affect the prognosis of patients after aortic valve replacement (AVR) and heart transplantation (HTx). Therefore, the assessment of Lp(a) concentration in these patients will allow for a more accurate stratification of their cardiovascular risk, and the possibility of lowering Lp(a) will allow for the optimization of this risk. In this article, we summarized the most important information regarding the role of Lp(a) and lipid-lowering treatment in patients after AVR and HTx.

An in-depth study of the oxidative liquefaction process for polymeric waste reduction and chemical production from wind turbine blades and personal protective equipment used in the medical field.

An in-depth study of the oxidative liquefaction process has been provided to degrade the polymeric waste from personal protective equipment (PPEs) and wind turbine blades (WTBs). Thermogravimetric investigations demonstrate that WTBs have three prominent peaks throughout the degradation, whereas PPEs display solitary peak features. Experiments are carried out employing specific experimental design approaches, namely the Central Composite Face-Centered Plan (CCF) for WTBs and the Central Composition Design with Fractional Factorial Design for PPEs in a batch-type reactor at temperature ranges of 250-350 °C, pressures of 20-40 bar, residence times of 30-90 min, H2O2 concentrations of 15-45 %, and waste/liquid ratios of 5-25 % for WTBs. These values were 200-300 °C, 30 bar, 45 min, 30-60 % and 5-7 % for PPE. A detailed comparison has been provided in the context of total polymer degradation (TPD) for PPE and WTBs. Liquid products from both types of wastes after the oxidative liquefaction process are subjected to gas chromatography with flame ionization detection (GC-FID) to identify the existence of oxygenated chemical compounds (OCCs). For WTBs, TPD was 20-49 % and this value was 55-96 % for PPE while the OCC yield for WTBs (36.31 g/kg - 210.59 g/kg) and PPEs (39.93 g/kg - 212.66 g/kg) was also calculated. Detailed optimization of experimental plans was carried out by performing the analysis of variance (ANOVA) and optimization goals were maximum TPD and OCCs yields against the minimum energy consumption, though a considerable amount of complex polymer waste can be reduced and high concentrations of OCC can be achieved, which could be applied for commercial and environmental benefits.

Ammonia recovery via direct contact membrane distillation: Modeling and performance optimization.

Ammonia recovery from wastewater has positive environmental benefits, avoiding eutrophication and reducing production energy consumption, which is one of the most effective ways to manage nutrients in wastewater. Specifically, ammonia recovery by membrane distillation has been gradually adopted due to its excellent separation properties for volatile substances. However, the global optimization of direct contact membrane distillation (DCMD) operating parameters to maximize ammonia recovery efficiency (ARE) has not been attempted. In this work, three key operating factors affecting ammonia recovery, i.e., feed ammonia concentration, feed pH, and DCMD running time, were identified from eight factors, by a two-level Plackett-Burman Design (PBD). Subsequently, Box-Behnken design (BBD) under the response surface methodology (RSM) was used to model and optimize the significant operating parameters affecting the recovery of ammonia though DCMD identified by PBD and statistically verified by analysis of variance (ANOVA). Results showed that the model had a high coefficient of determination value (R2 = 0.99), and the interaction between NH4Cl concentration and feed pH had a significant effect on ARE. The optimal operating parameters of DCMD as follows: NH4Cl concentration of 0.46 g/L, feed pH of 10.6, DCMD running time of 11.3 h, and the maximum value of ARE was 98.46%. Under the optimized conditions, ARE reached up to 98.72%, which matched the predicted value and verified the validity and reliability of the model for the optimization of ammonia recovery by DCMD process.

Medical image registration via neural fields.

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Use of computational modeling to examine fingertip force production in children with hemiplegic cerebral palsy.

