Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Sequencing analysis and efficient biodiesel production by lipase from Pseudomonas aeruginosa.

BACKGROUND: Recently, lipase processing for biodiesel production has shown a global increase as it is considered a potential alternative clean-fuel source. The current study's objective is to investigate of lipolytic activity of lipase produced from different strains of Pseudomonas aeruginosa (P. aeruginosa) in biodiesel production using edible plant oils. The goal is to develop an efficient and cost-effective method for producing inexpensive and environmentally friendly biodiesel. METHODS AND RESULTS: Four P. aeruginosa isolates were obtained from different environmental sources (soil), phenotypically identified, and it was confirmed by the PCR detection of the 16SrRNA gene. The isolated P. aeruginosa strains were screened for lipase production, and the recovered lipase was purified. Besides, the lipase (lip) gene was detected by PCR, and the purified PCR products were sequenced and analyzed. The production of biofuel was conducted using gas chromatography among tested oils. It was found that castor oil was the best one that enhances lipase production in-vitro.

Formulation of novel bioactive gelatin inspired by cinnamaldehyde for combating multi-drug resistant bacteria: Characterization, molecular docking, pharmacokinetic analyses, and in vitro assessments.

This study set out to formulate antibacterial and antioxidant gelatin boosted by cinnamaldehyde for combating multi-drug resistant bacteria previously obtained from chronic wounds. Towards this end, gelatin amine groups were conjugated with carbonyl groups of cinnamaldehyde, producing cinnamyl-gelatin Schiff bases. The physicochemical attributes of cinnamyl-gelatin Schiff bases were probed concerning alterations in chemical structures and microstructures compared to native gelatin. Besides, cinnamyl-gelatin Schiff bases exhibited higher thermal stability than gelatin, with a diminishing in solubility due to increases in hydrophobicity features. Interestingly, cinnamyl-gelatin derivatives exerted antibacterial activities versus multi-drug resistant Gram-negative and Gram-positive bacteria, showing maximum growth inhibition at the highest concentration of cinnamaldehyde incorporated into gelatin. The scavenging activities of gelatin against DPPH and ABTS•+ were promoted in cinnamyl-gelatin derivatives from 11.93 ± 0.6 % to 49.9 ± 2.5 % and 12.54 ± 0.63 % to 49.9 ± 3.12 %, respectively. Remarkably, cinnamyl-gelatin derivatives induced the proliferation of fibroblast cells, implying their prospective applications in tissue engineering. Molecular docking and pharmacokinetic investigations disclosed the potential antibacterial mechanisms of cinnamyl-gelatin derivatives alongside their biopharmaceutical applications. Altogether, these findings suggest that cinnamyl-gelatin derivatives could be utilized to tailor antibacterial-free antibiotics and antioxidant wound dressings against virulent bacteria to promote chronic wound recovery.

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.

BACKGROUND: Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial. Wearable artificial intelligence (AI) has emerged as a valuable tool for this purpose. It offers an objective, noninvasive, nonobtrusive, automated approach to continuously monitor biomarkers in real time, thereby addressing the limitations of traditional approaches such as self-reported questionnaires. OBJECTIVE: This systematic review and meta-analysis aim to assess the performance of wearable AI in detecting and predicting stress among students. METHODS: Search sources in this review included 7 electronic databases (MEDLINE, Embase, PsycINFO, ACM Digital Library, Scopus, IEEE Xplore, and Google Scholar). We also checked the reference lists of the included studies and checked studies that cited the included studies. The search was conducted on June 12, 2023. This review included research articles centered on the creation or application of AI algorithms for the detection or prediction of stress among students using data from wearable devices. In total, 2 independent reviewers performed study selection, data extraction, and risk-of-bias assessment. The Quality Assessment of Diagnostic Accuracy Studies-Revised tool was adapted and used to examine the risk of bias in the included studies. Evidence synthesis was conducted using narrative and statistical techniques. RESULTS: This review included 5.8% (19/327) of the studies retrieved from the search sources. A meta-analysis of 37 accuracy estimates derived from 32% (6/19) of the studies revealed a pooled mean accuracy of 0.856 (95% CI 0.70-0.93). Subgroup analyses demonstrated that the accuracy of wearable AI was moderated by the number of stress classes (P=.02), type of wearable device (P=.049), location of the wearable device (P=.02), data set size (P=.009), and ground truth (P=.001). The average estimates of sensitivity, specificity, and F1-score were 0.755 (SD 0.181), 0.744 (SD 0.147), and 0.759 (SD 0.139), respectively. CONCLUSIONS: Wearable AI shows promise in detecting student stress but currently has suboptimal performance. The results of the subgroup analyses should be carefully interpreted given that many of these findings may be due to other confounding factors rather than the underlying grouping characteristics. Thus, wearable AI should be used alongside other assessments (eg, clinical questionnaires) until further evidence is available. Future research should explore the ability of wearable AI to differentiate types of stress, distinguish stress from other mental health issues, predict future occurrences of stress, consider factors such as the placement of the wearable device and the methods used to assess the ground truth, and report detailed results to facilitate the conduct of meta-analyses. TRIAL REGISTRATION: PROSPERO CRD42023435051; http://tinyurl.com/3fzb5rnp.

