Recognition Function Research Articles

초등 실과에서 효율적인 소프트웨어와 로봇 및 인공지능 교육을 위한 플랫폼의 개발

This study aimed to develop an educational platform to efficiently implement software, robotics, and artificial intelligence education in the revised 2022 elementary practical arts education curriculum. The results of the study are as follows. The revised 2015 curriculum posed various methodological difficulties that hindered efficient software and robotics instruction. First, there were difficulties in pairing block-based programming tools (software) with robots, the hardware that executes coding content. Second, teachers found monitoring each student's coding progress in real-time difficult, and answering individual questions required additional time, making personalized guidance difficult. Third, it took considerable time to share correctly coded programs with students for comparison and modification or to explain the correct coding methods. To address these challenges, this study developed a platform to efficiently facilitate software, robot, and coding education for artificial intelligence. The platform allows for easy pairing between programming tools and hardware and allows teachers and students to share coding content in real time. In addition, students can copy correctly coded programs to their own platforms in real time and modify or execute them. This platform is compatible with not only computers but also tablets and mobile phones, with capabilities such as voice recognition, image recognition, and machine learning. Image recognition functionality includes blocks for face, hand, and character recognition.

A TWO-STEP AUTHENTICATION FACIAL RECOGNITION SYSTEM FOR AUTOMATED ATTENDANCE TRACKING

This study addresses the need for efficient, automated attendance systems through the design of a facial recognition application. Manual attendance systems are slow, error-prone and the retrieval of old records can be tedious. Universally assessable technological developments such as facial recognition software can easily solve these problems. However, the vast amount of computational resources required for its implementation has posed a limitation to its wide adoption. This study presents a two-step approach to resolve these challenges. By leveraging a faster, less-powerful model, as the first step, the workload of facial recognition can be distributed to save time and computational cost. A more powerful machine learning model is applied as the second step, deployed for tasks that are too complex for the first model to handle. The two-step authentication process will also reduce the occurrences of false negatives. Face_recognition, a python library is used for detection and encoding of face images read using python’s opencv library from an IP webcam. A flask application demonstrates this facial recognition functionality. The database connection and communication are accomplished using flask_sqlalchemy. A graphical user interface (web application) is used to interact with users on a high level, showing saved images of logged personnel and their times of entry. The system has a maximum accuracy of 98.78% and precision of 98.82% from tests. This shows its potential for application on a wider scale, with some added improvements such as cloud deployment and larger datasets.

Effectiveness of a social cognition remediation intervention for patients with schizophrenia: a randomized-controlled study

This randomized-controlled study evaluates the effectiveness of a newly developed social cognition rehabilitation intervention, the modified Social Cognition Individualized Activity Lab (mSoCIAL), in improving social cognition and clinical and functional outcomes of persons with schizophrenia recruited in two Italian sites: University of Campania “Luigi Vanvitelli” in Naples and ASST Fatebenefratelli-Sacco in Milan. mSoCIAL consists of a social cognitive training module focusing on different domains of social cognition and of a narrative enhancement module. We assessed changes in social cognition, clinical characteristics and functional variables in patients with schizophrenia who participated in 10 weekly sessions of mSoCIAL or received treatment as usual (TAU). A paired-sample t test and a repeated-measures MANOVA were used to investigate respectively within and between-group differences. Twenty people with schizophrenia were blindly assigned to mSoCIAL and 20 to TAU. After 10 weeks, mSoCIAL significantly improved disorganization, emotion recognition, functional capacity and real-life functioning. As compared to TAU, the mSoCIAL group showed a significant improvement in minimal and enriched social inference domain of theory of mind, and in key domains of real-life functioning (interpersonal relationships, everyday life skills, and work skills). mSoCIAL improved social cognition and real-life functioning of people with schizophrenia. These results highlight the importance of social cognition deficit treatment in schizophrenia and the necessity for these interventions to be multifaced and personalized. Such an approach ensures that improvements in social cognition translate into enhanced functional outcomes.Trial registration NCT05130853, registered on 24 November 2021.

