Correct Rate Research Articles

Nutritional Quality Analysis and Classification Detection of Buckwheat in Different Harvest Periods.

For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

Efficacy of personalized repetitive transcranial magnetic stimulation based on functional reserve to enhance ambulatory function in patients with Parkinson’s disease: study protocol for a randomized controlled trial

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is one of the non-invasive brain stimulations that modulate cortical excitability through magnetic pulses. However, the effects of rTMS on Parkinson’s disease (PD) have yielded mixed results, influenced by factors including various rTMS stimulation parameters as well as the clinical characteristics of patients with PD. There is no clear evidence regarding which patients should be applied with which parameters of rTMS. The study aims to investigate the efficacy and safety of personalized rTMS in patients with PD, focusing on individual functional reserves to improve ambulatory function.MethodsThis is a prospective, exploratory, multi-center, single-blind, parallel-group, randomized controlled trial. Sixty patients with PD will be recruited for this study. This study comprises two sub-studies, each structured as a two-arm trial. Participants are classified into sub-studies based on their functional reserves for ambulatory function, into either the motor or cognitive priority group. The Timed-Up and Go (TUG) test is employed under both single and cognitive dual-task conditions (serial 3 subtraction). The motor dual-task effect, using stride length, and the cognitive dual-task effect, using the correct response rate of subtraction, are calculated. In the motor priority group, high-frequency rTMS targets the primary motor cortex of the lower limb, whereas the cognitive priority group receives rTMS over the left dorsolateral prefrontal cortex. The active comparator for each sub-study is bilateral rTMS of the primary motor cortex of the upper limb. Over 4 weeks, the participants will undergo 10 rTMS sessions, with evaluations conducted pre-intervention, mid-intervention, immediately post-intervention, and at 2-month follow-up. The primary outcome is a change in TUG time between the pre- and immediate post-intervention evaluations. The secondary outcome variables are the TUG under cognitive dual-task conditions, Movement Disorder Society-Unified Parkinson’s Disease Rating Scale Part III, New Freezing of Gait Questionnaire, Digit Span, trail-making test, transcranial magnetic stimulation-induced motor-evoked potentials, diffusion tensor imaging, and resting state functional magnetic resonance imaging.DiscussionThe study will reveal the effect of personalized rTMS based on functional reserve compared to the conventional rTMS approach in PD. Furthermore, the findings of this study may provide empirical evidence for an rTMS protocol tailored to individual functional reserves to enhance ambulatory function in patients with PD.Trial registrationClinicalTrials.gov NCT06350617. Registered on 5 April 2024.

Assessment Study of ChatGPT-3.5's Performance on the Final Polish Medical Examination: Accuracy in Answering 980 Questions.

The use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with one correct answer per question, is administered in Polish, and assesses students' comprehensive medical knowledge and readiness for clinical practice. The aim of this study was to determine how ChatGPT-3.5 handles questions included in this exam. This study considered 980 questions from five examination sessions of the Medical Final Examination conducted by the Medical Examination Center in the years 2022-2024. The analysis included the field of medicine, the difficulty index of the questions, and their type, namely theoretical versus case-study questions. The average correct answer rate achieved by ChatGPT for the five examination sessions hovered around 60% and was lower (p < 0.001) than the average score achieved by the examinees. The lowest percentage of correct answers was in hematology (42.1%), while the highest was in endocrinology (78.6%). The difficulty index of the questions showed a statistically significant correlation with the correctness of the answers (p = 0.04). Questions for which ChatGPT-3.5 provided incorrect answers had a lower (p < 0.001) percentage of correct responses. The type of questions analyzed did not significantly affect the correctness of the answers (p = 0.46). This study indicates that ChatGPT-3.5 can be an effective tool for assisting in passing the final medical exam, but the results should be interpreted cautiously. It is recommended to further verify the correctness of the answers using various AI tools.

A rigorous and versatile statistical test for correlations between stationary time series.

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships.

