Applied family systems theories focus on determinants that can be operationalized as intervention practices. The analyses described in this paper focused on the direct and indirect effects of four family systems practices , parents’ psychological health , and parenting quality  in families of children with identified disabilities, medical conditions, or at-risk conditions for poor outcomes; (2) Methods: Data from previously completed meta-analyses of the relationships between family systems practices and parents’ psychological health outcomes and parenting quality outcomes were reanalyzed. Next, a meta-analysis of the relationships between parents’ psychological health and parenting quality was completed to identify which predictors were related to which parenting quality outcomes. Both main effects and mediated effects were examined; (3) Results: The four family systems practices were each related to six different psychological health measures and three parenting quality measures. The six different parental psychological health measures were also related to the three parenting quality measures. The relationships between family systems practices and parenting quality were partially mediated by parents’ psychological health; (4) Conclusions: The effects of family systems practices and parents’ psychological health on parenting quality were primarily direct and independent. The relationships between family systems practices and parenting quality were partially mediated by parents’ psychological health. Future research should focus on the identification of other mediator variables found to be important for explaining the indirect effects of family systems practices measures on parenting beliefs, behavior, and practices. Parenting quality is a multidimensional construct that includes parenting beliefs, parental involvement in children’s learning, and parenting practices to promote child learning and development ,2,3,4,5.Decades of research find that parenting quality matters a great deal in terms of positive child well-being, learning, and development ,15,16,17The birth and rearing of a child with special needs  or raising a child under adverse conditions  are often associated with increased parenting stress and burden . The. The46])Considerable effort has been expended by the author and his colleagues in investigating the relationships among the family systems intervention practices and parent, family, and child outcomes. This has included meta-analyses of practitioner use of family-centered practices ,50, famiResults from the meta-analyses showed that large numbers of unmet family needs are associated with poorer parent psychological health and well-being, but that family-centered practices, family resources, social supports, and family strengths are associated with attenuated poor parent psychological health and enhanced parent well-being. Moderator analyses found that the sizes of effects for the relationships between the family systems practices and parents’ psychological health were much the same for parents of children with identified disabilities, developmental delays, special health care needs, and children at risk for poor outcomes ,55,57,60A number of meta-analyses include findings regarding the relationships between the family systems intervention practices and parenting quality ,57,58,60The findings in the different research syntheses of the family systems intervention practices studies are all consistent with the basic premises of the applied family systems intervention model . There aThese methodological differences were addressed in this study by reorganizing, recoding, and reanalyzing the data in the meta-analyses so that the categorization of the psychological health measures  and parenting quality measures  were the same in each meta-analysis, which was the focus of additional analyses described in this paper. This also involved a meta-analysis of the relationships between parents’ psychological health and parenting quality so that the data necessary to conduct meta-analytic mediational analyses could be performed .This study addresses both the “lack of understanding regarding the [relationships] between specific dimensions of parental mental health and parenting” and the mechanism for understanding if and how intervention-related variables are directly or indirectly related to parenting quality, as described in the call for papers for the Special Issue of IJERPH on Parenting and Mental Health. Accordingly, correlational meta-analyses were used to test the hypothesized relationships between the family systems practices measures and the psychological health and parenting quality measures. This type of meta-analysis uses the correlations between variables of interest to determine the strength of relationships between the independent and dependent variables; specifically, weighted average correlation coefficients provide the best estimates of population effect sizes between measures.The intervention-related variables that were the focus of investigation included family needs, family resources, family supports, and family strengths, as well as their relationships with parents’ psychological health and parenting quality. The relationships between six different psychological health measures  and three different parenting quality measures  were also a focus of investigation.Ascertain the relationships between family systems intervention practices and parents’ psychological health .Ascertain the relationships between family systems intervention practices and parenting quality .Ascertain the relationships between parents’ psychological health and parenting quality .Ascertain the indirect relationships between family systems intervention practices and parenting quality mediated by parents’ psychological health .Ascertain whether the relationships between family systems intervention practices and parenting quality are partially, completely, or not at all mediated by parents’ psychological health .Ascertain if the targets of appraisals of the psychological health measures (parent-focused vs. nonparent-focused) differentially influenced the mediated effects between family systems practices and parenting quality .The specific aims of the study were to:The results were expected to identify which family systems intervention practices measures and which psychological health measures proved most important in terms of explaining variations in parenting beliefs, parental involvement, and parenting practices. The results were also expected to ascertain if the hypothesized relationships between the family systems intervention practices and parents’ psychological health and parenting quality were supported by the findings in the studies included in the meta-analyses.The guidelines described by Siddaway et al.  for condThe search sources for the studies in each of the family systems practices meta-analyses were PsycNet, ProQuest Central, PubMed, ERIC , Google Scholar, Directory of Open Access Journals, Bielefeld Academic Search Engine, and CORE. Controlled vocabulary searches were used in PsycNet, ProQuest Central, PubMed, and ERIC. Natural language searches were used in all eight search engines. The same sources were used to locate studies of the relationships between parents’ psychological health and parenting quality.The search terms for locating studies in each of the family systems practices meta-analyses are described in detail in the research reports. This included the names of the scales used to measure each of the systems practices found in the literature and the terms used to describe each family systems practice construct also found in the literature. The search terms for locating the psychological health measures studies included the names of the scales in the family systems practices meta-analyses, the names of other psychological health measures, and the terms used to describe different types of psychological health constructs found in the literature. These included, but were not limited to, general psychological health, depression, psychological stress, psychological distress, stressors, stressful life events, anxiety, well-being, quality of life, parenting stress, parenting distress, caregiving burden, and caregiver burden. The search terms for the parenting quality studies included the names of the scales in family systems practices meta-analyses and the terms used in the literature for describing different types of parenting beliefs, parenting behavior, and parenting practices. These included, but were not limited to, parent self-efficacy, parenting self-efficacy, parenting beliefs, parenting competence, parental involvement, parental engagement, parent-child activities, parenting practices, parent-child interactions, and parent-child relationships.Studies were included in the family systems practices meta-analyses if self-report measures of the family systems practices of interest were completed by the study participants and the correlations with either or both psychological health and parenting quality measures were reported. Studies were also included if self-report measures of one or more psychological health measures were completed by the study participants and the correlations with any of the three parenting quality constructs were reported. The study participants were parents or other primary caregivers  of children with identified disabilities, special health care needs, or children at risk for poor outcomes from birth to 18 years of age. No limitations were placed on the type of research report, where the studies were conducted, or the year of publication.Studies were excluded if only significant correlations between measures or incomplete correlations were reported, the study participants were not parents or primary caregivers of one of the three targeted groups of children and adolescents, the parents or primary caregivers had a diagnosis of a mental health or medical health condition, or insufficient information was included to ascertain the direction of effects between the independent and dependent measures. Studies were also excluded in the meta-analysis of the relationships between the psychological health and parenting quality measures if they were conducted during the COVID-19 pandemic, since the studies in the family systems practices meta-analyses were conducted before that adverse event. This exclusion was chosen so as not to add confounds to the results reported in this paper.The meta-analyses used in this study included family