Most children with hemiplegic cerebral palsy (HCP), one of the most prevalent subtypes of cerebral palsy, struggle with grasping and manipulating objects. This impairment may arise from a diminished capacity to properly direct forces created with the finger pad due to aberrant force application. Children with HCP were asked to create maximal force with the index finger pad in the palmar (normal) direction with both the paretic and non-paretic hands. The resulting forces and finger postures were then applied to a computational musculoskeletal model of the hand to estimate the corresponding muscle activation patterns. Subjects tended to create greater shear force relative to normal force with the paretic hand (p < 0.05). The resultant force was directed 33.6°±10.8° away from the instructed palmar direction in the paretic hand, but only 8.0°±7.3° in the non-paretic hand. Additionally, participants created greater palmar force with the non-paretic hand than with the paretic hand (p < 0.05). These differences in force production are likely due to differences in muscle activation pattern, as our computational models showed differences in which muscles are active and their relative activations when recreating the measured force vectors for the two hands (p < 0.01). The models predicted reduced activation in the extrinsic and greater reductions in activation in the intrinsic finger muscles, potentially due to reduced voluntary activation or muscle atrophy. As the large shear forces could lead to objects slipping from grasp, muscle activation patterns may provide an important target for therapeutic treatment in children with HCP.

Multifaceted Interventions Inclusive of Medication Optimization for Frailty in Aged Care: A Systematic Review.

OBJECTIVES: Implementation of best practice frailty guidelines in residential aged care is currently unclear, and there is a particular scarcity of evidence regarding multifaceted frailty treatments inclusive of medication optimization in these settings, despite the bidirectional relationship between polypharmacy and frailty. This review aimed to retrieve all relevant literature and evaluate the effect of medication optimization delivered in conjunction with exercise and/or nutritional interventions in the best-practice management of frailty in residential aged care. DESIGN: Systematic review with a qualitative synthesis. SETTINGS AND PARTICIPANTS: Older adults residing within residential aged care (otherwise referred to as nursing homes or long-term care). METHODS: The protocol was prospectively registered on PROSPERO (Reg. No.: CRD42022372036) using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Five electronic databases were searched from inception to November 23, 2023, with alerts monitored until March 28, 2024. Quality of studies was assessed using the ROB 2 and ROBIN-1 tools. RESULTS: A total of 10,955 articles were retrieved; 62 full articles were reviewed, with 3 studies included (2 randomized controlled trials and 1 nonrandomized controlled trial) involving 1030 participants. Included studies did not use specific frailty scores but reported individual components of frailty such as weight loss or number of medications prescribed. No trial combining medication review, exercise, and nutrition was identified. Medication review reduced the number of medications prescribed, whereas the use of nutritional support reduced gastrointestinal medication and maintained weight. CONCLUSION AND IMPLICATIONS: There is no published research investigating best-practice guidelines for medication optimization used in combination with both exercise and nutrition in aged care to address frailty. This review confirms the need for studies implementing Consensus Guidelines for frailty treatment in this vulnerable cohort.

L-asparaginase producing ability of Aspergillus species isolated from tapioca root soil and optimized ideal growth parameters for L-Asparaginase production.

This research was designed to isolate the predominant L-asparaginase-producing fungus from rhizosphere soil of tapioca field and assess the suitable growth conditions required to produce maximum L-asparaginase activity. The Aspergillus tubingensis was identified as a predominant L-asparaginase producing fungal isolate from 15 isolates, and it was characterized by 18S rRNA sequencing. The L-asparaginase-producing activity was confirmed by pink color zone formation around the colonies in modified Czapek Dox agar plate supplemented with 1% L-asparagine. The optimal growth conditions required for the L-asparaginase production by A. tubingensis were optimized as pH 6.0, temperature 30°C, glucose as carbon source, 1.5% of L-asparagine, ammonium sulphate as nitrogen source, rice husk as natural L-asparagine enriched source, and 8 days of the incubation period. The L-asparaginase activity from A. tubingensis was excellent under these optimal growth conditions. It significantly used rice husk as an alternative to synthetic L-asparagine. As a result, this may be considered a sustainable method of converting organic waste into valuable raw material for microbial enzyme production.