Predicting Arsenic Contamination in Groundwater: A Comparative Analysis of Machine Learning Models in Coastal Floodplains and Inland Basins

Arsenic (As) contamination in groundwater represents a major global health threat, potentially impacting billions of individuals. Elevated As concentrations are found in river floodplains across south and southeast Asia, as well as in the inland basins of China, despite varying sedimentological and hydrogeochemical conditions. The specific mechanisms responsible for these high As levels remain poorly understood, complicating efforts to predict and manage the contamination. Applying hydro-chemical, geological, and soil parameters as explanatory variables, this study employs multiple linear regression (MLIR) and random forest regression (RFR) models to estimate groundwater As concentrations in these regions. Additionally, random forest classification (RFC) and multivariate logistic regression (MLOR) models are applied to predict the probability of As levels exceeding 10 μg/L in the Hetao Basin (China) and Bangladesh. Model validation reveals that RFR explains 80% and 70% of spatial variability of As concentration in the Hetao Basin and Bangladesh, respectively, outperforming MLIR, which accounts for only 35% and 32%. Similarly, RFC outperforms MLOR in predicting high As probability, achieving correct classification rates of 98.70% (Hetao Basin) and 98.25% (Bangladesh) on training datasets, and 82.76% (Hetao Basin) and 91.20% (Bangladesh) on validation datasets. The performance of the MLOR model on the validation set yields accuracy rates of 81.60% and 72.18%, respectively. In the Hetao Basin, Ca2+, redox potential (Eh), Fe, pH, SO42−, and Cl− are key predictors of As contamination, while in Bangladesh, soil organic carbon (SOC), pH, and SO42− are significant predictors. This study underscores the potential of random forest (RF) models as robust tools for predicting groundwater As contamination.

Development of a genotoxicity/carcinogenicity assessment method by DNA adductome analysis

Safety evaluation is essential for the development of chemical substances. Since in vivo safety evaluation tests, such as carcinogenesis tests, require long-term observation using large numbers of experimental animals, it is necessary to develop alternative methods that can predict genotoxicity/carcinogenicity in the short term, taking into account the 3Rs (replacement, reduction, and refinement). We established a prediction model of the hepatotoxicity of chemicals using a DNA adductome, which is a comprehensive analysis of DNA adducts that may be used as an indicator of DNA damage in the liver. An adductome was generated with LC-high-resolution accurate mass spectrometer (HRAM) on liver of rats exposed to various chemicals for 24 h, based on two independent experimental protocols. The resulting adductome dataset obtained from each independent experiment (experiments 1 and 2) and integrated dataset were analyzed by linear discriminant analysis (LDA) and found to correctly classify the chemicals into the following four categories: non-genotoxic/non-hepatocarcinogens (−/−), genotoxic/non-hepatocarcinogens (+/−), non-genotoxic/hepatocarcinogens (−/+), and genotoxic/hepatocarcinogens (+/+), based on their genotoxicity/carcinogenicity properties. A prototype model for predicting the genotoxicity/carcinogenicity of the chemicals was established using machine learning methods (using random forest algorithm). When the prototype genotoxicity/carcinogenicity prediction model was used to make predictions for experiments 1 and 2 as well as the integrated dataset, the correct response rates were 89 % (genotoxicity), 94 % (carcinogenicity) and 87 % (genotoxicity/carcinogenicity) for experiment 1, 47 % (genotoxicity), 62 % (carcinogenicity) and 42 % (genotoxicity/carcinogenicity) for experiment 2, and 52 % (genotoxicity), 62 % (carcinogenicity), and 48 % (genotoxicity/carcinogenicity) for the integrated dataset. To improve the accuracy of the toxicity prediction model, the toxicity label was reconstructed as follows; Pattern 1: when +/+ and −/− chemicals were used from the toxicity labels +/+, +/−, −/+ and −/−; and Pattern 2: when +/+, +/−, and −/+ other than −/− were replaced with the label "Others". As a result, chemicals with only +/+ and −/− toxicity labels were used and the correct response rates were approximately 100 % for the measured data in experiment 1, 53 %–66 % for the data in experiment 2, and 59–73 % for the integrated data, all of which were 10 %–30 % higher compared with the data before the label change. In contrast, when the toxicity labels were replaced with −/− and “Others”, they reached nearly 100 % in the measured data from experiment 1, 65 %–75 % in the data from experiment 2, and 70 %–78 % in the integrated data, all of which were 10 %–50 % higher compared with the data before the label change.