systems practices measures, psychological health measures, and parenting quality measures. The complete list of scales in each family systems meta-analysis can be found in the research reports described above. The psychological health and parenting quality measures in the meta-analysis completed for this study can be found in the The family systems practices were measured using different family needs scales ,71, sociSix different psychological health constructs were the focus of investigation in the different family systems practices meta-analyses, as well as the meta-analysis completed for this paper. The constructs and associated measures included general psychological health ,79,80, dThree types of parenting quality measures were the focus of investigation: parenting beliefs, parental involvement, and positive parenting practices. Parenting beliefs included parents’ attitudes toward childrearing , parentsThe zero-order correlations between the different sets of measures and associated sample sizes were inputted into Meta-Essentials  in orderPreliminary analyses were conducted for each set of measures to identify outliers and influential cases . An iterBoth main and indirect effect analyses were conducted to achieve the aims of the study. Three sets of analyses were conducted to test the main effects of the relationships between the study variables. The first set of analyses evaluated the relationships between the four family systems practices measures  and the six psychological health measures .The second set of analyses evaluated the relationships between the four family systems practices measures and the three parenting quality measures . The third set of analyses evaluated the relationships between the six psychological health measures and the three parenting quality measures.The fourth set of analyses evaluated the extent to which the relationships between the family systems practices measures and the parenting quality measures were mediated by the psychological health measures. This was determined by the product of the average sizes of effect between the family systems intervention practices and the psychological health measures and the average sizes of effect between the psychological health and parenting quality measures.Most of the family systems meta-analyses included between 30 and 82 studies and 3303 to 7675 participants. The parents’ psychological health meta-analysis included 108 studies and 21,784 participants. The studies in all of the meta-analyses were found in both peer-reviewed journal articles and non-peer-reviewed sources. The latter included primarily doctoral dissertations and master’s theses. The primary caregivers in most studies were mothers of children with identified disabilities/developmental delays, medical conditions, or children at risk for poor outcomes. The children ranged in age from less than one year to 18 years of age. The average child age ranged between 6 and 12 years.The relationships between the four family systems practices and the six different psychological health measures are shown in The direction of effects was as expected. A greater number of family needs were associated with poorer general psychological health, depression, stress, caregiving burden, and parenting stress, as well as less positive psychological well-being. In contrast, the presence of more family resources, more social supports, and more family strengths was associated with attenuated poor psychological health and enhanced psychological well-being.Inspection of the sizes of effect between the family systems practices and the psychological health measures show that effect sizes are much the same for family needs, family resources, and family strengths. In contrast, the sizes of effects between the family supports and psychological health measures are much smaller but still significantly different from zero.Inspection of the sizes of effect for the relationships between the family systems practices and parenting quality measures shows that the effect sizes for family strengths are larger than those for the other family systems practices measures and are almost twice as large as those for family supports. These results, together with those in The relationships between the six psychological health measures and the three parenting quality measures are shown in Inspection of the sizes of effects between the psychological health and parenting quality measures shows that the effect sizes between depression, stress, well-being, parenting stress, and parenting quality are larger for parenting beliefs compared to parental involvement and parenting practices. In contrast, the sizes of effect between general psychological health, caregiving burden, and the three parenting quality measures were much the same.The sizes of effects between the family systems practices, psychological health, and parenting quality measures were considered the best estimates for determining if the psychological health measures mediated the relationships between the family systems practices and parenting quality measures. For each of the family systems practices measures, two sets of psychological health measures were used in the mediated analyses: the aggregated effect sizes for the four nonparent-focused measures  and the two parent-focused measures (parenting stress and caregiving burden). This permitted a determination of whether the targets of appraisals of the psychological health measures  influencA comparison of the mediated effects in Examination of the total effects for the family systems measures shows that the sizes of effect for the family strengths measures are 40 or larger for all three parenting quality measures and are nearly the same for both the parent-focused and nonparent-focused psychological health measures. The only other total effect sizes that are 40 or larger are for the relationships between family needs and parenting beliefs for both the parent-focused and nonparent-focused psychological health measures, as well as for the relationship between family resources and parenting practices for the parent-focused psychological health measures.Results from the secondary meta-analyses showed that the four family systems practices were all related to the six parents’ psychological health measures (Aim 1). The results also showed that both the family systems practices and the parents’ psychological health measures were related to the three parenting quality measures (Aims 2 and 3). These results are consistent with the foundations of the applied family systems theory that guided the conduct of meta-analyses . This paThe results from the mediated analyses showed that the relationships between the family systems practices and parenting quality measures were partially mediated by parents’ psychological health (Aims 4 and 5) but accounted for only small amounts of variance between measures. The comparisons between the nonparent-specific and parent-specific psychological health measures showed that caregiving burden and parenting stress mediated somewhat more of the variance for the relationships between the family systems practices and parenting quality measures than did the general psychological health measures (Aim 6). This pattern of results is similar to that found in other studies where parent-focused but not nonparent-focused parenting stress mediated the relationships between family supports and resources and parenting quality ,104. BonThe main effects results for the relationships between both the family systems practices and psychological health measures and the three parenting quality measures inform an understanding of which family systems practices and psychological health measures are related to which dimensions of parenting as stated in the call for papers for the Special Issue of the IJERPH on Parenting and Mental Health. This can be ascertained from the results in The mediated effects findings also provide the basis for an understanding of the mechanisms for the relationships between intervention-related measures and parenting quality, as described in the call for papers for the special issue of the IJERPH on Parenting and Mental Health. The results from the two sets of mediated analyses  indicateThe results reported in this paper, as well as findings reported elsewhere (e.g., ,54,55,58Several limitations need to be mentioned to place the results in methodological and procedural perspective. First, the data in all of the meta-analyses are correlational where causal statements may not be warranted. The results reported in this paper simply indicate that there is covariation between the variables of interest where the findings are consistent with hypothesized relationships between the family systems measures, psychological health measures, and the parenting quality measures. Second, there is also the possibility that the obtained relationships between measures were affected by other unobserved variables or by statistical artifacts. These might have resulted in under- or over-estimation of the effect sizes between measures.There are also limitations related to the use of meta-analysis for aggregating results from different studies. First, combining results from studies that differ for any number of reasons may have resulted in “mixing apples and oranges”. For example, the use of different scales for measuring any one of the parenting quality measures may have resulted in suppression of the strength of the relationships between measures. Second, methodological considerations that were beyond the scope of this paper, such as moderator effects, might explain differences reported in the primary studies. These types of analyses are the next step in the line of research described in this paper.The relationships between both the family systems practices and the parents’ psychological health and parenting quality measures were almost entirely direct and independent. The relationships between the family systems practices measures and parenting quality were partially mediated by caregiving burden and parenting stress. Future research should focus on other explanatory variables that might better explain the indirect effects of family systems practices on parenting quality. Previous studies in the line of research described in this paper found that self-efficacy beliefs  proved t


