Expression of a unique M. tuberculosis DNA MTase Rv1509 in M. smegmatis alters the gene expression pattern and enhances virulence.

Mycobacterium tuberculosis (M. tb) genome encompasses 4,173 genes, about a quarter of which remain uncharacterized and hypothetical. Considering the current limitations associated with the diagnosis and treatment of tuberculosis, it is imperative to comprehend the pathomechanism of the disease and host-pathogen interactions to identify new drug targets for intervention strategies. Using in-silico comparative genome analysis, we identified one of the M. tb genes, Rv1509, as a signature protein exclusively present in M. tb. To explore the role of Rv1509, a likely methyl transferase, we constructed a knock-in Mycobacterium smegmatis (M. smegmatis) constitutively expressing Rv1509 (Ms_Rv1509). The Ms_Rv1509 led to differential expression of many transcriptional regulator genes as assessed by RNA-seq analysis. Further, in-vitro and in-vivo studies demonstrated an enhanced survival of Ms_Rv1509 inside the host macrophages. Ms_Rv1509 also promoted phagolysosomal escape inside macrophages to boost bacterial replication and dissemination. In-vivo infection studies revealed that Ms_Rv1509 survives better than BCG and causes pathological manifestations in the pancreas after intraperitoneal infection. Long-time survival of Ms_Rv1509 resulted in lymphocyte migration, increased T regulatory cells, giant cell formation, and likely granuloma formation in the pancreas, pointing toward the role of Rv1509 in M. tb pathogenesis.

Activation of the lysosomal damage response and selective autophagy: the coordinated actions of galectins, TRIM proteins, and CGAS-STING1 in providing immunity against Mycobacterium tuberculosis.

Autophagy is a crucial immune defense mechanism that controls the survival and pathogenesis of M. tb by maintaining cell physiology during stress and pathogen attack. The E3-Ub ligases (PRKN, SMURF1, and NEDD4) and autophagy receptors (SQSTM1, TAX1BP1, CALCOCO2, OPTN, and NBR1) play key roles in this process. Galectins (LGALSs), which bind to sugars and are involved in identifying damaged cell membranes caused by intracellular pathogens such as M. tb, are essential. These include LGALS3, LGALS8, and LGALS9, which respond to endomembrane damage and regulate endomembrane damage caused by toxic chemicals, protein aggregates, and intracellular pathogens, including M. tb. They also activate selective autophagy and de novo endolysosome biogenesis. LGALS3, LGALS9, and LGALS8 interact with various components to activate autophagy and repair damage, while CGAS-STING1 plays a critical role in providing immunity against M. tb by activating selective autophagy and producing type I IFNs with antimycobacterial functions. STING1 activates cGAMP-dependent autophagy which provides immunity against various pathogens. Additionally, cytoplasmic surveillance pathways activated by ds-DNA, such as inflammasomes mediated by NLRP3 and AIM2 complexes, control M. tb. Modulation of E3-Ub ligases with small regulatory molecules of LGALSs and TRIM proteins could be a novel host-based therapeutic approach for controlling TB.

Burnout increased among university students during the COVID-19 pandemic: a systematic review and meta-analysis.

Generally, university students are at risk of burnout. This likely was exacerbated during the COVID-19 pandemic. We aimed to investigate burnout prevalence among university students during the COVID-19 pandemic and examine its distribution across countries, sexes, fields of study, and time-period. PubMed, EMBASE, PsycINFO, World Health Organization's Global COVID-19 database, Scopus, Epistemonikos, ERIC and Google Scholar were searched (protocol: https://doi.org/10.17605/OSF.IO/BYRXW ). Studies were independently screened and extracted. Random-effects meta-analysis was performed. Study quality was appraised, and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. We identified 44 primary studies comprising 26,500 students. Global prevalence rates were 56.3% for high emotional exhaustion (EE), 55.3% for high cynicism (CY) and 41.8% for low personal accomplishment (PA). Prevalence of EE, CY, and PA domains varied significantly across fields of study, countries and WHO and World Bank regions, but not sex. All studies demonstrated good internal validity, although substantial heterogeneity existed between studies. The certainty of evidence was rated as moderate. Considering its potentially severe consequences, burnout is a significant public health concern. The development and implementation of evidence-based localized interventions at organizational and individual levels are necessary to mitigate burnout.