Human-AI collaboration: Unraveling the effects of user proficiency and AI agent capability in intelligent decision support systems

Artificial intelligence (AI) agents are integral components of modern intelligent decision support systems (IDSS), providing their capability to assist in decision-making processes. Understanding the influence of AI involvement on human responses within these systems is crucial for fostering appropriate reliance on AI advice and ensuring a comfortable user experience. This study delves into the impact of AI agents on decision change, correct rate change, confidence rate change and pleasure rating among users. It explores the main and interactive effects of user proficiency and AI agent capability on user responses. A block-factorial experiment involving 45 participants was conducted. The results indicated that high-capability AI agents were associated with substantial decision change, correct rate change and confidence rate change. Participants with higher proficiency, both subjectively and objectively, exhibited reduced reliance on AI agents for task execution. Notably, the involvement of AI agents led to a slight decline in correct rates for participants of high competence (i.e., participants' independent correct rate exceeds AI agent capability), while still enhancing their confidence levels. Additionally, agents lacking human-like embodiment tended to enhance participants’ pleasure ratings. Individual differences in gender, collaboration experience with AI agent, and stereotype on AI agent capability also shaped the decision changes and pleasure ratings of participants. These findings underscore the presence of complementary competence interaction between user and AI agent within IDSS, and can be applied in designing capability of advisory AI agents with consideration of user proficiency and individual characteristics. Relevance to industryThe findings can be used as guidelines for designing AI agents and specifying their application strategies to integrate the assistance of AI agents effectively in intelligent decision support systems.

Alignment of Patient Information Leaflets with the Health Literacy Skills of Future End-Users: Are We on the Same Page?

ABSTRACT Effective health communication will not be possible if the messages directed to patients do not adapt to their health literacy skills. In this sense, if the Patient Information Leaflets (PILs) are not understood properly, they will not fulfill their purpose. Secondary education aims to develop students’ fundamental skills, including reading comprehension, which should align with future patients. This study assesses reading comprehension of commonly used PILs among secondary education students. An observational cross-sectional study involved 590 Spanish secondary school students from March to June 2022. They read 3 original PILs and completed a questionnaire about medication use information. The European health literacy survey questionnaire (short version) and sociodemographic data were collected. The average correct answer rate was only 51%, with just 14.58% understanding when to take ibuprofen, results that fell significantly below the minimum 80% threshold required by the European Union. Age, gender, and cultural background didn’t significantly impact comprehension. In conclusion, health literacy skills acquired in secondary education are insufficient for understanding current PILs. Educational reform is necessary to improve students’ ability to comprehend medication instructions and enhance PILs for effective communication. At the same time, our work highlights that current PILs need to be redesigned to reduce the cognitive burden imposed on patients.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Knowledge and practices of neonatal intensive care unit nurses concerning hypothermia in preterm infants: Adescriptive cross-sectional study.