Although cognitive impairment is a core symptom of schizophrenia related to poore outcome in different functional domains, it still remains a major therapeutic challenge. To date, no comprehensive treatment guidelines for cognitive impairment in schizophrenia are implemented.The aim of the European Psychiatric Association (EPA) guidance paper was to provide a comprehensive meta-review of the current available evidence-based treatments for cognitive impairment in schizophrenia, structured into three sections: pharmacological treatment, psychosocial interventions, and somatic treatments.Based on the reviewed evidence, the EPA guidance recommends an appropriate pharmacological management as a fundamental starting point in the treatment of cognitive symptoms in schizophrenia. Among psychosocial interventions, cognitive remediation and physical exercise are recommended for the treatment of cognitive impairment in schizophrenia. As for cognitive remediation, some variables have been confirmed as core elements for treatment effectiveness.The dissemination of the EPA guidance paper may promote the development of shared guidelines concerning the treatment of cognitive functions in schizophrenia, with the purpose to improve the quality of care and to achieve clinical recovery in this population.None Declared


The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms. The pediatric electroretinogram (ERG) is a measure of the electrical activity of the retina in response to light stimulation, typically performed on infants and children. The ERG signal consists of a series of positive and negative waveforms, labeled as a-wave, b-wave, and c-wave, which reflect the activity of different retinal cells . The a-wThe c-wave of the ERG is often considered less prominent and less studied compared to the a-wave and b-wave. The a-wave represents the initial negative deflection, reflecting the hyperpolarization of photoreceptors, while the b-wave represents the subsequent positive deflection, primarily originating from bipolar cells . These wIn contrast, the c-wave represents a slower, positive deflection following the b-wave, originating from the retinal pigment epithelium (RPE) and Müller cells . Its ampWhile the a-wave and b-wave are directly related to photoreceptor and bipolar cell function , the c-wERG may be of various types, depending on the specific electrophysiological protocol used. One type of ERG signal is the Scotopic 2.0 ERG response, which is obtained under conditions of low light intensity. This response is mainly generated by the rod photoreceptor cells in the retina and is characterized by a relatively slow and low-amplitude waveform. Another type of ERG signal is the Maximum 2.0 ERG response, which represents the maximum electrical response that can be elicited from the retina. This response is obtained under conditions of high light intensity and is mainly generated by the cone photoreceptor cells in the retina. The Maximum 2.0 ERG response is characterized by a faster and higher-amplitude waveform compared to the Scotopic 2.0 ERG response. The Photopic 2.0 ERG response is a third type of ERG signal that is obtained under conditions of moderate light intensity. This response is also mainly generated by the cone photoreceptor cells in the retina and is characterized by a waveform that is intermediate in both amplitude and latency between the Scotopic 2.0 ERG response and the Maximum 2.0 ERG response. It is important to note that the specific electrophysiological protocol used to obtain these different types of ERG signals can vary depending on the research question and the clinical application. Detailed information about the parameters of the electrophysiological study, including the light intensity, wavelength, and duration of the light stimulus, as well as the recording electrodes and amplification settings are shown in a previous study .The In a healthy subject in In an unhealthy subject in Alternatively, certain diseases may selectively affect specific components of the ERG waveform. For example, in some cases of congenital stationary night blindness, the b-wave may be reduced or absent while the a-wave remains relatively normal, indicating a specific defect in bipolar cell function ,13.Thus, the shape of the ERG signal in temporal representation provides valuable insights into the integrity and function of retinal cells and can aid in the diagnosis and understanding of various retinal diseases and disorders .In this study, we implemented trained deep-learning models using images of wavelet scalograms to determine the optimal mother wavelet. This approach differs from previous works, where the authors independently searched for features from the time-frequency representation of the signal. It should be noted that deep learning is optimal for image classification because it allows for the extraction of high-level features from raw data, which can result in higher accuracy. While adult ERGs can be standardized by establishing norms for various parameters, pediatric ERGs are less specific as the amplitude and latency of such ERGs can vary considerably. Consequently, diagnosis often necessitates the use of supplementary diagnostic methods. The study’s scientific novelty resides in utilizing deep learning techniques to identify optimal mother wavelet for pediatric ERGs, an approach that can also be applied to other types of ERG signals.The Wavelet analysis has been widely used to study ERG in the field of ophthalmology. In recent publications shown in Different wavelets emphasize various features of a signal, making it crucial to choose the most appropriate mother wavelet. In a previous study , researcThe use of the Morlet wavelet transform in ERG analysis has been shown to provide a more comprehensive analysis of the data. For example, in , the MorIn , the aimFinally, in , the GauIn summary, the selection of the mother wavelet plays a crucial role in ERG analysis, and various factors should be taken into consideration to ensure an accurate and comprehensive analysis of the data.In this study signals from the IEEEDataPort repository were used, which is a publicly accessible ophthalmic electrophysiological signals database . The datAn under-sampling technique was employed using the AllKNN function from the Imbalanced-learn package ,27. The Thus, dataset balancing was implemented. Further, we split the train and the test subsets: undersampling the test set can lead to a biased evaluation of a model’s performance, which could be detrimental in real-world scenarios. Therefore, the test set represents the real-world distribution of healthy and unhealthy samples with an 85:15 ratio to the training subset.At the following stages of Training and Cross-validation, the wavelet scalogram images are subjected to classification utilizing the training and validation datasets. Ultimately, the efficiency of the image classification process is assessed in the Evaluation stage using the balanced metrics.The dataset under investigation contains signals comprising of 500 entries each, alongside their corresponding target (diagnosis). To perform an analysis, CWT was carried out on each signal using the PyWavelets library . The basThe training was independently performed on five widely used architectures in the field of deep learning: VGG-11, ResNet-50, DensNet-121, ResNext-50 and Vision Transformer. The choice of models above was based on their popularity and proven effectiveness in the field of image classification. These models have been widely used in various computer vision tasks, and their performance has been thoroughly evaluated on standard datasets such as ImageNet ,30,31. IVGG-11 is one of the most popular pre-trained models for image classification. Introduced in the ILSVRC 2014 Conference, it remains the model to beat even today . VGG-11 Residual Network (ResNet) is a specific type of convolutional neural network introduced in the paper “Deep Residual Learning for Image Recognition” by He Kaiming, et al., 2016 . ResNet-ResNext-50 is a simple, highly modularized network architecture for image classification. It was constructed by repeating a building block that aggregates a set of transformations with the same topology .DenseNet name refers to Densely Connected Convolutional Networks developed by Gao Huang, et al. in 2017 . In thisVision Transformer is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into patches with fixed size, each of the patches then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to Transformer encoder . In the We used ADAM optimization with 0.001 initial learning rate. Each model was trained until convergence using the early stopping criteria on the validation loss with batch size of 16 on a single NVIDIA V100.The loss function plays a critical role in deep learning. In this work, we utilize the most commonly used Cross-entropy loss function for classification tasks , which rN—the size of the training set.The incorporation of data augmentation techniques during the training process leads to an augmentation of the distributional variability of input images. This augmentation is known to enhance the resilience of models by increasing their capacity to perform well on a wider range of inputs. Given the characteristics of our dataset, we opted to apply exclusively geometric transformations such as random cropping, vertical flipping, and image translation to the images under consideration.Cross-validation is a resampling method that is employed to assess the effectiveness of deep learning models on a dataset with limited samples. The technique entails partitioning the dataset into k groups. To account for the limited nature of our dataset and facilitate a more objective evaluation of the trained models, we applied a five-fold cross-validation strategy in the present study. The test subset was first separated according to the real-world distribution of healthy and unhealthy clinical patients for each type of ERG response. The remaining shuffled training subset was then divided into five folds of which one is used for validation and four for training. The process is repeated for five experiments, using every fold once as the validation set.For each experiment, a confusion matrix was constructed using the test dataset, and the evaluation metrics were subsequently computed . This apFor a complete understanding of the model performance, several metrics were computed: Precision, Recall, and F1 score :(2)PreciSince the test subset reflects the real-world distribution and is not balanced, we should consider Balanced Accuracy :(5)BalanThe findings from The findings from The findings from More detailed values of the metrics are given in the