The Preparation of Molecularly Imprinted Polymers and the Application in Pharmaceutical Analysis

Molecular imprinting technology (MIT) is an artificial method for preparing polymers with specific recognition of target molecules. Molecularly imprinted polymers (MIPs) have been widely used in electrochemical sensors due to their simple preparation, good stability, and molecular recognition function. Especially in drug analysis, MIPs-based electrochemical sensors have become a research hotspot. Therefore, this work focuses on MIPs-based electrochemical sensors. The performance of MIPs is closely related to their preparation methods. In this work, the preparation methods of MIPs are discussed in detail. This work also discussed MIP-based electrochemical sensors. MIP-based electrochemical sensors have achieved accurate detection in flavonoid and antibiotic drugs. Meanwhile, as a polymer, MIPs can also have an impact on the conductivity of working electrode. Therefore, it is necessary to choose some nanomaterials with excellent conductivity for its modification. In the future, molecular imprinting technology will have more applications in electrochemical sensors. In drug analysis, more imprinting targets should be explored for detection by using this technology.

Facial Affect Recognition and Executive Function Abnormalities in ADHD Subjects: An ERP Study.

Attention-deficit/hyperactivity disorder (ADHD) affects approximately 12% of children worldwide. With a 50% chance of persistence into adulthood and associations with impairments in various domains, including social and emotional ones, early diagnosis is crucial. The exact neural substrates of ADHD are still unclear. This study aimed to reassess the behavioral and neural metrics of executive functions and neural substrates of facial affect recognition. A total of 117 ADHD patients and 183 healthy controls were evaluated by two Go/NoGo tasks: the classic visual continuous performance test and the emotional continuous performance test, which requires facial affect encoding. Group differences between ADHD subjects and healthy controls were assessed using analysis of covariance (ANCOVA), with age and sex included as covariates. Dependent variables comprised behavioral (number of omission and commission errors, reaction time, and reaction time variability) and neurophysiological measures (event-related potentials [ERPs]). As the main result, we identified significant differences between ADHD patients and healthy controls in all behavioral metrics, one neural marker of action inhibition (P3d) and the facial processing marker (N170). The differences were moderate-to-large when expressed as effect size measures in behavioral variables and small-to-moderate for neurophysiological variables. The small-to-moderate effect sizes obtained from the neurophysiological measures suggest that ERPs are insufficient as sole markers for effectively screening emotion and face processing abnormalities in ADHD.

FBXW7 in gastrointestinal cancers: from molecular mechanisms to therapeutic prospects

F-box and WD repeat domain-containing 7 (FBXW7), formerly known as hCdc4, hAGO Fbw7, or SEL10, plays a specific recognition function in SCF-type E3 ubiquitin ligases. FBXW7 is a well-established cancer suppressor gene that specifically controls proteasomal degradation and destruction of many key oncogenic substrates. The FBXW7 gene is frequently abnormal in human malignancies especially in gastrointestinal cancers. Accumulating evidence reveals that mutations and deletions of FBXW7 are participating in the occurrence, progression and treatment resistance of human gastrointestinal cancers. Considering the current therapeutic challenges faced by gastrointestinal cancers, elucidating the biological function and molecular mechanism of FBXW7 can provide new perspectives and references for future personalized treatment strategies. In this review, we elucidate the key molecular mechanisms by which FBXW7 and its substrates are involved in gastrointestinal cancers. Furthermore, we discuss the consequences of FBXW7 loss or dysfunction in tumor progression and underscore its potential as a prognostic and therapeutic biomarker. Lastly, we propose potential therapeutic strategies targeting FBXW7 to guide the precision treatment of gastrointestinal cancers.

Gesture-Driven Home Automation System

This project addresses the critical challenge of making home automation accessible and inclusive for individuals with physical disabilities and visual impairments. Existing home automation methods, such as voice control, mobile apps, and timers, exhibit shortcomings that hinder their effectiveness for this demographic. To overcome these limitations, the project shows the development of a gesture-based home automation system. This system integrates gesture recognition and security functionality. It operates in two stages: user authentication through face recognition and gesture-based appliance control. A Convolutional Neural Network (CNN) processes webcam-captured gestures, with signals relayed to an Arduino via Pyfirmata for appliance activation. A model was trained where it could detect the human hand(s) and more soshow the 20 landmarks that media pipe offers. The system worked well in controlling various home appliances using gestures. Through this innovative approach, the project contributes to the advancement of accessible smart home technology, fostering independence and inclusivity for users with diverse abilities.