To investigate the knowledge level and clinical practice of neonatal intensive care unit nurses on the whole process of hypothermia prevention in preterm infants. A polycentric descriptive cross-sectional study was conducted in 23 hospitals in Shandong province involving 254 neonatal intensive care unit nurses. An evidence-based knowledge and clinical practice questionnaire on hypothermia prevention in preterm infants and a general information questionnaire were used to collect data. SPSS and EXCEL 24.0 database were used for data statistics and analysis. Nearly one-third of nurses were unclear about the definition of mild hypothermia and heat preservation measures during labour management and resuscitation. Knowledge about the correct rate of amniotic fluid evaporation was low. The higher the hospital level and nurses' education level, professional title, work experience, and position, the higher the knowledge level. The clinical practice of nurses differed in terms of body temperature assessment tools, measurement sites, and measurement frequency for premature infants. Nurses in neonatal intensive care units need to apply evidence-based knowledge and carry out practice interventions to ensure preterm infants' safe transition from the delivery room to the neonatal intensive care unit. There have been few studies evaluating neonatal nurses' knowledge and practices with regards to the preventing hypothermia of premature infants from delivery room to neonatal intensive care unit. The study identifies the deficiencies and problems in temperature management that can be attributed to the knowledge level of nurses. The findings will help improve the current curriculum, teaching strategies, and the nurses' knowledge levels, preventing premature hypothermia in infants. This study adhered to the STROBE statement for observational studies and obtained approval (KYLL-2023LW045) from the ethics committee of The Second Hospital of Shandong University. The other 22 third- and second-level hospitals, as our alliance members, recognized our chairperson status within the alliance and were willing to join our academic activities. Mutual responsibility for ethical filing between alliance units. 254 nurses were recruited from 23 tertiary and secondary public hospitals in Shandong province. No public or patient involvement. Members of the research group used the Questionnaire Star platform to prepare the electronic questionnaire, including obtaining informed consent, taking precautions for questionnaire completion, and using the aforementioned measurement tools. The project research team contacted the head nurses of 23 neonatal intensive care units in Shandong Province. After obtaining consent from the head nurses, they were asked to send the requirements and link of the electronic questionnaire to their WeChat management group. The nurses go through each question and make a choice which has two options of 'agree' or 'disagree.' Based on their answers, nurses were evaluated as having 'mastered' or 'not mastered' each item. Each 'mastered' item (correct answer) was given a score of 4; an item 'not mastered' (incorrect answer) was given a score of 0. The total score ranged from 0 to 100.

Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

Multi-modal Chinese Text Emotion Metaphor Computation Based on Mutual Information and Information Entropy

Metaphor is to express another thing through one thing, it is not only a rhetorical means, but also embodies a kind of analogical cognition and way of thinking of people. In recent years, metaphors have made more and more progress in Chinese language recognition, so their status has gradually risen. Chinese language sentiment analysis is a relatively difficult problem in language processing. Chinese language semantics is ambiguous and complex, so it is a great challenge to construct Chinese Emotion structure. In this paper, Chinese nouns and Chinese verbs are used as test samples to conduct metaphor test calculation. The test results of mutual information and information entropy show that for the sample metaphor calculation, the correct rate of individual words is less than 70%. The ratio between the macro-average of gerund words and the baseline fluctuates relatively large. The metaphor recognition of nouns is about 20% higher than the original test results and about 10% higher for verbs. It shows that under the action of mutual information and information entropy, the metaphor recognition performance of the basic structure of Chinese Emotion constructed from the perspective of metaphor is improved, and further illustrates the feasibility of mutual information and information entropy algorithm for building the basic structure.

Investigation of different methods in rapid microbial identification directly from positive blood culture bottles by MALDI-TOF MS.

Sepsis is a life-threatening condition, and rapid and accurate identification of the causative microorganisms from blood cultures is crucial for timely and effective treatment. Although there are many studies on direct identification from blood cultures with MALDI-TOF MS, further standardization is still needed. In our study, we analyzed the performance of three different preparation methods and compared by using two analysis modules of the Bruker Biotyper MALDI-TOF MS for direct identification of bacteria from numerous positive blood culture bottles. The literature reports a limited number of studies that compare different preparation methods for direct blood culture identification, processing a large number of blood samples concurrently and evaluating the same samples as in our study. Moreover, although SDS is used very frequently in medical laboratories, there are few studies on direct identification from blood culture bottles. In our study, the highest correct identification rate was observed with the SDS method.

Diagnostic performances of Claude 3 Opus and Claude 3.5 Sonnet from patient history and key images in Radiology's "Diagnosis Please" cases.

