MedLingua: A conceptual framework for a multilingual medical conversational agent.

This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.

Application of the SeDeM system for the preparation of antiparasitic tablets from mesquite flour for use in sheep.

The use of herbal medicine to treat various diseases is becoming increasingly important as an alternative therapy. Numerous plants have been traditionally used for different purposes, including antiparasitic in humans and animals. Diseases caused by gastrointestinal parasites in ruminants, especially by the nematode Haemonchus contortus, cause large economic losses to the producers, whether by complications of the diseases or the cost of treatment. The main way of handling nematodiasis is by administering anthelmintic drugs, but their excessive use has the disadvantage of causing drug resistance; therefore, an alternative is the use of herbal medicine for this purpose. Mesquite (Prosopis spp.) has been used in Mexico to treat gastrointestinal diseases attributed to helminths. The present study aimed to characterize the rheological properties of mesquite flour using the SeDeM Expert System to determine its suitability for tablet production by direct compression. Direct compression technology facilitates the tableting process by reducing manufacturing costs. The results of the present study indicate that mesquite flour can be processed by direct compression. The latter could allow the manufacturing of economic tablets to treat infections by H. contortus in ruminants.

Laboratory tests for investigating anemia: From an expert system to artificial intelligence.

Objective: To compare the laboratory tests conducted in real-life settings for patients with anemia with the expected prescriptions derived from an optimal checkup. Methods: A panel of experts formulated an "optimal laboratory test assessment" specific to each anemia profile. A retrospective analysis was done of the laboratory tests conducted according to the type of anemia (microcytic, normocytic or macrocytic). Using an algorithmic system, the laboratory tests performed in real-life practice were compared with the recommendations suggested in the "optimal laboratory test assessment" and with seemingly "unnecessary" laboratory tests. Results: In the analysis of the "optimal laboratory test assessment", of the 1179 patients with microcytic anemia, 269 (22.8%) had had one of the three tests recommended by the expert system, and only 33 (2.8%) had all three tests. For normocytic anemia, 1054 of 2313 patients (45.6%) had one of the eleven recommended tests, and none had all eleven. Of the 384 patients with macrocytic anemia, 196 (51%) had one of the four recommended tests, and none had all four. In the analysis of "unnecessary laboratory tests", one lab test was unnecessarily done in 727/3876 patients (18.8%), i.e. 339 of 1179 (28.8%) microcytic, 171 of 2313 (7.4%) normocytic, and 217 of 384 (56.5 %) macrocytic anemias. Conclusion: Laboratory investigations of anemia remain imperfect as more than half of the cases did not receive the expected tests. Analyzing other diagnostic domains, the authors are currently developing an artificial intelligence system to assist physicians in enhancing the efficiency of their laboratory test prescriptions.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings.

Objectives: Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis. Methods: The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose. Results: The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use. Conclusions: With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

Designing, validation and evaluation of the expert system of "Healthy Menopause" and assessing its effect on the management of menopause symptoms: an exploratory mixed method study protocol.

BACKGROUND: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. METHODS/DESIGN: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. DISCUSSION: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice.

Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance.

Breast cancer pathological image segmentation (BCPIS) holds significant value in assisting physicians with quantifying tumor regions and providing treatment guidance. However, achieving fine-grained semantic segmentation remains a major challenge for this technology. The complex and diverse morphologies of breast cancer tissue structures result in high costs for manual annotation, thereby limiting the sample size and annotation quality of the dataset. These practical issues have a significant impact on the segmentation performance. To overcome these challenges, this study proposes a semi-supervised learning model based on classification-guided segmentation. The model first utilizes a multi-scale convolutional network to extract rich semantic information and then employs a multi-expert cross-layer joint learning strategy, integrating a small number of labeled samples to iteratively provide the model with class-generated multi-cue pseudo-labels and real labels. Given the complexity of the breast cancer samples and the limited sample quantity, an innovative approach of augmenting additional unlabeled data was adopted to overcome this limitation. Experimental results demonstrate that, although the proposed model falls slightly behind supervised segmentation models, it still exhibits significant progress and innovation. The semi-supervised model in this study achieves outstanding performance, with an IoU (Intersection over Union) value of 71.53%. Compared to other semi-supervised methods, the model developed in this study demonstrates a performance advantage of approximately 3%. Furthermore, the research findings indicate a significant correlation between the classification and segmentation tasks in breast cancer pathological images, and the guidance of a multi-expert system can significantly enhance the fine-grained effects of semi-supervised semantic segmentation.

The Hombres Saludables Physical Activity Web-Based and Mobile Phone Intervention: Pilot Randomized Controlled Trial With Latino Men.