The Influence of Vowels on the Identification of Spoken Disyllabic Words in the Malayalam Language for Individuals with Hearing Loss.

The present study investigates the reasons for better recognition of disyllabic words in Malayalam among individuals with hearing loss. This research was conducted in three experiments. Experiment 1 measured the psychometric properties (slope, intercept, and maximum scores) of disyllabic wordlists. Experiment 2 examined PBmax scores across varying degrees of sensorineural hearing loss (SNHL) and compared these findings with studies in other Indian and global languages. Experiment 3 analyzed the recognition performance of different vowel combinations across varying degrees of hearing loss. Experiment 1: Psychometric functions for disyllabic word recognition were derived from 45 individuals with normal hearing. Word recognition was tested in quiet at nine hearing levels ranging from -10 to +40 dB HL. Experiment 2: 1000 participants with SNHL were categorized by hearing loss severity (mild, moderate, moderately severe, severe, and profound). Word recognition scores, including PBmax, were analyzed and compared across severity levels. Experiment 3: Percent error scores for 17 vowel combinations were assessed in 37 participants with SNHL. Ten disyllabic words represented each combination. Disyllabic wordlists showed significantly higher word recognition scores than monosyllabic lists across all degrees of hearing loss. Individuals with mild-to-moderately severe SNHL achieved higher PBmax scores, with performance declining at severe- and profound-loss levels. The higher recognition of disyllabic words was attributed to contextual cues and low-frequency vowel-based information, particularly benefiting those with residual low-frequency hearing. Error analysis highlighted the influence of specific vowel combinations on word recognition performance. Disyllabic words are easier to recognize than monosyllabic words for individuals with SNHL due to their rich contextual and low-frequency energy cues. Disyllabic wordlists sustain higher recognition scores up to moderately severe hearing loss but show a marked decline with more severe losses. The phonemic balance of wordlists and vowel combinations significantly influences word recognition, emphasizing the importance of these factors in developing wordlists for clinical use.

Research on the Application of Artificial Intelligence in Mental Health Literacy Service for Middle School Students

Artificial Intelligence (AI) is applied to improve the mental health literacy of middle school students. It can effectively improve the efficiency, effectiveness and feasibility of this service. Through the current big data analysis and machine autonomous learning technology, AI can analyze the characteristics of students' behavior patterns and physiological data by training the model used. This paper examines how artificial intelligence can improve the mental health literacy of middle school students. It begins by analyzing and interpreting three key factors that influence their understanding of mental health. Firstly, it analyzes and interprets the three factors that affect the mental health literacy of middle school students. Then from these three factors, the author discusses how artificial intelligence can be applied to enhance the mental health literacy of middle school students. That is to say, it puts forward the new application of artificial intelligence technology in mental health counseling, emotional recognition, mental health assessment, media publicity and education. For example, the use of artificial intelligence to develop virtual psychological teachers, through language processing analysis and emotional recognition to provide online counseling services; It develops emotion recognition function through video and speech analysis of artificial intelligence, and uses digital signal processing means. Through the comprehensive analysis of a large number of data, the mental health evaluation system is gradually intellectualized, so that the evaluation model standard is more targeted.

Abnormal tRNA epigenetic modifications and related impact on neurodegenerative diseases.

Neurodegenerative diseases are a heterogeneous group of neurological disorders characterized by the progressive loss of neurons in the central or peripheral nervous system. Research on the pathogenesis and drug targets of these diseases still faces many challenges due to the complex etiology. In recent years, the role of transfer RNA (tRNA) epigenetic modifications in neurodegenerative diseases has attracted widespread attention. The tRNA modification is crucial for regulating codon recognition, maintaining molecular structural stability, and the generation of tRNA-derived fragments (tRFs). Recent studies have highlighted a close association between abnormal tRNA modifications and the pathogenesis of various neurodegenerative diseases; especially for abnormalities of elongator complex-dependent tRNA modification and methylation modification, which impact the translation process and tRFs levels. These changes regulate protein homeostasis and cellular stress responses, ultimately influencing the survival of neuronal cells. Moreover, significant changes in tRFs levels have been noted in neurodegenerative diseases, and special tRFs show distinct effects on neurodegenerative diseases. This review aims to provide an overview of the physiological functions of tRNA epigenetic modifications and their regulatory mechanisms in neurodegenerative diseases, covering both classical functions such as codon recognition and non-classical functions such as tRFs biogenesis. Additionally, the potential of targeting tRNA modifications for therapeutic applications is also discussed.