TLE1 Expression in NUT Carcinoma: A Case Report Highlighting a Potential Diagnostic Pitfall for the Pathologist.

NUT carcinoma is a rare, aggressive malignancy defined as a carcinoma with a chromosomal rearrangement affecting the nuclear protein in testis (NUTM1) gene. This small round blue cell tumor classically exhibits focal abrupt keratinization and immunohistochemical positivity for keratin and squamous markers. However, keratinization is not always present and reports of positivity for other markers that may obscure the diagnosis are increasing. It is also noteworthy that gene fusions involving NUTM1 are not restricted to NUT carcinoma. Herein, we report a NUT carcinoma arising in the mediastinum of a male patient in his 40 s with morphological and immunohistochemical overlap with Ewing family sarcoma and poorly differentiated synovial sarcoma given a round cell morphology, diffuse strong immunoreactivity for CD99, and patchy strong immunoreactivity for TLE1. Squamous differentiation by morphology and p40 expression were notably absent in this case. Classification as NUT carcinoma was ultimately possible when the morphological and immunohistochemical findings were considered in the context of a BRD4::NUTM1 gene fusion identified by next-generation sequencing. While the patient initially responded to palliative radiotherapy, he died approximately one month later. To our knowledge, this is the first report of TLE1 immunoreactivity in NUT carcinoma. This case highlights a potential diagnostic pitfall and emphasizes the need for molecular confirmation in equivocal situations.

The secrets of environmental Pseudomonas aeruginosa in slaughterhouses: Antibiogram profile, virulence, and antibiotic resistance genes.

Environmental pollution is a serious problem that can cause sicknesses, fatality, and biological contaminants such as bacteria, which can trigger allergic reactions and infectious illnesses. There is also evidence that environmental pollutants can have an impact on the gut microbiome and contribute to the development of various mental health and metabolic disorders. This study aimed to study the antibiotic resistance and virulence potential of environmental Pseudomonas aeruginosa (P. aeruginosa) isolates in slaughterhouses. A total of 100 samples were collected from different slaughterhouse tools. The samples were identified by cultural and biochemical tests and confirmed by the VITEK 2 system. P. aeruginosa isolates were further confirmed by CHROMagar™ Pseudomonas and genetically by rpsL gene analysis. Molecular screening of virulence genes (fimH, papC, lasB, rhlI, lasI, csgA, toxA, and hly) and antibiotic resistance genes (blaCTX-M, blaAmpC, blaSHV, blaNDM, IMP-1, aac(6')-Ib-, ant(4')IIb, mexY, TEM, tetA, and qnrB) by PCR and testing the antibiotic sensitivity, biofilm formation, and production of pigments, and hemolysin were carried out in all isolated strains. A total of 62 isolates were identified as P. aeruginosa. All P. aeruginosa isolates were multidrug-resistant and most of them have multiple resistant genes. blaCTX-M gene was detected in all strains; 23 (37.1%) strains have the ability for biofilm formation, 33 strains had virulence genes, and 26 isolates from them have more than one virulence genes. There should be probably 60 (96.8%) P. aeruginosa strains that produce pyocyanin pigment. Slaughterhouse tools are sources for multidrug-resistant and virulent pathogenic microorganisms which are a serious health problem. Low-hygienic slaughterhouses could be a reservoir for resistance and virulence genes which could then be transferred to other pathogens.

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

BACKGROUND: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. OBJECTIVE: This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. METHODS: Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. RESULTS: Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. CONCLUSIONS: Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. TRIAL REGISTRATION: PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.

The performance of serious games for enhancing attention in cognitively impaired older adults.