The results from The selection of the mother wavelet in ERG analysis is of utmost importance as it directly influences the quality and interpretability of the results. Choosing an appropriate mother wavelet requires careful consideration of various factors to ensure accurate and comprehensive data analysis. Despite the abundance of literature on ERG analysis, there is a lack of a clearly formulated motivation for selecting a specific mother wavelet. Existing sources often fail to provide explicit reasoning or guidelines for choosing one wavelet over another in the context of ERG analysis. This gap in the literature hinders researchers from making informed decisions regarding the selection of the most suitable mother wavelet for their ERG data analysis, highlighting the need for further research and guidance in this area.The dataset comprised signals with 500 entries each, and CWT was applied using the PyWavelets library to generate 512 × 512 gray-scale images. The CWT was performed using different base functions, including Ricker, Morlet, Gaussian Derivative, Complex Gaussian Derivative, and Shannon.Deep learning architectures, namely VGG-11, ResNet-50, DenseNet-121, ResNext-50, and Vision Transformer, were independently trained on the dataset to establish baselines for performance comparison. These models have been extensively evaluated in computer vision tasks and have shown effectiveness in image classification. Training was carried out until convergence using the ADAM optimization with an initial learning rate of 0.001 and a batch size of 16 on a single NVIDIA V100.The performance of the models was evaluated using cross-validation with a five-fold strategy to account for the limited sample size. The dataset was divided into training and test subsets, and the training subset was further partitioned into five folds for validation. Confusion matrices were constructed using the test dataset, enabling the computation of evaluation metrics such as precision, recall, F1 score, and balanced accuracy. This approach provided a comprehensive assessment of the models’ performance across different folds and allowed for a detailed analysis of correct and incorrect predictions made by the models.The results demonstrated the effectiveness of the deep learning models in classifying the signals and diagnosing the corresponding conditions. Overall, the Vision Transformer model exhibited the highest performance, achieving the highest precision, recall, F1 score, and balanced accuracy among the tested architectures. The VGG-11, ResNet-50, and DenseNet-121 models also displayed strong performance, while the ResNext-50 model achieved slightly lower metrics. These findings highlight the potential of deep learning models, particularly the Vision Transformer architecture, in analyzing signal datasets and facilitating accurate diagnoses. The study’s results contribute to the understanding of the applicability of deep learning techniques in medical diagnostics and pave the way for future research in this domain.The choice of wavelet for ERG signal analysis depends on the waveform characteristics, with different wavelets having varying frequencies and temporal resolutions. An optimal wavelet should possess effective noise suppression capabilities , accuratWavelet characteristics: the specific properties of the Ricker Wavelet, including its shape and frequency properties, align well with the features present in ERG wavelet scalograms, leading to improved accuracy in classification compared to other wavelet types.Noise suppression capabilities: the Ricker Wavelet demonstrates superior noise suppression capabilities, effectively reducing unwanted noise in ERG wavelet scalograms while preserving important signal components, resulting in enhanced accuracy.Time-frequency localization: the Ricker Wavelet excels in accurately localizing transient and sustained components of ERG waveforms across different time intervals, enabling better capture and representation of crucial temporal features, thereby increasing the discriminative power of the wavelet in classifying ERG responses.The Ricker Wavelet yielded the highest median accuracy values for ERG wavelet scalogram classification due to the following potential reasons:It should be noted that the present study utilized a limited set of signals to identify the most appropriate mother wavelet for ERG analysis. Nevertheless, the sample was well-balanced, which lends confidence to the relatively stable classification outcomes.In comparison to electrophysiological data, which typically contain numerous parameters describing motor function, ERG analysis necessitates the addition of significant parameters to ensure the efficient classification of specific states. As ERG analysis involves only four parameters , insuffiSelected neural networks may exhibit superior performance when trained on larger datasets. For instance, the accuracy distribution of the Transformer model displays a wide range ; howeverAs the ERG signals are equipment- and intensity-specific, it is important to exercise caution when combining different datasets. An area of research that holds promise involves the creation of synthetic signals, which can augment the available training data.This research investigates the potential of AI algorithms in accurately classifying eye diseases and acknowledges their role as supportive tools to medical specialists. While the algorithms demonstrate good accuracy, we emphasize the indispensability of specialist involvement. The complex nature of human health, the significance of empathetic care, and the unparalleled decision-making capabilities of doctors underscore their ongoing essential role in delivering comprehensive and holistic medical care. Classification algorithms can serve as clinical decision support systems, enhancing physicians’ expertise and facilitating more efficient and effective healthcare delivery ,47.Equipment limitations: The utilization of only the Tomey EP-1000 equipment for ERG registration introduces a potential limitation to the generalizability of the model’s results. As different equipment may exhibit variations in signal acquisition and measurement precision, the model’s performance and outcomes may differ when applied with alternative ERG registration devices. Furthermore, the use of a corneal electrode during ERG registration does not entirely eliminate the possibility of electrooculogram-induced noise stemming from the involuntary movement of the eye muscles . ConsequStudy protocol considerations: The employment of specific ERG protocols, namely Maximum 2.0 ERG Response, Scotopic 2.0 ERG Response, and Photopic 2.0 ERG Response, within this investigation, ensures the acquisition of ERG recordings with optimal quality on the employed equipment . HoweverDataset limitations: The dataset used in this study comprises data from both healthy subjects and subjects with retinal dystrophy . While tNeural network feature limitations: While neural networks have shown improved performance with larger datasets, it is essential to consider the limitations associated with the available data. The accuracy of the chosen neural networks, especially the Transformer, increases with an increase in the training subset. However, the available datasets are limited, and the different nature of the origin of the signals makes it difficult to combine such datasets.To overcome equipment limitations in ERG recordings, the use of Erg-Jet electrodes can be considered . These eRegarding study protocol limitations, the adoption of standardized protocols ensures a uniform approach to ERG recordings. Furthermore, presenting detailed statistical data about the dataset used in the study helps address study protocol limitations by providing transparency and enabling researchers to evaluate the robustness and generalizability of the findings.To mitigate dataset limitations, it is crucial to expand the ERG dataset and include a broader range of retinal diseases. By increasing the size and diversity of the dataset, researchers can enhance the representativeness of the data and improve the model’s ability to generalize to various clinical scenarios. Incorporating additional retinal diseases beyond the scope of the current study would enable a more comprehensive understanding of the model’s performance and its applicability to a wider range of clinical conditions. Expanding the dataset can also help identify potential subgroups or rare conditions that may have specific ERG characteristics, contributing to the advancement of knowledge in the field. A promising development direction is the generation of synthetic ERG signals: combining the mathematical model of the signal and Generative Adversarial Networks . This wiThe results of this study indicate that the combination of Ricker Wavelet combined with Vision Transformer consistently achieves the highest median balanced accuracy values across all three ERG responses: 0.83, 0.85, and 0.88 consequently. The robust upper and lower quartile values provide compelling evidence for the superiority of this combination, consistently demonstrating high accuracy levels. However, it is important to acknowledge that other wavelet types also yield relatively high accuracy levels and should not be disregarded. These findings underscore the critical role of selecting an appropriate mother wavelet in determining the accuracy of ERG wavelet scalogram classification. Careful consideration of the wavelet type and transformer architecture holds significant potential for attaining even higher levels of accuracy. Overall, this study offers valuable insights into the effectiveness of different wavelet-model combinations, thereby contributing to the precise classification of pediatric ERG signals and advancing the field of healthcare.The findings of this study will be utilized to develop an AI-based decision support system in ophthalmology, leveraging the insights gained from ERG wavelet scalogram classification. This system aims to enhance ophthalmology-related applications by incorporating accurate and efficient analysis of ERGs. Furthermore, the results of this study may hold value for manufacturers of electrophysiological stations used in ERGs. The understanding of which wavelet types, such as the Ricker Wavelet, yield superior classification accuracy may guide the development and optimization of electrophysiological stations, enabling them to provide more reliable and advanced diagnostic capabilities in the field of ophthalmology.


