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.

Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.

BACKGROUND: In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. OBJECTIVE: The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. METHODS: A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. RESULTS: Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. CONCLUSIONS: These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.

Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.

BACKGROUND: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. OBJECTIVE: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. METHODS: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. RESULTS: Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. CONCLUSIONS: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

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.

Understanding water dynamics in Dal Lake: a comprehensive analysis of physiological parameters and seasonal variations.

Maintaining the standard of water quality in an aquatic habitat necessitates continual assessment of its physicochemical properties. The purpose of this study was to evaluate physicochemical properties and to discuss the causes of spatiotemporal variability in key physicochemical parameters at five different locations of Dal Lake. Water samples were collected in four seasons for 3 years (i.e., January 2019-December 2021) to evaluate various physicochemical properties using standard methods. The analysis shows that the macrophytic development has increased due to organic and inorganic load, leading to the Lake's deterioration. The analysis indicates positive and negative correlations among various parameters across five sampling sites. Principal component analysis shows that two components (PC1 and PC2) explain 47.35, 47.54, and 48.11% of the variability in the years 2019, 2020, and 2021, respectively. From 2019 to 2021, the continuous decrease in dissolved oxygen and increased levels of magnesium, conductivity, alkalinity, total hardness, calcium hardness, total phosphorus, and nitrate-nitrogen suggest a trend toward eutrophication in the lake.

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.

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 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.

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.

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.

Can Mycobacterium tuberculosis infection lead to cancer? Call for a paradigm shift in understanding TB and cancer.

Infections are known to cause tumours though more attributed to viruses. Strong epidemiological links suggest association between bacterial infections and cancers as exemplified by Helicobacter pylori and Salmonella spp. Infection with Mycobacterium tuberculosis (M. tb), the etiological agent of tuberculosis (TB), has been reported to predispose patients to lung cancers and possibly in other organs as well. While this etiopathogenesis warrant inclusion of M. tb in IARC's (International Agency for Research on Cancer) classified carcinogenic agents, the lack of well-defined literature and direct experimental studies have barred the research community from accepting the role of M. tb as a carcinogen. The background research, case studies, and experimental data extensively reviewed in Roy et al., 2021; provoke the debate for elucidating carcinogenic properties of M. tb. Moreover, proper, timely and correct diagnosis of both diseases (which often mimic each other) will save millions of lives that are misdiagnosed. In addition, use of Anti Tubercular therapy (ATT) in misdiagnosed non-TB patients contributes to drug resistance in population thereby severely impacting TB disease control measures. Research in this arena can further aid in saving billions of dollars by preventing the superfluous use of cancer drugs. In order to achieve these goals, it is imperative to identify the underlying mechanism of M. tb infection acting as major risk factor for cancer.

ArgD of Mycobacterium tuberculosis is a functional N-acetylornithine aminotransferase with moonlighting function as an effective immune modulator.

Mycobacterium tuberculosis (M. tuberculosis) encodes an essential enzyme acetyl ornithine aminotransferase ArgD (Rv1655) of arginine biosynthetic pathway which plays crucial role in M. tuberculosis growth and survival. ArgD catalyzes the reversible conversion of N-acetylornithine and 2 oxoglutarate into glutamate-5-semialdehyde and L-glutamate. It also possesses succinyl diaminopimelate aminotransferase activity and can thus carry out the corresponding step in lysine biosynthesis. These essential roles played by ArgD in amino acid biosynthetic pathways highlight it as an important metabolic chokepoint thus an important drug target. We showed that M. tuberculosis ArgD rescues the growth of ΔargD E. coli grown in minimal media validating its functional importance. Phylogenetic analysis of M. tuberculosis ArgD showed homology with proteins in gram positive bacteria, pathogenic and non-pathogenic mycobacteria suggesting the essentiality of this protein. ArgD is a secretory protein that could be utilized by M. tuberculosis to modulate host innate immunity as its moonlighting function. In-silico analysis predicted it to be a highly antigenic protein. The recombinant ArgD protein when exposed to macrophage cells induced enhanced production of pro-inflammatory cytokines TNF, IL6 and IL12 in a dose dependent manner. ArgD also induced the increased production of innate immune effector molecule NOS2 and NO in macrophages. We also demonstrated ArgD mediated activation of the canonical NFkB pathway. Notably, we also show that ArgD is a specific TLR4 agonist involved in the activation of pro-inflammatory signaling for sustained production of effector cytokines. Intriguingly, ArgD protein treatment activated macrophages to acquire the M1 phenotype through the increased surface expression of MHCII and costimulatory molecules CD80 and CD86. ArgD induced robust B-cell response in immunized mice, validating its antigenicity potential as predicted by the in-silico analysis. These properties of M. tuberculosis ArgD signify its functional plasticity that could be exploited as a possible drug target to combat tuberculosis.

Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review.

BACKGROUND: Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics. OBJECTIVE: This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters. METHODS: We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data. RESULTS: From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%). CONCLUSIONS: This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.

Teleological cooption of Mycobacterium tuberculosis PE/PPE proteins as porins: Role in molecular immigration and emigration.

Permeation through bacterial cells for exchange or uptake of biomolecules and ions invariably depend upon the existence of pore-forming proteins (porins) in their outer membrane. Mycobacterium tuberculosis (M. tb) harbours one of the most rigid cell envelopes across bacterial genera and is devoid of the classical porins for solute transport across the cell membrane. Though canonical porins are incompatible with the evolution of permeability barrier, porin like activity has been reported from membrane preparations of pathogenic mycobacteria. This suggests a sophisticated transport mechanism that has been elusive until now, along with the protein family responsible for it. Recent evidence suggests that these slow-growing mycobacteria have co-opted some of PE/PPE family proteins as molecular transport channels, in place of porins, to facilitate uptake of nutrients required to thrive in the restrictive host environment. These reports advocate that PE/PPE proteins, due to their structural ability, have a potential role in importing small molecules to the cell's interior. This mechanism unveils how a successful pathogen overcomes its restrictive membrane's transport limitations for selective uptake of nutrients. If extrapolated to have a role in drug transport, these channels could help understand the emergence of drug resistance. Further, as these proteins are associated with the export of virulence factors, they can be exploited as novel drug targets. There remains, however, an interesting question that as the PE/PPE proteins can allow the 'import' of molecules from outside the cell, is the reverse transport also possible across the M. tb membrane. In this review, we have discussed recent evidence supporting PE/PPE's role as a specific transport channel for selective uptake of small molecule nutrients and, as possible molecular export machinery of M. tb. This newly discovered role as transmembrane channels demands further research on this enigmatic family of proteins to comprehend the pathomechanism of this very smart pathogen.

Depression among healthcare workers in the Eastern Mediterranean Region: a systematic review and meta-analysis.

BACKGROUND: Depression is a major population health challenge globally. This systematic review and meta-analysis aims to (i) determine depression prevalence and (ii) identify the risk and protective factors of depression among healthcare workers (HCWs) in the Eastern Mediterranean Region (EMR). METHODS: The protocol was registered on Open Science Framework (registration ID: https://osf.io/rdv27 ). We searched five databases (PubMed, Embase, PsycINFO, Al Manhal, Google Scholar) till July 22, 2020 without language restrictions. We included studies from the EMR using a depression screening or diagnostic instrument to measure the depression prevalence among HCWs. Studies were assessed and data were pooled using random-effects meta-analysis based on the Cochrane handbook. RESULTS: The systematic review identified 108 studies from 12 EMR countries with varying quality. Working long hours, poor sleep quality and being female were risk factors for depression in EMR HCWs. The meta-analysis comprised 77 studies providing 122 prevalence measures across 7 EMR countries. The pooled prevalence of depression among EMR HCWs was 33.03% (95% CI = 27.40-39.19%). Emergency HCWs had markedly higher rates of depression [53.14% (95% CI = 26.63-77.99%)] compared to HCWs of other specialties. Most studies had an appropriate sample size. CONCLUSIONS: Depression among EMR HCWs is a major concern. Steps must be taken to prevent, identify, and manage depression among HCWs. Fostering a compassionate and empathetic environment is critically important to building a resilient healthcare system. Generating high-quality regional data from longitudinal studies on mental health will further contribute to a better understanding and management of depression among EMR HCWs.

Capacity building in health care professions within the Gulf cooperation council countries: paving the way forward.