Hypoimmunogenic CD19 CAR-NK cells derived from embryonic stem cells suppress the progression of human B-cell malignancies in xenograft animals.

Chimeric antigen receptor (CAR) engineered natural killer (NK) cells exhibit advantages such as MHC-independent recognition and strong anti-tumor functions. However, allogeneic CAR-NK cells derived from human tissues are heterogeneous and susceptible to clearance by hosts. We generated a B2M knockout, HLA-E and CD19 CAR ectopic expressing embryonic stem cell (ESC) line, which differentiated normally and gave rise to homogeneous CD19 CAR-NK (CD19 CAR-UiNK) cells using an organoid aggregate induction method. The CD19 CAR-UiNK were co-cultured with T cells or NK cells derived from peripheral blood mononuclear cells (PBMC) with the mismatched HLA to evaluate the immunogenicity of CD19 CAR-UiNK cells. We further assessed the therapeutic effects of CD19 CAR-UiNK cells on CD19+ tumor cells through in vitro cytotoxicity assays and in vivo animal models. The CD19 CAR-UiNK cells exhibited typical expression patterns of activating and inhibitory receptors, and crucial effector molecules of NK cells, similar to those of unmodified NK cells. In co-culture assays, the CD19 CAR-UiNK cells evaded allogeneic T cell response and suppressed allogeneic NK cell response. Functionally, the CD19 CAR-UiNK cells robustly secreted IFN-γ and TNF-α, and upregulated CD107a upon stimulation with Nalm-6 tumor cells. The CD19 CAR-UiNK cells effectively eliminated CD19+ tumor cells in vitro, including B-cell cancer cell lines and primary tumor cells from human B-cell leukemia and lymphoma. Further, the CD19 CAR-UiNK cells exhibited strong anti-tumor activity in xenograft animals. We offer a strategy for deriving homogeneous and hypoimmunogenic CD19 CAR-iNK cells with robust anti-tumor effects from ESCs. Our study has significant implications for developing hypoimmunogenic CD19 CAR-NK cell therapy using human ESC as an unlimited cell source.

DNA Nanolock-Based Logic Gate-Directed Reciprocal Feedback for Stepwise Cell Typing and Combination Treatment.

To achieve accurate molecular diagnosis and early-stage intervention of disease on demand, there is an urgent need for the monitoring of multiple biomarkers and multipath information acquisition in living cells. The DNA combinatorial logic gate is an appropriate strategy for providing a systematic proof of concept with comprehensive information and function. Herein, a modular DNA logic gate nanomachine is designed for sufficient multistep reciprocal cell identification and therapy via the iteration of simple logic operations. In this logic gate system, this main module is constructed by G-quadruplex-locked gold nanocages (AuNCs), serving dual functions of drug encapsulation and cell recognition. The logic system is composed of OR, XNOR, AND, and NOR gates employing two intracellular disease biomarkers (microRNA 21 and microRNA 155) as inputs and the fluorescence signal of doxorubicin (Dox) as an output. The output signals of the four logic gates are iterated to process the imaging analysis data from the complex matrix in the living cell. Via positive and negative reciprocal feedback, the series circuit of different gates enables different functions, including the preliminary screening and the distinction of the cell type. Through the mutual preliminary screening and further proof, this logic system achieves accurate identification of cells, controlled drug release, and photothermal treatment using the AuNC as a photothermal transducer. This DNA logic system broadens the applications of the biocomputing system in disease screening and logic-controlled treatment fields.