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.

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200 ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.

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.

24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology.

Little is known about links of circadian rhythm alterations with neuropsychiatric symptoms and cognition in memory impaired older adults. Associations of actigraphic rest/activity rhythms (RAR) with depressive symptoms and cognition are examined using function-on-scalar regression (FOSR). Forty-four older adults with memory impairment (mean: 76.84 ± 8.15 years; 40.9% female) completed 6.37 ± 0.93 days of actigraphy, the Beck depression inventory-II (BDI-II), mini-mental state examination (MMSE) and consortium to establish a registry for Alzheimer's disease (CERAD) delayed word recall. FOSR models with BDI-II, MMSE, or CERAD as individual predictors adjusted for demographics (Models A1-A3) and all three predictors and demographics (Model B). In Model B, higher BDI-II scores are associated with greater activity from 12:00-11:50 a.m., 2:10-5:50 p.m., 8:40-9:40 p.m., 11:20-12:00 a.m., higher CERAD scores with greater activity from 9:20-10:00 p.m., and higher MMSE scores with greater activity from 5:50-10:50 a.m. and 12:40-5:00 p.m. Greater depressive symptomatology is associated with greater activity in midafternoon, evening, and overnight into midday; better delayed recall with greater late evening activity; and higher global cognitive performance with greater morning and afternoon activity (Model B). Time-of-day specific RAR alterations may affect mood and cognitive performance in this population.

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.

Regulation of autophagy by SARS-CoV-2: The multifunctional contributions of ORF3a.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) regulates autophagic flux by blocking the fusion of autophagosomes with lysosomes, causing the accumulation of membranous vesicles for replication. Multiple SARS-CoV-2 proteins regulate autophagy with significant roles attributed to ORF3a. Mechanistically, open reading frame 3a (ORF3a) forms a complex with UV radiation resistance associated, regulating the functions of the PIK3C3-1 and PIK3C3-2 lipid kinase complexes, thereby modulating autophagosome biogenesis. ORF3a sequesters VPS39 onto the late endosome/lysosome, inhibiting assembly of the soluble NSF attachement protein REceptor (SNARE) complex and preventing autolysosome formation. ORF3a promotes the interaction between BECN1 and HMGB1, inducing the assembly of PIK3CA kinases into the ER (endoplasmic reticulum) and activating reticulophagy, proinflammatory responses, and ER stress. ORF3a recruits BORCS6 and ARL8B to lysosomes, initiating the anterograde transport of the virus to the plasma membrane. ORF3a also activates the SNARE complex (STX4-SNAP23-VAMP7), inducing fusion of lysosomes with the plasma membrane for viral egress. These mechanistic details can provide multiple targets for inhibiting SARS-CoV-2 by developing host- or host-pathogen interface-based therapeutics.

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.

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.

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.

Mycobacterium tuberculosis protein MoxR1 enhances virulence by inhibiting host cell death pathways and disrupting cellular bioenergetics.

Mycobacterium tuberculosis (M. tb) utilizes the multifunctionality of its protein factors to deceive the host. The unabated global incidence and prevalence of tuberculosis (TB) and the emergence of multidrug-resistant strains warrant the discovery of novel drug targets that can be exploited to manage TB. This study reports the role of M. tb AAA+ family protein MoxR1 in regulating host-pathogen interaction and immune system functions. We report that MoxR1 binds to TLR4 in macrophage cells and further reveal how this signal the release of proinflammatory cytokines. We show that MoxR1 activates the PI3K-AKT-MTOR signalling cascade by inhibiting the autophagy-regulating kinase ULK1 by potentiating its phosphorylation at serine 757, leading to its suppression. Using autophagy-activating and repressing agents such as rapamycin and bafilomycin A1 suggested that MoxR1 inhibits autophagy flux by inhibiting autophagy initiation. MoxR1 also inhibits apoptosis by suppressing the expression of MAPK JNK1/2 and cFOS, which play critical roles in apoptosis induction. Intriguingly, MoxR1 also induced robust disruption of cellular bioenergetics by metabolic reprogramming to rewire the citric acid cycle intermediates, as evidenced by the lower levels of citric acid and electron transport chain enzymes (ETC) to dampen host defence. These results point to a multifunctional role of M. tb MoxR1 in dampening host defences by inhibiting autophagy, apoptosis, and inducing metabolic reprogramming. These mechanistic insights can be utilized to devise strategies to combat TB and better understand survival tactics by intracellular pathogens.