IntroductionThis case study aimed to enhance the traceability and retrieval accuracy of ChatGPT-4 in medical text by employing a step-by-step systematic approach. The focus was on retrieving clinical answers from three international guidelines on diabetic ketoacidosis (DKA).MethodsA systematic methodology was developed to guide the retrieval process. One question was asked per guideline to ensure accuracy and maintain referencing. ChatGPT-4 was utilized to retrieve answers, and the 'Link Reader' plug-in was integrated to facilitate direct access to webpages containing the guidelines. Subsequently, ChatGPT-4 was employed to compile answers while providing citations to the sources. This process was iterated 30 times per question to ensure consistency. In this report, we present our observations regarding the retrieval accuracy, consistency of responses, and the challenges encountered during the process.ResultsIntegrating ChatGPT-4 with the 'Link Reader' plug-in demonstrated notable traceability and retrieval accuracy benefits. The AI model successfully provided relevant and accurate clinical answers based on the analyzed guidelines. Despite occasional challenges with webpage access and minor memory drift, the overall performance of the integrated system was promising. The compilation of the answers was also impressive and held significant promise for further trials.ConclusionThe findings of this case study contribute to the utilization of AI text-generation models as valuable tools for medical professionals and researchers. The systematic approach employed in this case study and the integration of the 'Link Reader' plug-in offer a framework for automating medical text synthesis, asking one question at a time before compilation from different sources, which has led to improving AI models' traceability and retrieval accuracy. Further advancements and refinement of AI models and integration with other software utilities hold promise for enhancing the utility and applicability of AI-generated recommendations in medicine and scientific academia. These advancements have the potential to drive significant improvements in everyday medical practice. Remarkable advancements in artificial intelligence and language processing capabilities have propelled AI text generative models, such as ChatGPT, to new heights of performance -4. TheseTo ensure a systematic and rigorous approach, this study employed a carefully designed methodology to retrieve, analyze, and compile information exclusively from three internationally recognized guidelines on treating diabetic ketoacidosis (DKA). The selected guidelines were from the United Kingdom , Canada Throughout the study, utmost attention was given to the accuracy and integrity of the information produced and cited. The results are presented as they were produced by the AI model, allowing verification and validation. The focus remained solely on presenting the final recommendations and related questions and answers without personal opinions or subjective analysis.This study provided a potential framework for automating medical text synthesis by implementing this systematic approach and leveraging advanced AI tools. The goal is to benefit medical professionals and patients by offering reliable and consolidated information from multiple reputable sources.ChatGPT-4, with the "Link Reader" plug-in, gathered relevant information and generated well-informed recommendations. The clear instructions provided, the systematic approach adopted, and allowing the model sufficient time to create responses all contributed to the positive results achieved in our study. We present the final recommendations in Table During the first phase of our study, we observed a consistent level of accuracy in the responses generated by the first prompt. For instance, when addressing the diagnostic criteria for DKA, the answers consistently aligned with the recommendations provided in the guidelines. The retrieved numbers always matched the specified thresholds across different guidelines, whether blood sugar levels were >14 mmol/L or >11 mmol/L. Similarly, when exploring the signs and symptoms of DKA, the generated answers consistently reflected those mentioned in each guideline.The compilation process, initiated with the second prompt, yielded highly accurate results. This ensured the inclusion of relevant information and appropriate citations throughout the compilation. The compiled answers were significantly more extensive than the final recommendations, further reinforcing the robustness of our study's outcomes. Notably, the generated recommendations demonstrated excellent quality, affirming the overall success of our research.During our study, we encountered some challenges accessing specific web pages and ensuring direct alignment with the intended questions. For instance, one of the sources provided diagnostic criteria instead of particular signs and symptoms, while another focused primarily on investigations related to the diagnosis of DKA. These challenges were addressed during the compilation stage, as ChatGPT-4 compiled the answers from the three sources, ensuring a comprehensive representation of the information.Additionally, we observed "memory drift," where the model generated answers that deviated from the original questions, requiring us to restart the process. However, it is essential to note that these limitations were relatively minor and did not significantly impact the overall findings of our study.It is essential to highlight that the results discussed in this study pertain specifically to the ChatGPT-4 model and the associated plug-in. These resources are currently available through paid access, which restricts their usage to a more limited audience, limiting their accessibility to the broader public.In conclusion, despite the difficulties and limitations encountered during our study, we maintained a commendable level of accuracy, reliability, and quality in the generated content and compiled recommendations. The output can be considered as a draft suitable for expert review.Our study aimed to assess a systematic methodology specifically developed to enhance the traceability and retrieval accuracy of ChatGPT-4, an advanced AI text generative model. By retrieving, analyzing, and synthesizing information solely from three internationally recognized guidelines on the treatment of DKA, our objective was to test the hypothesis that adopting a single-source, single-question approach from reliable sources would significantly improve the accuracy and traceability of the information generated by the model.This research contributes to the growing body of literature on successfully utilizing ChatGPT-4 for medical data retrieval, extraction, and compilation. The findings shed light on the potential of employing a focused methodology, supported by reliable sources and systematic techniques, to enhance the accuracy and traceability of information generated by ChatGPT-4 when applied to high-quality medical knowledge, such as medical guidelines. These results affirmed our expectations and underscored the importance of a meticulous methodology in enhancing AI-generated text's trustworthiness, verifiability, and applicability, particularly within academic and medical research.The remarkable accuracy demonstrated by the first prompt in reporting ensured the inclusion of relevant information accompanied by proper citations. Additionally, the subsequent recommendations and the synthesized data from the second prompt exhibited a commendable level of accuracy, reliability, and quality. These findings underscore the necessity of prioritizing accuracy, truthfulness, proper citation, and alignment with the original text throughout the synthesis process. By focusing on these critical elements, we generated content that closely adhered to the desired standards of reliability and traceability.While our study encountered particular challenges and limitations, such as difficulties accessing specific web pages and occasional alignment issues with the intended questions, our approach remained robust, resulting in comprehensive answers. Instances of 'memory drift' were observed but did not significantly impact our findings. Instead, we recognized these limitations as opportunities for further refinement of the AI model and its application, bolstering the process rather than impeding it.Our study provides compelling evidence for the utility and potential of AI text generative models in medical research, as long as their limitations are continuously addressed and mitigated. Our work represents a significant stride toward a broader goal. As AI continues to develop and advance, it is imperative to conduct further research to refine these tools and amplify their value within academic settings, particularly in medicine.Our methodology offers a promising framework for automating medical text synthesis and showcases an innovative approach to utilizing ChatGPT-4 and the 'Link Reader' plug-in. Given the rapid pace of development, additional alternatives will likely emerge in the coming weeks and months. These tools can serve as dependable chatbots, utilizing international guidelines to generate comprehensive answers rapidly. Moreover, our method can be adapted to access multiple international guidelines simultaneously, broadening the scope and strengthening the reliability of the gathered information.Beyond the specific context of our study, the systematic process of selecting reliable resources, formulating precise questions, and compiling consensus-driven information can be adapted across various domains within medical research and other scientific fields. The versatility of this approach highlights the expansive possibilities offered by advanced AI tools.We anticipate that the direct beneficiaries of our work will be medical professionals and patients, who will gain access to reliable, consolidated, and easily digestible information from numerous reputable sources. Additionally, the indirect benefits of our research extend to potentially revolutionizing how scientific knowledge is collated, presented, and interpreted.The content generated in our study, deemed suitable for expert review, not only represents a notable advancement in AI-assisted medical research but also showcases the broader transformative potential of AI in scientific inquiry. Our study marks a pivotal step toward enhancing AI-generated text's reliability, traceability, and utility within academic discourse.AI has revolutionized clinical decision-making in several areas. Treatment planning benefits from machine learning algorithms that suggest personalized treatment plans based on disease severity . ClinicaIn conclusion, this study aimed to enhance the traceability and retrieval accuracy of ChatGPT-4, an AI text generative model, in the context of medical text synthesis. By employing a systematic methodology and retrieving information from international guidelines on DKA, we successfully demonstrated the benefits of integrating ChatGPT-4 with the 'Link Reader' plug-in. Clear prompting instructions and a step-wise decomposition of the task strengthened the approach. Despite some limitations, such as occasional memory drift and challenges in accessing web pages, our system maintained a commendable level of accuracy and reliability.The findings of this study hold significant implications for AI-assisted medical research. By providing a framework for automating medical text synthesis and addressing limitations, our work contributes to developing AI text generative models as valuable tools for medical professionals and researchers. Further advancements in this field have the potential to enhance the utility and applicability of AI-generated recommendations in scientific academia. Continued research and refinement of AI models will be crucial in unlocking their full potential and driving advancements in medical research.