Electroactive supramolecular nanoprobes for electrochemical detection of rituximab in non-Hodgkon’s lymphoma patients

Polymeric supramolecular assemblies (PSAs) exhibit excellent chemical stability, mechanical integrity, and multifunctionality, which could drive the development of biosensors. However, trapped by the significant differences in the physicochemical properties and structures of recognition ligands and signal tags, PSAs that simultaneously carry ligands and signals have rarely been reported for bioanalysis. In this study, an amphiphilic block copolymer (PVIM-b-PFMMA) was controllably synthesized by reversible addition fragmentation chain transfer (RAFT) radical polymerization for the first time. The resultant copolymer can self-assemble into highly electroactive supramolecular nanoprobes (eSNPs) with integrated target recognition and signal amplification functions. Compared to traditional electroactive probes based on conductive nanomaterials or enzyme catalysis, these versatile eSNPs avoid disadvantages including high cost, tedious modification, low load capacity and leakage of signal tags. For the rapid, sensitive and accurate monitoring of rituximab in biological fluids, a new electrochemical biosensor was constructed by coupling eSNPs with mimotope peptide CN14-modified screen-printed gold electrodes. The developed biosensor achieved a limit of detection (LOD) as low as 25 pg/mL and was successfully used to monitor rituximab concentration in a series of serum samples from non-Hodgkon’s lymphoma patients. Compared with commercial enzyme-linked immunosorbent assay (ELISA) and previously reported methods, the proposed biosensor demonstrated higher sensitivity, lower cost, simpler operation and more efficient preparation, owing to the unique superiority of eSNPs. Overall, this study presents a promising pathway for developing PSAs-based biosensors and their applications in bioanalysis.

When France Advised Syria: Cooperation as a Principle of Recognition, and its Limits

Abstract This article is a re-examination of cooperation in international relations through the example of Franco-Syrian relations between 2001 and 2004. I explore how cooperation fulfils – or not – a function of recognition, especially in the case of countries under unequal terms. Based on archival research and interviews, the article analyses three concrete moments of cooperation between Syria and France during this time. While traditional Franco-Syrian cooperation, prior to 2000, was based on a mutual recognition of partners, in the aftermath of presidential change in Syria in 2000 and within the shifting regional context, the Franco-Syrian relationship seemed to be marked by an inequality. Therefore, the article aims to capture the symbols of this recognition between France and Syria, and to study the role of cooperation not only in deepening a bilateral relationship, but – in a new perspective in international relations – in undermining it.

Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images

BackgroundThe Food and Drug Administration mandates patient labeling materials like the Medication Guide (MG) and Instructions for Use (IFU) to support appropriate medication use. However, challenges such as low health literacy and difficulties navigating these materials may lead to incorrect medication usage, resulting in therapy failure or adverse outcomes. The rise of generative AI, presents an opportunity to provide scalable, personalized patient education through image recognition and text generation. ObjectiveThis study aimed to evaluate the accuracy and safety of medication instructions generated by ChatGPT based on user-provided drug images, compared to the manufacturer's standard instructions. MethodsImages of 12 medications requiring multiple steps for administration were uploaded to ChatGPT's image recognition function. ChatGPT's responses were compared to the official IFU and MG using text classifiers, Count Vectorization (CountVec), and Term Frequency-Inverse Document Frequency (TF-IDF). The clinical accuracy was further evaluated by independent pharmacists to determine if ChatGPT responses were valid for patient instruction. ResultsChatGPT correctly identified all medications and generated patient instructions. CountVec outperformed TF-IDF in text similarity analysis, with an average similarity score of 76%. However, clinical evaluation revealed significant gaps in the instructions, particularly for complex administration routes, where ChatGPT's guidance lacked essential details, leading to lower clinical accuracy scores. ConclusionWhile ChatGPT shows promise in generating patient-friendly medication instructions, its effectiveness varies based on the complexity of the medication. The findings underscore the need for further refinement and clinical oversight to ensure the safety and accuracy of AI-generated medical guidance, particularly for medications with complex administration processes.

Prenatal ethanol exposure impairs hippocampal plasticity and cognition in adolescent mice

