Simple Program Research Articles

P-1754. An Antimicrobial Stewardship Program (ASP) Without Antibiotic Restrictions - Performance Update

Abstract Background In 2011, University Medical Center of Southern Nevada (UMC) in Las Vegas presented introductory results of a simple, yet effective ASP program with the unique strategy of not restricting antibiotics. Since this time, ASP has continued to strive towards appropriate antimicrobial use in an attempt to improve patient care and minimize development of resistance. This project is an update of the progress of this ASP program in terms of resistance, antibiotic use and costs. Methods ASP started January 2008 with minimal resources. Baseline activities included targeted medication use evaluation (MUE), duration regulation, expansion of infection prevention and education. In contrast to many ASP programs our program did not restrict antibiotics – formulary agents were available for any practitioner. Over time activities were adjusted, reporting technologies advanced although personnel resources remained limited. ASP modifications:Added MUE agentsTiered sensitivity reportingReduce duration to 7 daysExpand service to 6 days per week Deviating from initial procedures, in 2015 we implemented restriction of 5 “last-line” antimicrobials to UMC employed Infectious Disease Physicians (table 1) using the EHR. Markers of ASP progress was monitored by rates of organism isolation per 1000 patient days and antimicrobial in days of therapy per 1000 patient days. Financial implications were based on antibiotic expenditures per adult patient day. Results Monitored bacterial pathogens decreased 65 – 94% (table 2). The largest reduction was with Acinetobacter sp. followed by carbapenem resistant P. aeruginosa. Antibiotic use also improved (table 3). Overall use was decreased 71.3 ± 14.7% with the largest changes with fluoroquinolones and piperacillin/tazobactam. Of note, not all antibiotics reported are MUE, and use of these agents still improved. Despite expanding formulary, and cost monitoring activities, antibiotic expenditures decreased 47% per adult patient day. Conclusion ASP at UMC has demonstrated that simple programs, with limited resources, can decrease the use of unnecessary antimicrobial agents and isolation of specific bacterial pathogens. Additional benefits include a decrease in hospital costs without requiring significant restriction of antimicrobial agents. Disclosures Kimberly D. Leuthner, PharmD, FIDSA, AbbVie Inc.: Advisor/Consultant|AbbVie Inc.: Honoraria|Basilea Pharamceutica: Advisor/Consultant|La Jolla Pharmaceutical Company: Honoraria|Merck: Advisor/Consultant|Merck: Honoraria|Shionogi, Inc: Honoraria

Design and assessment of a musculoskeletal injury prevention programme for wind players

Background The cervical and mandibular regions are main areas of occurrence of pain in wind players. Objective The aim of this study was to describe the design and to find out the level of satisfaction of a specific exercise programme for cervical and mandibular musculature and mobility in wind players. Methods The programme was divided into two phases: the first one of education and the second one of implementation. The exercises included were divided into warm-up exercises (including cervical and jaw muscle mobility and activation, as well as postural awareness) and cool-down exercises (stretching and relaxing the main muscles in these areas). The programme was 12 weeks long. Participants were wind musician students. Results Twenty-seven students completed the pre- and post-intervention assessment, of which 13 completed the exercises. Considering the total sample, the described difficulty was low and satisfaction was high. The musicians who performed the programme indicated a high importance in instrumental practice and injury prevention, as well as ease of implementation. Conclusions Satisfaction with the exercise programme has been high. The fact that it is short, easy to perform and can be done by everyone makes it a quick and simple programme for everyone to do.

POSSIBILITIES OF NEURAL NETWORKS FOR LEARNING FOREIGN LANGUAGES IN HIGHER EDUCATIONAL INSTITUTIONS

The article analyses the possibilities of using neural networks in teaching foreign languages in higher education institutions. It is determined that a neural network is a computer program that works in accordance with the human brain, i.e., it passes input data through a system of "neurons" - simpler programs that interact with each other, and then produces a certain calculation result based on this interaction, taking into account the experience and errors from previous runs of the programme. Neural networks are based on a mathematical model that can mimic the functioning of biological neurons, i.e. the principles by which the human brain works and is able to learn and absorb new information. Accordingly, the educational process was not ignored. Today, this new tool in the educational process is rapidly being used in the study of foreign languages in higher education institutions. At the moment, there are such neural networks as Chat GPT, Twee, MagicSchool.ai, ReadyTo, Curipod. The article notes that neural networks open up new opportunities for learning and teaching foreign languages in higher education. Besides, chatbots are one of the examples of neural networks. The author notes that a chatbot is a specially adapted network that has the ability to simulate dialogues with real people, answer questions, search, analyse and modify information according to the user's request or communicate in a user-specified manner. In this article, the author discusses the advantages and disadvantages of chatbots. The main disadvantage is that chatbots make mistakes and cannot replace natural communication through non-verbal means. Nevertheless, neural networks are very lightweight, simple and easy-to-use tools that do not require special skills to work with. In addition, they are great helpers for teachers in creating and preparing materials for classes that are original, interesting and motivating for students in learning a foreign language.