One Biosecurity is an interdisciplinary approach to policy and research that builds on the interconnections between human, animal, plant, and ecosystem health to effectively prevent and mitigate the impacts of invasive alien species. To support this approach requires that key cross-sectoral research innovations be identified and prioritized. Following an interdisciplinary horizon scan for emerging research that underpins One Biosecurity, four major interlinked advances were identified: implementation of new surveillance technologies adopting state-of-the-art sensors connected to the Internet of Things, deployable handheld molecular and genomic tracing tools, the incorporation of wellbeing and diverse human values into biosecurity decision-making, and sophisticated socio-environmental models and data capture. The relevance and applicability of these innovations to address threats from pathogens, pests, and weeds in both terrestrial and aquatic ecosystems emphasize the opportunity to build critical mass around interdisciplinary teams at a global scale that can rapidly advance science solutions targeting biosecurity threats.  Natural sciences; Ecology; Biological sciences It also includes human wellbeing including protection from non-infectious diseases, food-borne hazards and immunological mediated hypersensitivity .Animal Health: Consistent with the World Organization for Animal Health (WOAH) it includes the prevention and management of epizootic diseases that affect terrestrial and aquatic animals, including wildlife as well as animal welfare. This includes an understanding of invasive species as vectors of animal diseases but also their detrimental role in animal welfare through harm because of bites, stings, poisoning, dermatitis, photosensitization etc.Plant Health: Consistent with the International Plant Protection Convention, plant health is an overarching term for emerging risks including pests, diseases and weeds, integrated pest management and innovation in plant protection. The definition extends beyond the protection of cultivated plants to the protection of natural flora and plant products. It also takes into consideration both direct and indirect damage by pests, so it includes weeds.Ecosystem Health: Ecosystem health is a holistic measure describing the general condition of an ecosystem in relation to the impacts of environmental change drivers such as pollution, overharvesting, land-use change, climate change and the effects of alien pathogens, pests, and weeds. Although there is no standard benchmark as to what qualifies as a healthy ecosystem, the term is frequently applied to portray the state of ecosystems in relation to their conservation status. In the context of the present article, the emphasis on ecosystem health is primarily on the factors influencing the distribution, population dynamics and impacts of invasive alien species.One Health: One Health is a collaborative, multisectoral approach that aspires to sustainably balance and optimize the health of people, animals, and ecosystems. By recognizing that the health of humans, animals, plants, and the wider environment are closely linked and interdependent, One Health aims to achieve optimal health and well-being outcomes.Invasive alien pathogens, pests, and weeds pose a significant challenge to the environment worldwide and their management results in huge global economic costs.,,,Nevertheless, despite fundamental similarities in the invasion process of pathogens, pests, and weeds, irrespective of whether they impact human, animal, or plant health, government policymakers and regulators, researchers, and industry generally take a siloed approach to biosecurity delimited by sectoral and taxonomic identities. Although there have been calls to better integrate invasive alien species threats under a One Health umbrella,,Box 2,Emerging biosecurity topics were identified and evaluated using a modified iterative Delphi method of expert consultation such as voting and anonymity, similar to procedures used in horizon scans of biological invasions.In response, the concept of One Biosecurity has been developed to foster an interdisciplinary approach to biosecurity policy and research, building on interconnections between human, animal, plant, and ecosystem health to prevent and mitigate the impacts of invasive alien species.,,Conventional surveillance of biosecurity incursions for invasive alien pathogens, pests, and weeds using traps or direct observation is labor intensive, time-consuming, costly, and rarely completely effective. Although across the human, animal, plant, and ecosystem health sectors there is growing interest in using the keen sense of smell possessed by dogs to detect chemical signatures associated with human pathogens,,Autonomous surveillance technologies, such as sensor networks and robotic systems, linked to intelligent algorithms, have the potential to prevent, detect and manage biosecurity threats. For example, sensing and robotic systems have been used to track flying foxes as disease vectors; detect fruit flies, identify weeds, and differentiate diseased from healthy plants.,,A case in point is the Internet of Things (IoT) that refers to the network of sensors that connect and exchange data with other devices and systems over the Internet or other communications networks. While the application of IoT is still in its infancy, it has considerable scope to improve the tracing and tracking of biosecurity risks. Passive radio frequency identification devices (RFID) store information on a microchip that, when combined with a suitable mobile receiver, permit out-of-line-of-sight tracing and have been used to track the movements of shipping containers, livestock and pets, horticultural plants and even hospital patients.,More recently, Bluetooth Low Energy (BLE) sensors have been developed with a battery life of up to 5 years, and these are able to send an electromagnetic signal at distances of over 200m. Because Bluetooth devices can communicate with each other, they can capture details of similar devices nearby and thus it is possible to track organisms, people or containers, allowing checks as to whether there has been contact with a biosecurity incursion or risk, enabling more comprehensive tracing.,,,,The rapidly evolving field of nucleic acid based environmental metabarcoding (eNA) has led to an unprecedented ability to identify potential biosecurity threats to human, animal, plant, and ecosystem health. There is a huge opportunity to leverage the rapid development of eNA diagnostic tools used during the SARS-CoV-2 pandemic to improve the detection of biosecurity threats in the animal, plant, and ecosystem health sectors. Rapid, sensitive, and cost-effective tools include loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) which have recently been implemented as portable devices that can be deployed in the field for “point of care” detection.,Despite differences in the gene targets that are used for taxonomic resolution, the application of eNA in human, animal, plant, and ecosystem health faces similar challenges including sampling and purifying nucleic acids from environmental samples, incomplete reference databases, and complex bioinformatics requirements that might better be resolved by an integrated approach across the different sectors.Furthermore, eNA has been recognized as a cost-effective tool to monitor evolution and has been used to track the changes of nucleotide diversity, derive variant-specific reproduction numbers, and geographically locate the emergence of novel mutation constellations in human pathogens.,,,,,The ability to track evolutionary change using high-throughput third generation nucleic acid sequencing, provides the capacity to sequence, analyze, and interpret data in near real-time and at relatively low cost, also offers an opportunity to determine the geographical origin of biosecurity incursions, and their subsequent spread post-border. Mitochondrial and genomic metabarcoding have been used to support phylogeographic analyses that describe the origin and global spread of a wide range of invasive alien pests and pathogens.,Box 3In 2018, the New Zealand government sponsored a survey to capture public attitudes toward biosecurity. The survey comprised a representative  sample of 1150 adults, including a specific sample of 150 Māori . The survey highlighted that most individuals do not see themselves or their family playing a substantial role in the biosecurity system and instead identify national and local government as the main players in the protection of the environment and economy from invasive alien pathogens, pests, and weeds.Governments are increasingly looking to enhance public participation in biosecurity since greater engagement of wider ranging actors can benefit invasive alien species surveillance and detection, and participation in eradication, control programs, and prevention activities.Thus, the characteristics of any biosecurity policy or response should identify the perceived merit of the system at risk whether the system is human society, a semi-natural landscape, an ecosystem, a farm, or even a single species. Assessments of merit should take into account the inherent worth of the system, the services  it provides to people, and/or the meaningfulness of relationships between people and the system at risk, such as cultural identities of indigenous people derived from their connection to the land.,,The inclusion of indigenous communities and local users of environments who have previously been excluded from biosecurity decision-making processes must, however, be meaningful, involving an equitable sharing of power, influence, and resources.,,,The direct effects of biosecurity incursions on human, animal, and plant health have often been quantified in economic terms,There is, thus, a need to understand the benefits and impacts of biosecurity actions on the wellbeing of people both directly and indirectly. Appreciating what is at risk from biosecurity incursions also requires attention to how