BackgroundPrenatal alcohol exposure (PAE) induces a wide range of neurodevelopmental disabilities that are grouped under the term ‘fetal alcohol spectrum disorders’ (FASD). The effects of PAE on brain development are dependent on complex neurochemical events, including modification of AMPA receptors (AMPARs). We have recently found that chronic ethanol (EtOH) exposure decreases AMPA-mediated neurotransmission and expression through the overexpression of the specific microRNA (miR)137 and 501-3p, which target GluA1 AMPA subunit, in the developing hippocampus in vitro. Here, we explored how PAE mice may alter AMPAergic synapses in the hippocampus, and its effects on behavior. MethodsTo model PAE, we exposed C57Bl/6 pregnant mice to 10 % EtOH during during the first 10 days of gestation (GD 0–10; equivalent to the first trimester of pregnancy in humans). AMPA subunits postsynaptic expression in the hippocampus, electrical properties of CA1 neurons, memory recognition, and locomotor functions were then analyzed in adolescent PAE-exposed offspring. ResultsPAE adolescent mice showed dysregulation of AMPAergic neurotransmission, and increased miR 501-3p expression, associated with a significant reduction of spontaneous AMPA currents and intrinsic somatic excitability. In addition, PAE reduced the phosphorylation of AMPAR-containing GluA1 subunit, despite an increase in its total levels. Of note, the total levels of GluA2 and GluA3 AMPA receptors were enhanced as well. Consistently, at behavioral level, PAE reduced object recognition without altering locomotor activity. ConclusionsOur study shows that PAE leads to dysfunctional formation of AMPAergic synapses that could be responsible for neurobehavioral impairments, contributing to the understanding of the pathogenesis of FASD.

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses.

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

A multilevel resistive switching memristor based on flexible organic–inorganic hybrid film with recognition function

Abstract Brain-inspired neuromorphic computing systems fueled the emergence of memristor-based artificial synapses, however, conventional silicon-based devices restricted their usage in the wearable field because of their difficulty in bending. To tackle the above challenge, a vertically structured flexible memristor with aluminum-based hydroquinone organic–inorganic hybrid film and Al2O3 as the functional layer, ITO and Pt as the bottom and top electrodes, and PET as the substrate has been developed utilizing molecular/atomic layer deposition to achieve a tradeoff between the resistive transition properties and the flexibility of memristors. The obtained devices combine stable resistive switching behavior and flexibility, showing high switching ratio of 103, better retention (up to 105 s) and endurance properties (up to 104 cycles), and robustness at radius of curvature of 4.5 mm after 104 bending cycles. Furthermore, the presence of multilevel resistive states in these devices ensures that the memristor can emulate synaptic properties such as paired-pulse facilitation, transition from short-term plasticity to long-term plasticity, long-term potentiation and depression, and spike-time-dependent plasticity. The resistive switching mechanism and the role of the bending state on the electrical performance of the device are explored. The fully connected artificial neural network based on the memristor can achieve a recognition accuracy of 90.2% for handwritten digits after training and learning. Flexible memristor will bring feasible advances to the integration of neuromorphic computing and wearable functionality.

Establishment and application of autoverification system for HbA1c testing.

This study aimed to determine autoverification rules for routine glycated hemoglobin (HbA1c) analysis based on high-performance liquid chromatography (HPLC) principle. Laboratory information system (LIS) and Bio-Rad D-100 Advisor software (Bio-Rad, Hercules, USA) with graphics recognition function were carriers for the autoverification system. A total of 105,126 HbA1c results, including 98,249 HbA1c matching fast plasma glucose (FPG) results of real-world data from May 2019 to June 2020, were collected to determine autoverification rules including flags, delta checks, reporting limits, and logical rules. The validation database was composed of 48,045 HbA1c results and 41,083 matching FPG results. Autoverification passing rate and the reduction of turnaround time (TAT) were evaluated. Four autoverification systems (A, B, C, D) were established by two types of delta check rules, 28 flags, one reporting limits, and two kinds of logical rules. The autoverification passing rates were 80.6%, 78.8%, 83.7%, and 81.3%, and the average time saved in TAT were 117.5 min, 116.7 min, 121.1 min, and 121.7 min, respectively. Autoverification system C was the optimal one. Application of distribution of FPG corresponding to HbA1c groups had better performance as logical rules. Established HbA1c autoverifcation system shortened the auditing report time and improved work efficiency.

Digital health apps : How to achieve the successful integration into healthcare

Digital health applications (apps) have been available on prescription since 2019 and offer a multitude of functions in health monitoring, symptom recognition, follow-up monitoring and patient care. The Digital Care Act from 2023 strives for a comprehensive integration of apps into medical care. The utilization of artificial intelligence (AI) in health apps promises an earlier diagnosis, sensitive symptom monitoring, optimization of courses of treatment, more effective doctor-patient communication and saving of resources. The integration of health apps and AI can enable a stronger personalized high-value care.