Effect of vitamin D3, omega-3 fatty acids, and exercise on serum sclerostin levels and bone turnover markers.

Sclerostin inhibits canonical Wnt signaling, a pathway promoting bone formation. The effects of vitamin D3, omega-3 fatty acids (omega-3s), and exercise on serum sclerostin levels and bone metabolism are unclear. To investigate the effects of 2000 IU/d vitamin D3, 1g/d omega-3s, and a simple home-based strength exercise program (SHEP), alone or in combination, on serum sclerostin and bone turnover marker levels. Sclerostin, procollagen type 1 N propeptide (P1NP) and C-terminal telopeptide (β-CTx) levels were pre-defined secondary outcomes of DO-HEALTH, a double blind, randomized controlled trial in healthy physically active older adults in five European countries. Changes in yearly serum sclerostin, P1NP and β-CTx levels over 3 years, adjusted for age, sex, prior falls, study site, baseline BMI, and baseline level of the respective outcome. 1,848 participants were included (mean age 74.8 ± 4.4 years, 58.9 % women, 41.4 % 25(OH)D < 20 ng/mL, 83.9 % at least moderately physically active at baseline). Vitamin D3 and omega-3s supplementation alone did not change sclerostin levels significantly, while SHEP compared with control exercise (joint mobility) led to greater decrease in sclerostin levels [-1.56 pmol/L (-2.54, -0.58), p=0.002]. Omega-3s plus SHEP led to a greater decrease in sclerostin levels compared to no omega-3s/control exercise [-1.93pmol/L (-3.31, -0.54), p=0.007]. For P1NP and β-CTx there were no significant effects for any of the individual treatments and treatment combinations. In this 3-year prevention trial among largely vitamin D replete adults age 70 and older, SHEP alone or in combination with omega-3s reduced serum sclerostin levels, while vitamin D3 and omega-3s alone did not affect serum sclerostin levels.

Evaluation of a Computer-Based Morphological Analysis Method for Free-Text Responses in the General Medicine In-Training Examination: Algorithm Validation Study.

The General Medicine In-Training Examination (GM-ITE) tests clinical knowledge in a 2-year postgraduate residency program in Japan. In the academic year 2021, as a domain of medical safety, the GM-ITE included questions regarding the diagnosis from medical history and physical findings through video viewing and the skills in presenting a case. Examinees watched a video or audio recording of a patient examination and provided free-text responses. However, the human cost of scoring free-text answers may limit the implementation of GM-ITE. A simple morphological analysis and word-matching model, thus, can be used to score free-text responses. This study aimed to compare human versus computer scoring of free-text responses and qualitatively evaluate the discrepancies between human- and machine-generated scores to assess the efficacy of machine scoring. After obtaining consent for participation in the study, the authors used text data from residents who voluntarily answered the GM-ITE patient reproduction video-based questions involving simulated patients. The GM-ITE used video-based questions to simulate a patient's consultation in the emergency room with a diagnosis of pulmonary embolism following a fracture. Residents provided statements for the case presentation. We obtained human-generated scores by collating the results of 2 independent scorers and machine-generated scores by converting the free-text responses into a word sequence through segmentation and morphological analysis and matching them with a prepared list of correct answers in 2022. Of the 104 responses collected-63 for postgraduate year 1 and 41 for postgraduate year 2-39 cases remained for final analysis after excluding invalid responses. The authors found discrepancies between human and machine scoring in 14 questions (7.2%); some were due to shortcomings in machine scoring that could be resolved by maintaining a list of correct words and dictionaries, whereas others were due to human error. Machine scoring is comparable to human scoring. It requires a simple program and calibration but can potentially reduce the cost of scoring free-text responses.