people are impacted not only in terms of loss of livelihoods  but also the sense of alarm that biosecurity response operations evoke.The truly holistic approach in One Biosecurity requires thinking about how we can maintain a focus on wellbeing throughout the biosecurity continuum. For example, developing tools for the remediation of adverse effects of biosecurity actions can help ameliorate public resistance to control methods. A greater appreciation of how biosecurity intersects with wellbeing will help improve policies that plan to mitigate for adverse effects of interventions. It may also be key to curbing the dissemination of false and misleading information regarding biosecurity threats and interventions.,,The multitude of quantitative models developed during the SARS-CoV-2 pandemic,,Advances in high-performance computing and statistical inference techniques are also improving the utility of agent-based models and related complex systems-based methods for modeling dynamics of holistic socio-environmental systems.,,The accuracy of predictions from complex models and decision support tools depends on the availability and suitability of data to inform their many parameters. Artificial intelligence (AI) algorithms show promise to rapidly scan the large and complex data spread across multiple information sources  and extract key information. To date, AI has been used with large datasets to predict the effect of climate scenarios on vector-borne animal diseases,,The preceding section highlights four emerging issues where an integrated One Biosecurity approach that spans human, animal, plant, and ecosystem health will bring considerable benefits for global and national biosecurity systems . AlthougOne of the few silver linings of the SARS-CoV-2 pandemic was the global, transdisciplinary uniting of science and medicine to focus on a single global problem, sharing ideas, expertise, technology and information, from genomic studies, to tracking and tracing surveillance tools, spatiotemporal epidemiological models to forecast transmission trends, to the development of methods to limit SARS-CoV-2 spread and ultimately mitigate adverse impacts.Furthermore, responding to future biosecurity threats before they arise will require a change to the governance of multilateral institutions to have a stronger focus on providing equal ownership and leadership opportunities to all member countries.The SARS-CoV-2 pandemic also fueled digital transformation in health systems and demonstrated benefits of a more open data landscape.In addition, the SARS-CoV-2 pandemic produced an extensive genetic testing infrastructure, developed in most countries to collect genomic data directly from individuals  or indirectly in wastewater. There are concerns that this expensive technological infrastructure and supporting organizational backbones will be repurposed to conduct other forms of genetic testing to support wider goals of biosecurity.These complexities highlight the importance of transparency, accountability, and community engagement (data governance) to ensure data usage is appropriate. Additionally, it is essential to establish the right to collect and use data (data sovereignty) through a process that has widespread community acceptance . Consideration of the collation and use of data must include its potential to significantly impact on people’s lives , requiring data systems to be co-designed and co-managed, and assuring that indigenous people will contribute to decision-making when preparing for and responding to an incursion.Biosecurity is intrinsically an outcome focused activity, where biosecurity systems are frequently judged on their failures  more than their successes . As a result, the value proposition for governments to invest in biosecurity is increasingly challenged by other more visible problems such as rising food and water insecurity,Unfortunately, while national biosecurity systems were expected to be strengthened and modernized following the hard lessons from the SARS-CoV-2 pandemic, few countries have moved toward more technologically advanced surveillance, better data integration or fostered more resilient communities.


Sensors based on graphene materials have promising applications in the fields of biology, medicine and environment etc. A laser-scribed graphene provides a versatile, low-cost, and environmental friendly method for stress, bio, gas, temperature, humidity and multifunctional integrated sensors. Real time detection of biological, chemical and physical information is important for medical diagnosis and environmental monitoring. Hence, it is urgent to develop high performance sensors with various functions and even multifunctions to meet the requirements of information era. Graphene is a unique material with two dimensional structure which has been proven to be an excellent material candidate for mechanical stress and strain, biochemical substances, as well as gases, temperature, and humidity sensors as illustrated in Fig. 1, both CVD and EG requires high energy consumption and are expensive2, whereas rGO causes environmental pollution during preparation process3. Therefore, a method to produce graphene that avoids these drawbacks are of great interest. Laser direct writing (LDW) technique received great attention in recent years due to the advantages such as selective and localized reduction, precise and fast patterning, and the absence of masks and additional chemicals. The LDW technique was incorporated to irradiate carbon precursors and prepare graphene by in-situ scribing4. X. Liu and colleages from the University of Shanghai for Science and Technology and Queensland University of Technology presented a comprehensive review article entitled “Laser-Scribed Graphene for Sensors: Preparation, Modification, Applications and Future Prospects” on the existing laser-scribed graphene (LSG) for sensor fabrication in Light: Advanced Manufacturing5. In the review, a recent development of LSG in sensor applications was introduced and future prospects were provided.To date, graphene has been fabricated through conventional methods such as mechanical exfoliation (ME), chemical vapor deposition (CVD), epitaxial growth (EG) and reduction of graphene oxide (rGO). The ME has low efficiency6 and laser-induced graphene7. For the first method, the LSG was prepared from the reduced GO using femtosecond laser irradiation. For the second method, the LSG was prepared by laser irradiation of polymer precursors such as polyimide and phenolic resin and natural materials such as paper and wood. The surface morphologies and properties of the LSG can be modified by adjusting laser parameters, controlling atmospheric condition and doping8. The laser parameters include laser power, scanning speed, and pulse repetition rate. For instance, a higher scanning speed and pulse repetition rate, and moisture atmosphere improve the surface hydrophilicity of the LSG. Doping improves the range and sensitivity of the LSG-based sensors. For instance, the creation of conductive channels between dopants and LSG increases the number of microcracks and resistance, which improves the performance of the stress and strain sensors, respectively.The LSG could be prepared by two methods through the LDW: laser-reduced graphene oxide (GO)10. Biosensors converts biological or biochemical reactions into physicochemical signals for analysis. The LSG contains electrocatalytic activity, strong adsorption capacity and sensitive to the detection of ions and biological molecules such as glucose, dopamine, hydrogen peroxide, nucleic acid and insulin12. The detection for changes in environment such as gas, temperature and humidity were discussed. The large number of three-dimensional micro-nanoporous structure in the LSG provides more active sites and diffusion paths for gas detection such as ammonia and hydrogen14. Temperature sensors were developed to real-time monitor a change in body temperature and battery temperature16. The LSG has high wearability and fast response time for a change between graphene resistance and temperature in a negative exponential relationship. A combination of LSG and GO shows higher sensitivity to humidity change than other functional conductive materials because the porosity of the LSG improves the hydrophilic nature of the GO to absorb water molecules17. Apart from typical sensors, multifunctional integrated sensors combine the detection of two or more physical or biological signals to enable more comprehensive and accurate applications in various fields such as a uric acid and tyrosine detection in sweat integrated with biochemical, temperature and strain sensors18.Next, special attention was given to the application of LSG in various sensors in terms of strategies of designing sensors with high sensitivity, wide detection range, fast response time, and good repeatability. Stress sensor detects force signals by converting them into electrical signals. The LSG is mechanically strong, highly conductive and sensitive to various external applied stress, which changes the capacitance and resistance of the sensorsDespite of the numerous advantages of the sensors integrated with the LSG, there exists several challenges for further development. For the preparation of LSG, investigations of new precursors, increase productivity, adhesion ability to biochemical substances and reduces defects are some worthwhile endeavors. To improve the performance of the sensors, designing suitable LSG surface microstructures and doping for stress sensors, employing electrochemical tests to analyze biosensors and incorporating ultrafine laser lithography to maximize the specific surface area of the LSG structures are recommended. We also expect that the LDW technique can be expanded to other materials and sensors with higher sensitivity, wider detection range, faster response time, and much better repeatability can be developed based on the deep understanding of laser matter interaction mechanism and development of precise control technique of laser induced structures.


Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions. Traditional knowledge of bioresources for microbial, plant and animal metabolites, as well as their processing and application, has been contributing tremendously to quality of life for thousands of years. Small molecular weight natural products have accompanied humankind and supported their quality of life in highly relevant areas, such as nutrition, diagnostics and therapy of diseases, dyes, cosmetics, and well-being, with an ever-increasing knowledge base. Numerous achievements have been made in terms of the isolation and purification of metabolites from natural bioresources and the elucidation of their molecular structures, demonstrating their large structural diversity. Renewed interest in the small molecule domain of biology  and the The concept of the metabolome , coined The development of analytical methodologies for identifying biologically active metabolites and the interactions between metabolites and proteins and other biomolecules ,25,26 ofThe use of isotopes for labelling small molecules continues to be essential for analytical methodologies, from measuring metabolite concentrations in biological matrices and determining metabolite fluxes in biological organisms to the discovery of metabolic pathways ,28,29. BThe purpose of this work is to provide an overview of design and engineering approaches for biocatalytic systems in metabolite production and their application in manufacturing processes. The significance of biocatalytic systems for metabolite production is connected with the general strategic advantages of using biocatalysis in synthesis, such as their high selectivity and shortened synthetic routes .Biocatalytic system design and engineering towards the synthesis of metabolites starts with route selection and includes the preparation of suitable biocatalysts and raw materials, reaction engineering, process integration, intensification, and scaling of the selected metabolite manufacturing processes . The maiHaving synthetic access to a metabolite or metabolite-like compound, or even more convenient, having an already available pure product, has been and continues to be highly important and relevant ,48 for aIn view of millions of protein sequences, genomic enzymology tools are highly valuable for generating clues and hypotheses to guide experiments towards the correct assignment of enzyme and metabolic functions, as the functions of a significant fraction of protein sequences are unknown, uncertain, or even misassigned . The finThere are also disadvantages to overcome for biocatalytic systems when substrate and product concentrations are limited by solubility in aqueous media or enzyme inhibition. In these cases, a reaction type corresponding to a well-established key functionalization reaction in organic chemistry requires intense elaboration of a suitable biocatalytic systems for a selected substrate to product conversion due to the narrow substrate scope of the biocatalyst. Stability issues, for biocatalysts as well as for substrates and products, need to be checked, and in case of fragile biologically active metabolites, suitable operating windows for the reaction and workup must be selected. Improving energy and resource efficiency in manufacturing metabolites, such as reducing energy use and avoiding protection–deprotection schemes in lengthy chemical synthesis from fossil-based raw materials or minimizing biological waste in low-yield extractive procedures from biobased raw materials, is essential in building more resilient and sustainable manufacturing routes to metabolites. A resilient manufacturing route to a metabolite means a route with a robust and stable production process with the ability to effectively cope with changing boundary conditions, respond, and maintain reliable manufacturing. The molecular transformations catalyzed by enzymes and metabolic pathways in nature, as well as the great advances of biocatalysis, provide a blueprint and rich sources of knowledge for designing and engineering biocatalytic systems for the synthesis of metabolites.Biocatalytic systems using whole cells have been developed into numerous fermentation processes as well as biotransformation processes at large industrial scales for the production of metabolites ,51,52,53While the step economy of a biosynthetic pathway is already considered in the design phase, possible improvements such as complexity reduction, intermediate purification steps, and the number of separate reactors needed are also taken into account by the degree of process integration involved in biocatalytic metabolite synthesis. Depending on how many reaction steps are needed for transforming the starting materials to the metabolites, one-step to multistep enzymatic reactions are developed, if possible in one pot. If all the enzymatic reactions of a biosynthetic pathway can be performed well, enzymatic total synthesis can be achieved .The milestone discoveries that small molecules of life could not only be isolated from nature, but could also be synthesized in the laboratory from inorganic chemical precursors, started the new era of synthetic organic chemistry . The synallo-cholanate synthesized previously was converted via cholestan-3-ol, cholestane-3-one, and 4-cholesten-3-one into cholesterol  cycloaddition of the same substrates with high enantioselectivity and broadening the scope of the dienes and dienophiles accepted as substrates [The advances in the characterization and engineering of enzymes catalyzing routine reactions in organic chemistry laboratories, such as Diels–Alder reactions, Claisen, and Cope rearrangements are very promising for translating biocatalytic complexity-generating reactions into routine operations . The devbstrates .cis-(+)-12-oxophytodienoic acid [Combining biocatalytic and chemical reaction steps in chemoenzymatic syntheses offers new opportunities for overcoming challenges and diversifying metabolite structures. Advances have been achieved in the synthesis of diversified compounds of the plant metabolite oic acid , a varieoic acid ,222.The growing number of metabolite structures identified in biological organisms and the renewed interest in the elucidation of the fundamental roles and useful properties of metabolites have also increased the necessity of developing analytical and synthetic methods and tools for a great diversity of metabolites. The identification of natural metabolic pathways has provided great starting points for the development of straightforward biocatalytic synthesis routes inspired by nature. Biocatalytic systems offer particular benefits not only for producing naturally occurring metabolites but also for stable isotope-labelled metabolites, drug metabolites, and non-natural metabolite-like compounds. The molecular economy and reduced complexity of highly selective and protecting group-free biocatalytic reactions have enabled resource-efficient and robust production procedures for these metabolite classes. When the extraction yields of naturally occurring metabolites from biological resources are rather low and quite variable, or if challenging and lengthy chemical routes using protection–deprotection schemes require significant purification steps, biocatalytic production procedures using recombinant biocatalysts are a preferred choice. The strategic advantages of biocatalytic systems enable the fast generation of molecular complexity through shorter synthetic routes in a straightforward way, as demonstrated by the development of the biocatalytic synthesis of the antiviral compound islatravir. The cascade of the three biocatalytic reaction steps leading to a single stereoisomer of islatravir from achiral 2-ethinylglycerol see  shortensEngineering enzymes towards desired activities and selectivities under certain reaction conditions and the combination of the necessary enzymes in a cascade reaction enable us to overcome unfavorable thermodynamic equilibria, enzyme inhibition, and the isolation of intermediates. Minimizing the time for the development, manufacturing, and supply of a compound in high demand and urgently needed is always very critical, but it has been even more crucial during the COVID-19 pandemic. The fast development of a short scalable biocatalytic route to the antiviral agent Molnupiravir using engineered ribosyl-1-kinase and uridine phosphorylase as key enzymes was impressive . The advExcellent opportunities appear for exploring uncharted territory regarding novel metabolites and their biological functions , naturalWith the rising gap between the number of known metabolites and the number of metabolites whose syntheses have been reported, there are more than enough concrete problems to be solved, such as the synthesis of important metabolites with high biological activity and inherent instability, or biologically active metabolites which are synthesized by bifunctional enzymes carrying synthetic and degrading functions within the same protein. Rapid advances in enzyme engineering and computational methodologies have been outlined and appear promising on the road towards the design of robust enzymes with desired enzyme functional properties . Many ro