Attention, which is the process of noticing the surrounding environment and processing information, is one of the cognitive functions that deteriorate gradually as people grow older. Games that are used for other than entertainment, such as improving attention, are often referred to as serious games. This study examined the effectiveness of serious games on attention among elderly individuals suffering from cognitive impairment. A systematic review and meta-analyses of randomized controlled trials were carried out. A total of 10 trials ultimately met all eligibility criteria of the 559 records retrieved. The synthesis of very low-quality evidence from three trials, as analyzed in a meta-study, indicated that serious games outperform no/passive interventions in enhancing attention in cognitively impaired older adults (P < 0.001). Additionally, findings from two other studies demonstrated that serious games are more effective than traditional cognitive training in boosting attention among cognitively impaired older adults. One study also concluded that serious games are better than traditional exercises in enhancing attention. Serious games can enhance attention in cognitively impaired older adults. However, given the low quality of the evidence, the limited number of participants in most studies, the absence of some comparative studies, and the dearth of studies included in the meta-analyses, the results remain inconclusive. Thus, until the aforementioned limitations are rectified in future research, serious games should serve as a supplement, rather than a replacement, to current interventions.

Estimating Blood Glucose Levels Using Machine Learning Models with Non-Invasive Wearable Device Data.

In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. Data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches.

Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children. The objective of this study is to examine the impact of intermittent fasting during Ramadan on stress levels in school children as measured using wearable artificial intelligence (AI). Twenty-nine school children (aged 13-17 years and 12M / 17F ratio) were given Fitbit devices and their stress, activity and sleep patterns analyzed 2 weeks before, 4 weeks during Ramadan fasting and 2 weeks after. This study revealed no statistically significant difference on stress scores during fasting, despite changes in stress levels being observed for 12 of the participants. Our study may imply intermittent fasting during Ramadan poses no direct risks in terms of stress, suggesting rather it may be linked to dietary habits, furthermore as stress score calculations are based on heart rate variability, this study implies fasting does not interfere the cardiac autonomic nervous system.

Performance of Artificial Intelligence in Predicting Future Depression Levels.

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively identifying or predicting depression. In this work, we aim to examine the performance of simple linear and non-linear models in the prediction of depression levels. We compared eight linear and non-linear models (Ridge, ElasticNet, Lasso, Random Forest, Gradient boosting, Decision trees, Support vector machines, and Multilayer perceptron) for the task of predicting depression scores over a period using physiological features, motor activity data, and MADRAS scores. For the experimental evaluation, we used the Depresjon dataset which contains the motor activity data of depressed and non-depressed participants. According to our findings, simple linear and non-linear models may effectively estimate depression scores for depressed people without the need for complex models. This opens the door for the development of more effective and impartial techniques for identifying depression and treating/preventing it using commonly used, widely accessible wearable technology.

Anti-capsular activity of CuO nanoparticles against Acinetobacter baumannii produce efflux pump.

Copper oxide nanoparticles are modern kinds of antimicrobials, which may get a lot of interest in the clinical application. This study aimed to detect the anti-capsular activity of CuO nanoparticles against Acinetobacter baumannii produce efflux pump. Thirty-four different clinical A. baumannii isolates were collected and identified by the phenotypic and genetic methods by the recA gene as housekeeping. Antibiotic sensitivity and biofilm-forming ability, capsular formation were carried out. The effect of CuO nanoparticles on capsular isolates was detected, the synergistic effects of a combination CuO nanoparticles and gentamicin against A. baumannii were determined by micro broth checkerboard method, and the effect of CuO nanoparticles on the expression of ptk, espA and mexX genes was analyzed. Results demonstrated that CuO nanoparticles with gentamicin revealed a synergistic effect. Gene expression results show reducing the expression of these capsular genes by CuO nanoparticles is major conduct over reducing A. baumannii capsular action. Furthermore, results proved that there was a relationship between the capsule-forming ability and the absence of biofilm-forming ability. As bacterial isolates which were negative biofilm formation were positive in capsule formation and vice versa. In conclusion, CuO nanoparticles have the potential to be used as an anti-capsular agent against A. baumannii, and their combination with gentamicin can enhance their antimicrobial effect. The study also suggests that the absence of biofilm formation may be associated with the presence of capsule formation in A. baumannii. These findings provide a basis for further research on the use of CuO nanoparticles as a novel antimicrobial agent against A. baumannii and other bacterial pathogens, also to investigate the potential of CuO nanoparticles to inhibit the production of efflux pumps in A. baumannii, which are a major mechanism of antibiotic resistance.