Strategi Meningkatkan Kesadaran Menabung pada Anak-Anak Sejak Dini di Dusun II Desa Pantai Labu Kecamatan Pantai Labu

UMSU KKN activities in Dusun II, Pantai Labu Village focus on developing a simple financial education program for elementary school students through the concept of saving. This program is motivated by the importance of instilling the habit of saving from an early age to form a wise young generation in managing finances. One of the innovations implemented is the Savings Challenge, which is designed to increase students' motivation to save consistently. This competition involves all students, where they compete to save with a certain target during a predetermined period. The purpose of this program is to teach students about the importance of saving regularly and introduce the concept of simple financial planning. The method used is a participatory approach, where students are actively involved through games, stories, and group saving challenges. In addition, collaboration with teachers and parents is also an important part in ensuring the sustainability of the program. The results of this program show an increase in students' motivation to save, both at school and at home. Children not only understand the importance of saving, but are also motivated to achieve their short-term financial targets. In addition, this program also received support from teachers and parents, who felt a positive impact on students' behavior in managing their money.

Effect of vitamin D, omega-3 supplementation, or a home exercise program on muscle mass and sarcopenia: DO-HEALTH trial.

We aimed to investigate the effect of daily supplemental vitamin D, omega-3s, and a thrice-weekly home exercise program, alone or in combination, on change of appendicular lean muscle mass index (ALMI) and incident sarcopenia in older adults. This is a secondary endpoint analysis of a 3-year randomized, double-blind, placebo-controlled trial with a 2 × 2 × 2 factorial design among 2157 community-dwelling, healthy adults aged 70 + years, from 2012 to 2018 (DO-HEALTH). Participants were randomized to 2000 IU/d vitamin D and/or 1 g/d marine omega-3s and/or exercise. Change in ALMI over 3 years was calculated in all participants who underwent dual energy X-ray absorptiometry (DXA) (n = 1495) using mixed effect models. Incident sarcopenia was analyzed based on the Sarcopenia Definitions and Outcomes Consortium in all non-sarcopenic participants (n = 1940). Among 1495 participants (mean age 74.9 (sd 4.4); 63.3% were women; 80.5% were at least moderately physically active at baseline) mean gait speed at baseline was 1.2 m/s (sd 0.3), mean ALMI at baseline was 6.65 (SD 0.95) in women, and 8.01 (SD 0.88) kg/m2 in men. At year 3, average change of ALMI was -0.09 (sd 0.34) kg/m2 (-1.35%) in women and - 0.17 (sd 0.33) kg/m2 (-2.0%) in men. None of the treatments individually or in combination had a benefit on ALMI change compared to control over 3 years, with omega-3s showing a small protective effect on ALMI at year 1 only (-0.021 vs. no-omega-3s -0.066 kg/m2, p = 0.001). Of 1940 non-sarcopenic participants at baseline, 88 (4.5%) developed incident sarcopenia over 3 years. None of the treatments individually or in combination reduced the odds of incident sarcopenia compared with placebo. Among healthy, physically active older adults, ALMI and incidence of sarcopenia were not improved by treatment of daily 2000 IU vitamin D, daily 1 g omega-3s, or a simple home exercise program compared with control over 3 years.

Stochastic Processes with Expected Stopping Time

Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decision processes (MDPs) extend Markov chains by incorporating non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization criterion is the maximal expected total reward where the MDP stops after T steps, which can be computed by a simple dynamic programming algorithm. We consider a natural generalization of the problem where the stopping times can be chosen according to a probability distribution, such that the expected stopping time is T, to optimize the expected total reward. Quite surprisingly we establish inter-reducibility of the expected stopping-time problem for Markov chains with the Positivity problem (which is related to the well-known Skolem problem), for which establishing either decidability or undecidability would be a major breakthrough. Given the hardness of the exact problem, we consider the approximate version of the problem: we show that it can be solved in exponential time for Markov chains and in exponential space for MDPs.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication.

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot's operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

An Aged Tree with a New Bloom: A Simple Spatiotemporal Programming Strategy Enables Carbon Dot Photosensitizers to Regulate Cell Pyroptosis for Enhanced Tumor Photodynamic-Immunotherapy.