BACKGROUND: Owing to structural-level, interpersonal-level, and individual-level barriers, Latino men have disproportionately high rates of physical inactivity and experience related chronic diseases. Despite these disparities, few physical activity (PA) interventions are culturally targeted for Latino men. OBJECTIVE: This study reported the feasibility and acceptability of Hombres Saludables PA intervention for Latino men. We also reported the preliminary efficacy of the intervention on PA change and provided the results of the exploratory moderator and mediator analysis. METHODS: We completed a 6-month, single-blind, pilot randomized controlled trial of Hombres Saludables with Latino men aged between 18 and 65 years. Men were randomized to either (1) a theory-driven, individually tailored, internet-based and SMS text message-based, Spanish-language PA intervention arm or (2) a nutrition and wellness attention contact control arm that was also delivered via the web and SMS text message. We assessed the primary study outcomes of feasibility using participant retention and acceptability using postintervention survey and open-ended interview questions. We measured the preliminary efficacy via change in minutes of moderate to vigorous PA per week using ActiGraph wGT3X-BT accelerometry (primary measure) and self-reported minutes per week using 7-day Physical Activity Recall. Participants completed the assessments at study enrollment and after 6 months. RESULTS: The 38 participants were predominantly Dominican (n=8, 21%) or Guatemalan (n=5, 13%), and the mean age was 38.6 (SD 12.43) years. Retention rates were 91% (21/23) for the PA intervention arm and 100% (15/15) for the control arm. Overall, 95% (19/20) of the intervention arm participants reported that the Hombres study was somewhat to very helpful in getting them to be more physically active. Accelerometry results indicated that participants in the intervention group increased their PA from a median of 13 minutes per week at study enrollment to 34 minutes per week at 6 months, whereas the control group participants showed no increases. On the basis of self-reports, the intervention group was more likely to meet the US PA guidelines of 150 minutes per week of moderate to vigorous PA at 6-month follow-up, with 42% (8/19) of the intervention participants meeting the PA guidelines versus 27% (4/15) of the control participants (odds ratio 3.22, 95% CI 0.95-13.69). Exploratory analyses suggested conditional effects on PA outcomes based on baseline stage of motivational readiness, employment, and neighborhood safety. CONCLUSIONS: The PA intervention demonstrated feasibility and acceptability. Results of this pilot study indicate that the Hombres Saludables intervention is promising for increasing PA in Latino men and suggest that a fully powered trial is warranted. Our technology-based PA intervention provides a potentially scalable approach that can improve health in a population that is disproportionately affected by low PA and related chronic disease. TRIAL REGISTRATION: ClinicalTrials.gov NCT03196570; https://classic.clinicaltrials.gov/ct2/show/NCT03196570. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23690.

Paediatric orthopaedic expert system.

The paediatric orthopaedic expert system analyses and predicts the healing time of limb fractures in children using machine learning. As far we know, no published research on the paediatric orthopaedic expert system that predicts paediatric fracture healing time using machine learning has been published. The University Malaya Medical Centre (UMMC) offers paediatric orthopaedic data, comprises children under the age of 12 radiographs limb fractures with ages recorded from the date and time of initial trauma. SVR algorithms are used to predict and discover variables associated with fracture healing time. This study developed an expert system capable of predicting healing time, which can assist general practitioners and healthcare practitioners during treatment and follow-up. The system is available online at https://kidsfractureexpert.com/.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.

BACKGROUND: Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment. OBJECTIVE: This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of care. METHODS: An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists; n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group technique. RESULTS: Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation. CONCLUSIONS: The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support timelier decision-making and enhance continuity of care through the automation of more frequent automated checkpoints, while empowering patients to self-manage their symptoms more effectively than standard care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-045806.

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research.

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow.

BACKGROUND: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. METHOD: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. RESULTS: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). CONCLUSION: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

SeDeM as a Tool to Validate Drug Substance Manufacturing Processes and Assess Scalability and Suitability for Direct Compression: Supplier Screening.

During the development of an oral solid form of a drug substance, a thorough understanding of the critical material attributes is necessary, as the physical properties of the active pharmaceutical ingredient (API) can profoundly influence the drug product's manufacturability, critical quality attributes, and bioavailability. The objective of this study was to validate the manufacturing process of the drug Linezolid from three different sources at both the pilot and industrial scale and to identify differences in critical material attributes between the API manufacturers. Furthermore, the scalability factor between the pilot and industrial scale and the suitability of a process for direct compression were also evaluated. In the present study, the different sources of API were characterized by SeDeM methodology, particle size distribution, and scanning electron microscopy determinations. The statistical analysis revealed that no statistically significant differences were found for any of the parameters under study for the same API source analyzed on both scales. On the other hand, for most of the parameters evaluated, statistical differences were observed between the different sources. It was concluded that SeDeM was able to successfully validate the API manufacturing process, assess scalability, and distinguish between sources. Therefore, it could be highly valuable in the formulation phase to select the best API source.