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)-driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

Binary CuO\CoO nanoparticles inhibit biofilm formation and reduce the expression of papC and fimH genes in multidrug-resistant Klebsiella oxytoca.

BACKGROUND AND AIM: Binary copper-cobalt oxide nanoparticles (CuO\CoO NPs) are modern kinds of antimicrobials, which may get a lot of interest in clinical application. This study aimed to detect the effect of the binary CuO\CoO NPs on the expression of papC and fimH genes in multidrug-resistant (MDR) isolates of Klebsiella oxytoca to reduce medication time and improve outcomes. METHODS: Ten isolates of K. oxytoca were collected and identified by different conventional tests besides PCR. Antibiotic sensitivity and biofilm-forming ability were carried out. The harboring of papC and fimH genes was also detected. The effect of binary CuO\CoO nanoparticles on the expression of papC and fimH genes was investigated. RESULTS: Bacterial resistance against cefotaxime and gentamicin was the highest (100%), while the lowest percentage of resistance was to amikacin (30%). Nine of the ten bacterial isolates had the ability to form a biofilm with different capacities. MIC for binary CuO\CoO NPs was 2.5 µg/mL. Gene expression of papC and fimH was 8.5- and 9-fold lower using the NPs. CONCLUSION: Binary CuO\CoO NPs have a potential therapeutic effect against infections triggered by MDR K. oxytoca strains due to the NPs-related downregulation ability on the virulence genes of K. oxytoca.

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.

Serious Games for Learning Among Older Adults With Cognitive Impairment: Systematic Review and Meta-analysis.

BACKGROUND: Learning disabilities are among the major cognitive impairments caused by aging. Among the interventions used to improve learning among older adults are serious games, which are participative electronic games designed for purposes other than entertainment. Although some systematic reviews have examined the effectiveness of serious games on learning, they are undermined by some limitations, such as focusing on older adults without cognitive impairments, focusing on particular types of serious games, and not considering the comparator type in the analysis. OBJECTIVE: This review aimed to evaluate the effectiveness of serious games on verbal and nonverbal learning among older adults with cognitive impairment. METHODS: Eight electronic databases were searched to retrieve studies relevant to this systematic review and meta-analysis. Furthermore, we went through the studies that cited the included studies and screened the reference lists of the included studies and relevant reviews. Two reviewers independently checked the eligibility of the identified studies, extracted data from the included studies, and appraised their risk of bias and the quality of the evidence. The results of the included studies were summarized using a narrative synthesis or meta-analysis, as appropriate. RESULTS: Of the 559 citations retrieved, 11 (2%) randomized controlled trials (RCTs) ultimately met all eligibility criteria for this review. A meta-analysis of 45% (5/11) of the RCTs revealed that serious games are effective in improving verbal learning among older adults with cognitive impairment in comparison with no or sham interventions (P=.04), and serious games do not have a different effect on verbal learning between patients with mild cognitive impairment and those with Alzheimer disease (P=.89). A meta-analysis of 18% (2/11) of the RCTs revealed that serious games are as effective as conventional exercises in promoting verbal learning (P=.98). We also found that serious games outperformed no or sham interventions (4/11, 36%; P=.03) and conventional cognitive training (2/11, 18%; P<.001) in enhancing nonverbal learning. CONCLUSIONS: Serious games have the potential to enhance verbal and nonverbal learning among older adults with cognitive impairment. However, our findings remain inconclusive because of the low quality of evidence, the small sample size in most of the meta-analyzed studies (6/8, 75%), and the paucity of studies included in the meta-analyses. Thus, until further convincing proof of their effectiveness is offered, serious games should be used to supplement current interventions for verbal and nonverbal learning rather than replace them entirely. Further studies are needed to compare serious games with conventional cognitive training and conventional exercises, as well as different types of serious games, different platforms, different intervention periods, and different follow-up periods. TRIAL REGISTRATION: PROSPERO CRD42022348849; https://tinyurl.com/y6yewwfa.

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.

BACKGROUND: In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction. OBJECTIVE: The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels. METHODS: We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices. CONCLUSIONS: This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future. TRIAL REGISTRATION: PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc.

Wearable devices for anxiety & depression: A scoping review.

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Correction: Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

[This corrects the article DOI: 10.2196/42672.].

Chatbot features for anxiety and depression: A scoping review.

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap. Their role in mental health care is expected to increase.