Pyroptosis induced by photodynamic therapy (PDT) is a promising field in both PDT and immunotherapy for tumors. However, effectively inducing tumor cell pyroptosis while triggering a strong immune response using current photosensitizers remains challenging. Herein, the developed positively charged carbon dots (PCDs) nanoPSs were utilized to modulate tumor cell pyroptosis for the first time through a simple spatiotemporal programming strategy. Briefly, PCDs enabled precisely time-dependent targeting of the cell membrane or lysosome. Upon light irradiation, in vitro studies revealed that lysosome-targeted PDT primarily induced apoptosis, while membrane-targeted PDT triggered pyroptosis, resulting in enhanced PDT efficacy and robust activation of the immune response. Conclusively, in vivo studies demonstrated that PCDs could serve as a novel pyroptosis nanotuner for enhanced photodynamic-immunotherapy, thereby simultaneously eliminating primary tumors and inhibiting distant tumor growth and metastases. This spatiotemporal programming strategy unprecedentedly offers a rejuvenation of aged PSs and expands the biomedical use of CDs in immunotherapy.

Short-term Load Forecasting using Genetic Algorithm based Artificial Neural Network

The electrical load Forecasting is considered one of the main and important points for the planning for the future in the electric power networks in both of transmission and distribution systems. In this paper, the genetic optimization algorithm (GA) with the artificial intelligent network (ANN) methods are proposed to forecast the electrical load of the building of Electrical Power Distribution office in the Iraq /Anbar Governorate. Moreover, the optimization method of GA is presented to train the ANN weights using Matlab software with simple programming code. Then, the proposed method is applied to the actual load and the results are compared with that obtained in traditional method such as ANN and regression trees (RG trees) methods. Hence, from the simulation results and the comparison, we can confirm that the proposed method is better than the traditional methods, and it achieved a good agreement with actual load and it provide a good total RMSE of 6% when compared with other methods

An Optimized CRISPR/Cas12a Assay to Facilitate the BRAF V600E Mutation Detection.

Accurate detection of the BRAF V600E (1799T > A) mutation status can significantly contribute to selecting an optimal therapeutic strategy for diverse cancer types. CRISPR-based diagnostic platforms exhibit simple programming, cost-effectiveness, high sensitivity, and high specificity in detecting target sequences. The goal of this study is to develop a simple BRAF V600E mutation detection method. We combined the CRISPR/Cas12a system with recombinase polymerase amplification (RPA). Subsequently, several parameters related to CRISPR/Cas12a reaction efficiency were evaluated. Then, we conducted a comparative analysis of three distinct approaches toward identifying BRAF V600E mutations in the clinical samples. Our data suggest that CRISPR/Cas detection is considerably responsive to variations in buffer conditions. Magnesium acetate (MgOAc) demonstrated superior performance compared to all other examined additive salts. It was observed using 150 nM guide RNA (gRNA) in an optimized reaction buffer containing 14 mM MgOAc, coupled with a reduction in the volumes of PCR and RPA products to 1 μL and 3 μL, respectively, resulted in an enhanced sensitivity. Detection time was decreased to 75 min with a 2% limit of detection (LOD), as evidenced by the results obtained from the blue light illuminator. The CRISPR/Cas12a assay confirmed the real-time PCR results in 31 of 32 clinical samples to identify the BRAF V600E mutation status, while Sanger sequencing detected BRAF V600E mutations with lower sensitivity. We propose a potential diagnostic approach that is facile, fast, and affordable with high fidelity. This method can detect BRAF V600E mutation with a 2% LOD without the need for a thermocycler.

Developing a Sitting Simple Baduanjin program for advanced cancer patients with the fatigue-sleep disturbance symptom cluster: A feasibility study.

We describe a development and feasibility study of a Sitting Simple Baduanjin program for advanced cancer patients suffering from the fatigue-sleep disturbance symptom cluster. This study was to evaluate the practicality and safety of the Sitting Simple Baduanjin intervention and determine its preliminary efficacy. This work employed a single-arm mixed-methods approach. The primary outcome measures were feasibility (i.e., recruitment, adherence, and satisfaction) and safety. Validated self-report questionnaires were used to evaluate the preliminary effects of the program, including fatigue, sleep quality, and quality of life at the 4th, 8th, and 12th weeks of the intervention. Qualitative interviews were also conducted after the program. A total of 30 participants were enrolled, of which 23 (77%) completed the 12-week Sitting Simple Baduanjin program. The mean adherence rate was 88% and no adverse events were reported. Statistically significant improvements were observed in terms of fatigue, sleep quality, and quality of life after program completion. Four themes emerged from the qualitative interview data: (a) acceptability of the Sitting Simple Baduanjin technique, (b) perceived benefits of exercise, (c) barriers, and (d) facilitators. The findings support the feasibility of the Sitting Simple Baduanjin program for advanced cancer patients and show promise in improving patients' levels of the fatigue-sleep disturbance symptom cluster and quality of life.