The diagnostic performance of large language artificial intelligence (AI) models when utilizing radiological images has yet to be investigated. We employed Claude 3 Opus (released on March 4, 2024) and Claude 3.5 Sonnet (released on June 21, 2024) to investigate their diagnostic performances in response to the Radiology's Diagnosis Please quiz questions. In this study, the AI models were tasked with listing the primary diagnosis and two differential diagnoses for 322 quiz questions from Radiology's "Diagnosis Please" cases, which included cases 1 to 322, published from 1998 to 2023. The analyses were performed under the following conditions: (1) Condition 1: submitter-provided clinical history (text) alone. (2) Condition 2: submitter-provided clinical history and imaging findings (text). (3) Condition 3: clinical history (text) and key images (PNG file). We applied McNemar's test to evaluate differences in the correct response rates for the overall accuracy under Conditions 1, 2, and 3 for each model and between the models. The correct diagnosis rates were 58/322 (18.0%) and 69/322 (21.4%), 201/322 (62.4%) and 209/322 (64.9%), and 80/322 (24.8%) and 97/322 (30.1%) for Conditions 1, 2, and 3 for Claude 3 Opus and Claude 3.5 Sonnet, respectively. The models provided the correct answer as a differential diagnosis in up to 26/322 (8.1%) for Opus and 23/322 (7.1%) for Sonnet. Statistically significant differences were observed in the correct response rates among all combinations of Conditions 1, 2, and 3 for each model (p < 0.01). Claude 3.5 Sonnet outperformed in all conditions, but a statistically significant difference was observed only in the comparison for Condition 3 (30.1% vs. 24.8%, p = 0.028). Two AI models demonstrated a significantly improved diagnostic performance when inputting both key images and clinical history. The models' ability to identify important differential diagnoses under these conditions was also confirmed.

Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks

ObjectiveTo present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks. Study designThis is a prospective descriptive study conducted at a university hospital.1654 patients were recruited to the study according to adenomyosis diagnosed by transvaginal ultrasound (TVS). For adenomyosis characteristics and ultrasound images, automatic identification of adenomyosis were performed based on deep learning methods. We called this unique technique A2DNet: Adenomyosis Auto Diagnosis Network. ResultsThe A2DNet exhibits excellent performance in diagnosis of adenomyosis, achieving an accuracy of 92.33%, a precision of 96.06%, a recall of 91.71% and an F1 score of 93.80% in the test group. The confusion matrix of experimental results show that the A2DNet can achieve a correct diagnosis rate of 92% or more for both normal and adenomyosis samples, which demonstrate the superiority of the A2DNet comparing with the state-of-the-arts. ConclusionThe A2DNet is a safe and effective technique to aid in automatic diagnosis of adenomyosis. The technique which is nondestructive and non-invasive, is new and unique due to the advantages of artificial intelligence.

A Comparative Analysis of ChatGPT and Medical Faculty Graduates in Medical Specialization Exams: Uncovering the Potential of Artificial Intelligence in Medical Education.

Background This study aims to evaluate the performance of ChatGPT in the medical specialization exam (MSE) that medical graduates take when choosing their postgraduate specialization and to reveal how artificial intelligence-supported education can increase the quality and academic success of medical education. The research aims to explore the potential applications and advantages of artificial intelligence in medical education and examine ways in which this technology can contribute to student learning and exam preparation. Methodology A total of 240 MSE questions were posed to ChatGPT, 120 of which were basic medical sciences questions and 120 were clinical medical sciences questions. A total of 18,481 people participated in the exam. The performance of medical school graduates was compared with ChatGPT-3.5 in terms of answering these questions correctly. The average score for ChatGPT-3.5 was calculated by averaging the minimum and maximum scores. Calculations were done using the R.4.0.2 environment. Results The general average score of graduates was a minimum of 7.51 in basic sciences and a maximum of 81.46, while in clinical sciences, the average was a minimum of 12.51 and a maximum of 80.78. ChatGPT, on the other hand, had an average of at least 60.00 in basic sciences and a maximum of 72.00, with an average of at least 66.25 and a maximum of 77.00 in clinical sciences. The rate of correct answers in basic medical sciences for graduates was 43.03%, while for ChatGPT was 60.00%. In clinical medical sciences, the rate of correct answers for graduates was 53.29%, while for ChatGPT was 64.16%. ChatGPT performed best with a 91.66% correct answer rate in Obstetrics and Gynecology and an 86.36% correct answer rate in Medical Microbiology. The least successful area for ChatGPT was Anatomy, with a 28.00% correct answer rate, a subfield of basic medical sciences. Graduates outperformed ChatGPT in the Anatomy and Physiology subfields. Significant differences were found in all comparisons between ChatGPT and graduates. Conclusions This study shows that artificial intelligence models such as ChatGPT can provide significant advantages to graduates, as they score higher than medical school graduates. In terms of these benefits, recommended applications include interactive support, private lessons, learning material production, personalized learning plans, self-assessment, motivation boosting, and 24/7 access, among a variety of benefits. As a result, artificial intelligence-supported education can play an important role in improving the quality of medical education and increasing student success.