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

Comprehensive guava fruit data set: Digital and thermal images for analysis and classification.

Guava (Psidium guajava) is a nutritious fruit known for its origin in Mexico, Central or South America, and the Caribbean. Its production faces declining levels, infections, and disease outbreaks. This dataset focuses on the early identification of guava diseases using image processing and computer vision. Farmers can detect and address diseases promptly by developing an expert system, increasing yields and reducing economic losses. The technology behind this dataset enables sustainable guava farming and disease prevention. This dataset consists of digital and thermal images of guava fruits, including healthy, damaged, and various diseased conditions such as wilt, Anthracnose, canker, and rot. The images are categorized based on the fruit's maturity level (mature, half-mature, and mature) and captured under different drop heights. The dataset also includes information on the damage-inducing methods, storage conditions, image capture schedule, and specific diseases present. The thermal images were acquired using hot air with controlled temperature and velocity.

Therapy-based expert system on function and postural stability after anterior cruciate ligament reconstruction: a pilot study.

PURPOSE: Wii Fit exergames have been less commonly used for the rehabilitation of athletes after Anterior Cruciate Ligament Reconstruction (ACLR). This study aims to investigate the effects of an expert system using Wii Fit exergames compared to conventional rehabilitation following ACLR. A forward-chaining rule-based expert system was developed which proposed a rehabilitation program that included the number and type of exercise in terms of difficulty and ease and the duration of each exercise in a progressive manner according to the patient's physical condition. MATERIALS AND METHODS: Twenty eligible athletes aged 20-30 who underwent ACLR were enrolled in this study and randomly assigned to two groups; and received 12 sessions of either Wii Fit exergames as Wii group (n = 10) or conventional rehabilitation as CL group (n = 10). RESULTS: The main outcomes consisted of pain (Visual Analogue Scale (VAS)), knee effusion, knee flexion range (KFR), thigh girth (TG), single-leg hop for distance (SLHD), and for time (SLHT), static and dynamic balance tests. Both groups had considerable improvement in all outcomes, also there were significantly differences between Wii and CL groups as follows; VAS (P < 0.001), knee effusion (P < 0.001), TG (P = 0.001), KFR (P = 0.012), static balance in stable position (P < 0.001) and in unstable position (P = 0.001), dynamic balance in the anterior (P < 0.001), posteromedial (P < 0.001), posterolateral (P = 0.004) directions, symmetry index of SLHD (P < 0.001) and symmetry index of SLHT (P = 0.013). CONCLUSIONS: The findings showed that using Wii Fit exergames in post-ACLR patients reduced pain and effusion while also improving function and balance significantly. Iranian Registry of Clinical Trials registration number is IRCT20191013045090N1, and the registration date is 03-03-2020.

Development of Child-Friendly Lisdexamfetamine Chewable Tablets Using Ion Exchange Resin as a Taste-Masking Carrier Based on the Concept of Quality by Design (QbD).

Taste masking is critical to improving the compliance of pediatric oral dosage forms. However, it is challenging for extremely bitter lisdexamfetamine dimesylate (LDX) with a long half-life and given in large dose. The present study aims to develop an immediate-release, taste-masked lisdexamfetamine chewable tablet. Lisdexamfetamine-resin complexes (LRCs) were prepared using the batch method. The molecular mechanism of taste masking was explored by PXRD, PLM, STA, and FT-IR. The results showed that taste masking was attributed to the ionic interaction between drug and the resin. The ion exchange process conformed to first-order kinetics. The rate-limiting step of drug release was the diffusion of ions inside the particles, and the concentration of H+ was the key factor for immediate release. The masking efficiency of the prepared LRCs in saliva exceeded 96%, and the drug could be completely released within 15 min in aqueous HCl (pH 1.2). Furthermore, the SeDeM expert system was used for the first time to comprehensively study the powder properties of LRCs and to quickly visualize their defects (compressibility, lubricity/stability, and lubricity/dosage). The selection of excipients was targeted rather than traditional screening, thus obtaining a robust chewable tablet formulation suitable for direct compression. Finally, the difference between chewable tablets containing LRCs and chewable tablets containing lisdexamfetamine dimesylate was compared by in vitro dissolution test, electronic tongue, and disintegration test. In conclusion, an immediate-released, child-friendly lisdexamfetamine chewable tablets without bitterness was successfully developed by the QbD approach, using the SeDeM system, which may help in further development of chewable tablets.

Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.