Comparison of Memory, Balance and Fear of Falling in Older Women After Performing Yoga and Square-stepping Exercises

Objectives Most falls in the elderly occur due to physical and cognitive risk factors. This study aims to compare the effect of two types of mind-body exercises, yoga and square-stepping exercise (SSE), on memory, balance, and fear of falling in older women. Methods &amp; Materials A quasi-experimental study was conducted on 20 elderly women with an age range of 60 to 70 years. Participants were selected through purposive sampling and were randomly assigned to yoga and Square-Stepping groups. Both groups did exercise for 12 weeks and participated in a pre-test and post-test. The Wechsler memory test, stork test, berg balance test, and international falls efficacy scale were used to measure memory quotient, static balance, dynamic balance, and fear of falling, respectively. Analysis of variance with repeated measures was conducted to analyze the data Results The mean scores of memory quotient in the two groups after exercises were significantly higher than before exercises (F(1, 18)=32.692, P&lt;0.001, η2=0.645). The mean static balance time in the yoga group was significantly higher than in the SSE group (F(1, 18)=60.755, P&lt;0.001, η2=0.771). Furthermore, the mean dynamic balance score in the SSE group was significantly higher than in the yoga group (F(1, 18)=7.714, P=0.012, η2=0.300). In addition, the mean square of fear of falling in the SSE group was significantly lower than in the yoga group (F(1, 18)=11.777, P=0.003, η2=0.396). Conclusion It seems that square stepping exercises can also be used as a simple exercise program to improve physical and cognitive functions in older women.

Support vector machine based on the quadratic unconstrained binary optimization model

Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

NGHIÊN CỨU THIẾT KẾ, CHẾ TẠO THIẾT BỊ THEO DÕI TRUYỀN DỊCH TĨNH MẠCH ỨNG DỤNG CÔNG NGHỆ MẠNG KẾT NỐI VẠN VẬT

This paper focuses on the research and development of a medical device designed to monitor and supervise intravenous infusion for patients by leveraging Internet of Things (IoT) technologies. The proposed device is expected to have the following fundamental functionalities: setting the volume of the solution to be infused; setting the drip rate in drops per minute; calculating and displaying the remaining volume of the solution; calculating and displaying the remaining infusion time; and Alerting doctors and patients at different time intervals. The advantages of the designed product include low system design cost based on the current price of components; simple and comprehensible programming language with high scalability; experimental results indicating stable and accurate system operation with low power consumption; and ease of operation, use, maintenance, and servicing. Furthermore, the research results have practical applicability in hospitals or medical facilities and can be used as a practice model for Biomedical Engineering students at educational institutions, where there is a significant lack of practical training products in this field. Keywords: Intravenous infusion; medical devices; monitoring; control; Internet of Things.

Accurate Sampling-Based Cardinality Estimation for Complex Graph Queries

Accurately estimating the cardinality (i.e., the number of answers) of complex queries plays a central role in database systems. This problem is particularly difficult in graph databases, where queries often involve a large number of joins and self-joins. Recently, Park et al. [ 55 ] surveyed seven state-of-the-art cardinality estimation approaches for graph queries. The results of their extensive empirical evaluation show that a sampling method based on the WanderJoin online aggregation algorithm [ 47 ] consistently offers superior accuracy. We extended the framework by Park et al. [ 55 ] with three additional datasets and repeated their experiments. Our results showed that WanderJoin is indeed very accurate, but it can often take a large number of samples and thus be very slow. Moreover, when queries are complex and data distributions are skewed, it often fails to find valid samples and estimates the cardinality as zero. Finally, complex graph queries often go beyond simple graph matching and involve arbitrary nesting of relational operators such as disjunction, difference, and duplicate elimination. Neither of the methods considered by Park et al. [ 55 ] is applicable to such queries. In this article, we present a novel approach for estimating the cardinality of complex graph queries. Our approach is inspired by WanderJoin, but, unlike all approaches known to us, it can process complex queries with arbitrary operator nesting. Our estimator is strongly consistent, meaning that the average of repeated estimates converges with probability one to the actual cardinality. We present optimisations of the basic algorithm that aim to reduce the chance of producing zero estimates and improve accuracy. We show empirically that our approach is both accurate and quick on complex queries and large datasets. Finally, we discuss how to integrate our approach into a simple dynamic programming query planner, and we confirm empirically that our planner produces high-quality plans that can significantly reduce end-to-end query evaluation times.