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

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

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

BACKGROUND: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. OBJECTIVE: This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. METHODS: We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. RESULTS: Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. CONCLUSIONS: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

The exploitation of host autophagy and ubiquitin machinery by Mycobacterium tuberculosis in shaping immune responses and host defense during infection.

Intracellular pathogens have evolved various efficient molecular armaments to subvert innate defenses. Cellular ubiquitination, a normal physiological process to maintain homeostasis, is emerging one such exploited mechanism. Ubiquitin (Ub), a small protein modifier, is conjugated to diverse protein substrates to regulate many functions. Structurally diverse linkages of poly-Ub to target proteins allow enormous functional diversity with specificity being governed by evolutionarily conserved enzymes (E3-Ub ligases). The Ub-binding domain (UBD) and LC3-interacting region (LIR) are critical features of macroautophagy/autophagy receptors that recognize Ub-conjugated on protein substrates. Emerging evidence suggests that E3-Ub ligases unexpectedly protect against intracellular pathogens by tagging poly-Ub on their surfaces and targeting them to phagophores. Two E3-Ub ligases, PRKN and SMURF1, provide immunity against Mycobacterium tuberculosis (M. tb). Both enzymes conjugate K63 and K48-linked poly-Ub to M. tb for successful delivery to phagophores. Intriguingly, M. tb exploits virulence factors to effectively dampen host-directed autophagy utilizing diverse mechanisms. Autophagy receptors contain LIR-motifs that interact with conserved Atg8-family proteins to modulate phagophore biogenesis and fusion to the lysosome. Intracellular pathogens have evolved a vast repertoire of virulence effectors to subdue host-immunity via hijacking the host ubiquitination process. This review highlights the xenophagy-mediated clearance of M. tb involving host E3-Ub ligases and counter-strategy of autophagy inhibition by M. tb using virulence factors. The role of Ub-binding receptors and their mode of autophagy regulation is also explained. We also discuss the co-opting and utilization of the host Ub system by M. tb for its survival and virulence.Abbreviations: APC: anaphase promoting complex/cyclosome; ATG5: autophagy related 5; BCG: bacille Calmette-Guerin; C2: Ca2+-binding motif; CALCOCO2: calcium binding and coiled-coil domain 2; CUE: coupling of ubiquitin conjugation to ER degradation domains; DUB: deubiquitinating enzyme; GABARAP: GABA type A receptor-associated protein; HECT: homologous to the E6-AP carboxyl terminus; IBR: in-between-ring fingers; IFN: interferon; IL1B: interleukin 1 beta; KEAP1: kelch like ECH associated protein 1; LAMP1: lysosomal associated membrane protein 1; LGALS: galectin; LIR: LC3-interacting region; MAPK11/p38: mitogen-activated protein kinase 11; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MAP3K7/TAK1: mitogen-activated protein kinase kinase kinase 7; MAPK8/JNK: mitogen-activated protein kinase 8; MHC-II: major histocompatibility complex-II; MTOR: mechanistic target of rapamycin kinase; NBR1: NBR1 autophagy cargo receptor; NFKB1/p50: nuclear factor kappa B subunit 1; OPTN: optineurin; PB1: phox and bem 1; PE/PPE: proline-glutamic acid/proline-proline-glutamic acid; PknG: serine/threonine-protein kinase PknG; PRKN: parkin RBR E3 ubiquitin protein ligase; RBR: RING-in between RING; RING: really interesting new gene; RNF166: RING finger protein 166; ROS: reactive oxygen species; SMURF1: SMAD specific E3 ubiquitin protein ligase 1; SQSTM1: sequestosome 1; STING1: stimulator of interferon response cGAMP interactor 1; TAX1BP1: Tax1 binding protein 1; TBK1: TANK binding kinase 1; TNF: tumor necrosis factor; TRAF6: TNF receptor associated factor 6; Ub: ubiquitin; UBA: ubiquitin-associated; UBAN: ubiquitin-binding domain in ABIN proteins and NEMO; UBD: ubiquitin-binding domain; UBL: ubiquitin-like; ULK1: unc-51 like autophagy activating kinase 1.