Personal identification system based on multidimensional electroencephalographic signals

Personal authentication using electroencephalographic (EEG) signals, is one of the important applications in brain computer interface (BCI). In this work we investigate the use of EEG signals as a biometric trait. Multidimensional EEG signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine learning (ML) classifiers. MDRM classifier achieved a correct recognition rate (CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%.

Raman spectroscopy-based authentication of powder goat milk adulteration with cow milk

This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powdered goat milk (target class) against adulteration with powdered cow milk. Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, respectively), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed. Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD). Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent analytical insights for the authentication of pure powdered goat milk, with a significance level of 0.01. This methodology furnishes distinct spectral signatures associated with the macromolecular composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% w/w of cow milk added to powdered goat milk. Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food analysis, especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.

Improvement in Adverse Drug Reaction (ADR) Knowledge: A Pre- and Post-video Intervention Study Among Doctors.

Background Adverse drug reactions (ADRs) represent a significant public health concern, contributing to mortality, morbidity, and healthcare costs worldwide. Healthcare practitioners especially doctors play a vital role in identifying and reporting ADR. This study investigates the prevalence of knowledge regarding ADR among doctors and enhances it with educational intervention. It also explores the association between demographic factors and baseline ADR awareness. Methods A prospective cross-sectional interventional study was conducted among doctors in Ahmedabad, India, to evaluate their knowledge of ADR reporting and the effectiveness of an educational video intervention. Pre- and post-intervention questionnaires were administered to assess knowledge improvement. Statistical analysis, including paired t-tests and chi-square tests, was performed to evaluate the intervention's impact and explore associations between demographic factors and ADR awareness. Results Analysis of pre- and post-intervention questionnaires revealed a significant increase in correct response rates post-intervention, indicating the effectiveness of the educational video intervention. Demographic factors, particularly age, were associated with ADR awareness. Following the intervention, participants demonstrated an improved understanding of ADR definitions, WHO causality assessment, reporting mechanisms, and challenges faced by pharmacovigilance programs. All participants found the video helpful and expressed intent to share their knowledge post-intervention. Conclusion The results of the study suggest that educational video intervention can serve as an effective tool for understanding ADRconcepts and pharmacovigilance practices. Moreover, the association of demographic factors, particularly age, with ADR awareness further emphasizes the importance of educational interventions in addressing specific population needs.

South Korean first-time mothers' knowledge of digestive health problems in infancy and their utilization of health care facilities for digestive health concerns: a descriptive study.

This study aimed to assess the knowledge level of first-time mothers regarding digestive health issues in infancy and to examine the utilization of healthcare facilities for such problems. Data from 119 first-time mothers of infants under 6 months of age were analyzed. Descriptive statistics, t-test, and one-way analysis of variance (ANOVA) were conducted using the SPSS software. The average correct response rate for first-time mothers' knowledge of digestive health problems in infancy was 61.9%. The highest correct response rate was observed for infantile colic, while diarrhea had the lowest. Less than 50% of mothers received education on infant digestive health problems across all categories. Among digestive health problems in infancy, diarrhea exhibited the highest rate of healthcare utilization, whereas infantile colic had the lowest. First-time mothers' knowledge of digestive health problems in infancy varied based on maternal age (t=-3.66, p<.001), education level (t=-2.26, p=.026), and planned pregnancy (t=3.24, p=.002). Moreover, mothers who received education on infant digestive health problems demonstrated better overall knowledge of digestive health problems. The rate of education regarding digestive health problems during infancy among first-time mothers was < 50%. Furthermore, mothers educated on infant digestive health issues exhibited improved knowledge. Therefore, it is necessary to provide appropriate pre-education to primiparous common gastrointestinal health issues in infants.