Automated Assignment of 15N And 13C Enrichment Levels in Doubly-Labeled Proteins.

Uniform enrichment of 15N and 13C in proteins is commonly employed for 2D heteronuclear NMR measurements of the three-dimensional protein structure. Achieving a high degree of enrichment of both elements is important for obtaining high quality data. Uniform labeling of proteins and glycoproteins expressed in higher organisms (yeast or mammalian cell lines) is more challenging than expression in Escherichia coli, a prokaryote that grows on simple, chemically defined media but does not provide appropriate eukaryotic modifications. It is difficult to achieve complete incorporation of both heavy isotopes, and quality control measures are important for quantitating the level of their enrichment. Mass spectrometry measurements of the isotopic distribution of the intact protein or its proteolytic fragments provide the means to assess the enrichment level. A mass accuracy of 1 ppm or better is shown to be required to distinguish the correct combination of 13C and 15N enrichment due to subtle shifts in peak centroids with differences in the underlying, but unresolved, isotopic fine structure. A simple computer program was developed to optimize the fitting of experimental isotope patterns to statistically derived distributions. This method can determine the isotopic abundance from isotope patterns and isotopologue masses in conventional MS data for peptides, intact proteins, and glycans. For this purpose, MATLAB's isotope simulator, isotopicdist, has been modified to permit the variation of 15N and 13C enrichment levels and to perform a two-dimensional grid search of enrichment levels of both isotopes. We also incorporated an alternate isotope simulator, js-emass, into MATLAB for use in the same fitting program. Herein we benchmark this technique on natural abundance ubiquitin and uniformly [15N,13C]-labeled ubiquitin at both the intact and peptide level, outline considerations for data quality and mass accuracy, and report several improvements we have made to the previously reported analysis of the [15N,13C]-enriched human IgG Fc domain, a glycoprotein that has been expressed in Saccharomyces cerevisiae.

Training program focused on tasks aimed at promoting sustainable balance management in elderly individuals

Background: physical performance deteriorates over the years, due to the disuse of the musculoskeletal system, predisposing to balance and gait problems. During the development of the research the priority attention group-elderly population. Objective: to implement physical activity, prevention of sedentary lifestyle, through exercise it was possible to improve balance with the implementation of a double and simple task exercise program due to the consequences generated by dependence in older adults regarding physical, psychological and social health, in which older adults have been found to lose their independence. Methods: the research was descriptive based on the qualitative, cross-sectional approach in which an initial and final evaluation of the Tinetti test was performed, then the exercise program was executed for 16 weeks, lasting 10 to 45 minutes for each exercise. Results: there was a predominance of 63.3% to the female gender and 36.7% male gender, with the significant verification ap value of (p&gt;0.039) was obtained in the test; being less than alpha (p&lt;0.05) rejecting the null hypothesis. Conclusions: the double and simple task exercise program was systematically applied to the entire sample population, which through its intervention is concluded to give the expected results verified with the Initial and Final Tinetti Test there is a finding of great importance improving balance and avoiding the risk of falls in older adults.

Self-polymerization silica nanoparticles based molecularly imprinted polymers for selective recognition of protein

Molecularly imprinted polymers (MIPs) are promising for precise protein separation and purification. However, challenges persist due to their large size, variable configuration, and instability during preparation. Here, a simple silicon self-assembly program was designed to synthesize MIPs without any organic reagents and acid-base catalysis, avoiding the structural damage of protein under severe conditions. In this method, employing hemoglobin (Hb) as a model protein, with tween-20 in emulsification, and tetraethyl orthosilicate (TEOS) as the cross-linking agent, along with co-functional monomers 3-aminopropyltriethoxysilane (APTES) and benzyl(triethoxy)silane (BnTES), enhanced binding efficacy was achieved. Successful imprinting was evidenced through surface morphology observation and physical/chemical property evaluations of the synthesized MIPs. A series of adsorption experiments were performed to investigate the recognition performance of Hb-MIPs. The Hb-MIPs not only exhibited large adsorption capacity (400 μg/mg) and good imprinting factor (6.09) toward template protein, but also showed satisfactory selectivity for reference proteins. Five cycles of adsorption proved that the Hb-MIPs had good reusability. In addition, the successful isolation of HB from bovine blood indicated that Hb-MIPs were an excellent separation and purification material. The mild preparation conditions and good adsorption capacity demonstrated the potential value of this method in separation and purification research.