BACKGROUND: There is a worldwide shortage of health care workers. This problem is particularly severe in the Gulf Cooperation Council (GCC) countries because of shortages in certain medical disciplines, due to a lack of nationally-trained professionals and a less developed educational system compared to other high income countries. Consequently, GCC countries are heavily dependent on an expatriate health care workforce; a problem exacerbated by high turnover. We discuss challenges and potential strategies for improving and strengthening capacity building efforts in health care professions in the GCC. MAIN TEXT: In the GCC, there are 139 schools providing professional health education in medicine, dentistry, pharmacy, nursing, midwifery, and other specialties. Health education school density reported for the GCC countries ranges between 2.2 and 2.8 schools per one million inhabitants, except in Oman where it is 4.0 per one million inhabitants. The GCC countries rely heavily on expatriate health professionals. The number of physicians and nurses in the GCC countries are 2.1 and 4.5 per 1000 respectively, compared to 2.8 and 7.9 among member countries of the Organisation for Economic Cooperation and Development (OECD). Interestingly, the number of dentists and pharmacists is higher in the GCC countries compared to OECD countries. A nationally trained health care workforce is essential for the GCC countries. Physiotherapy and occupational therapy are two identified areas where growth and development are recommended. Custom-tailored continuing medical education and continuing professional development (CPD) programs can augment the skills of health practitioners, and allow for the expansion of their scope of practice when warranted. CONCLUSION: Capacity building can play an essential role in addressing the major health challenges and improving the overall quality of health care in the region. Efforts aimed at increasing the number of locally-trained graduates and developing and implementing need-based CPD programs are vital for capacity building and lifelong learning in health care professions.

Mycobacterium indicus pranii protein MIP_05962 induces Th1 cell mediated immune response in mice.

Utility of Mycobacterium indicus pranii (MIP) as a multistage vaccine against mycobacterial infections demands identification of its protective antigens. We explored antigenicity and immunogenicity of a candidate protein MIP_05962 that depicts homology to HSP18 of M. leprae and antigen1 of Mycobacterium tuberculosis. This protein elicited substantial antibody response in immunized mice along with modulation of cellular immune response towards protective Th1 type. Both CD4+ and CD8+ subsets from immunized mice produced hallmark protective cytokines, IFN-γ, TNF-α and IL-2. This protein also enhanced the CD4+ effector memory that could act as first line of defence during infections. These results point to MIP_05962 as a protective antigen that contributes, in conjunction with others, to the protective immunity of this live vaccine candidate.

Knowledge and perceptions about Zika virus in a Middle East country.

BACKGROUND: Zika virus, an emerging serious infectious disease, is a threat to persons living or travelling to regions where it is currently endemic, and also to contacts of infected individuals. The aim of this study was to assess knowledge about this new public health threat to persons residing in a Middle Eastern country. METHODS: We conducted a survey at several international universities in Qatar to assess knowledge and awareness about this disease. An adapted version of the survey was also conducted using online channels from Qatar. RESULTS: The median age of the 446 participants, was 25 years, 280 (63%) were females, and 32% were from Gulf Cooperation Council (GCC) or other Middle East countries. Based upon their knowledge about availability of a vaccine, role of mosquitoes and other modes of transmission, and disease complications, we classified respondent's knowledge as "poor" (66%), "basic" (27%) or "broad" (7%). Forty-five (16%) persons with poor knowledge considered themselves to be well-informed. CONCLUSIONS: This report from a sample of persons associated with Middle East educational complex, reveals inadequate knowledge about Zika virus, a serious emerging infectious disease. Although few cases have been reported from the region, future cases are possible, since this area is a transit hub connecting currently infected regions to North America, Europe and Asia. As a preventive measure, an educational program about Zika virus would be valuable, especially for individuals or family members travelling to afflicted regions.

Hiding clinical information in medical images: A new high capacity and reversible data hiding technique.

A new high capacity and reversible data hiding scheme for e-healthcare applications has been presented in this paper. Pixel to Block (PTB) conversion technique has been used as an effective and computationally efficient alternative to interpolation for the cover image generation to ensure reversibility of medical images. A fragile watermark and Block Checksum (computed for each 4×4 block) have been embedded in the cover image for facilitating tamper detection and tamper localization, and hence content authentication at receiver. The EPR, watermark data and checksum data has been embedded using Intermediate Significant Bit Substitution (ISBS) to avoid commonly used LSB removal/replacement attack. Non-linear dynamics of chaos have been put to use for encrypting the Electronic Patient Record (EPR)/clinical data and watermark data for improving the security of data embedded. The scheme has been evaluated for perceptual imperceptibility and tamper detection capability by subjecting it to various image processing and geometric attacks. Experimental results reveal that the proposed system besides being completely reversible is capable of providing high quality watermarked images for fairly high payload. Further, it has been observed that the proposed technique is able to detect and localise the tamper. A comparison of the observed results with that of some state-of-art schemes show that our scheme performs better.