Background and Objectives: Although many studies have reported that kidney donation is not physically harmful to living kidney donors, there are few studies on the psychological changes that they experience, especially post-traumatic growth. This study aimed to investigate the influence of self-determination and social support on post-traumatic growth among living kidney donors. Materials and Methods: This study used a descriptive, cross-sectional design. Data were collected from 114 living kidney donors who visited the outpatient solid organ transplant center at Seoul National University Hospital. The data were analyzed using the t-test, one-way analysis of variance, and stepwise multiple regression. Results: The results showed that the mean for post-traumatic growth of living kidney donors was 4.24 (0.81), a level higher than the midpoint. The factors affecting total post-traumatic growth were the relatedness of self-determination, the social support of their significant other, and donor type. In particular, the relatedness of self-determination was a significant factor affecting changed perceptions of self, relating to others, and spiritual change, subscales of post-traumatic growth. Additionally, the social support of donors’ significant others was a significant factor affecting relating to others and new possibilities, subscales of post-traumatic growth. Conclusions: Healthcare providers should endeavor to help living kidney donors experience post-traumatic growth, which can be facilitated by improving their self-determination and social support.  Kidney donation is an important life-saving method for patients with end-stage renal disease, with living kidney transplantation accounting for nearly 40% of overall kidney transplantation activity ,2. CompaOrgan trafficking is strictly prohibited by the Organ Transplantation Act, and living kidney donations are guaranteed by the same act so that they can be voluntarily carried out. Voluntariness and self-determination are important in the legal and ethical frameworks of living kidney donation. For kidney donations not to be determined or manipulated by coercion, the process must be performed by the autonomous and voluntary behavior of the living kidney donor . AccordiDonating a kidney is a life-changing experience , and livAs motivation induces personal growth and self-conceptualization, according to self-determination theory, autonomous motivation can lead to positive cognitive, behavioral, and affective outcomes . Living Social support refers to all forms of help exchanged in stable interpersonal relationships. Patients with high social support experience higher post-traumatic growth because they can express their thoughts and feelings more often in stable interpersonal relationships . In otheYucetin et al.  reportedHowever, few studies have investigated the effects of living kidney donors’ general characteristics, self-determination, and social support on post-traumatic growth. Investigating the effects of these factors on post-traumatic growth may help develop interventions in which kidney donors can grow mentally through the kidney donation process. Therefore, this study investigated the effects of living kidney donors’ general characteristics, self-determination, and social support on post-traumatic growth.n = 98) was calculated under the assumptions of a significance level of 0.05, an effect size of 0.15, and a power of 0.80. A total of 120 living kidney donors agreed to participate in the study; however, 114 living kidney donors completed the questionnaire. All questionnaires were distributed by personal contact, exercising appropriate precautions during the COVID-19 pandemic. The overall response was therefore 95%. Thus, data from 114 participants (a suitable sample size) were included in the final analysis.This study used a cross-sectional and descriptive design to investigate the factors affecting post-traumatic growth of living kidney donors in South Korea. Participants were living kidney donors who visited the outpatient solid organ transplant center at Seoul National University Hospital for regular post-donation medical physical examinations between 1 November 2019 and 15 August 2020. The participants were adults aged ≥18 years who understood the purpose of the study, agreed to participate in the study, and were able to understand and respond to the questionnaire. The required sample size was calculated using G Power 3.1.9.7  . The minParticipants’ general characteristics were examined by age, sex, education, marital status, religion, donor type, donation recommendation, health recovery after donation, and autonomy of donation decision.The Post-Traumatic Growth Inventory (PTGI) developed by Tedeschi and Calhoun , translaThe Multidimensional Scale of Perceived Social Support (MPSS), developed by Zimet and Dahlem , was useSelf-determination was measured using the Korean version of the Basic Psychological Needs Index developed by Deci and Ryan , which wThe study was conducted between 1 November 2019 and 15 August 2020, at the outpatient solid organ transplant center at Seoul National University Hospital. Data were collected from living kidney donors by using structured questionnaires and medical charts. The researchers explained the purpose of the study and distributed the questionnaires to the participants. The subjects agreed to participate prior to returning the completed questionnaires to the researchers. All participants were given a small gift of gratitude for their voluntary participation.This study was approved by the Institutional Review Board of Seoul National University . The participants were enrolled only after they provided written informed consent. In addition, participants were informed that they could choose not to participate in the study at any time without any personal disadvantage or penalty. All self-questionnaires were completed anonymously and data on each participant’s information were coded. The collected data were maintained in the researchers’ private office, which was locked to ensure confidentiality.Data were analyzed using SPSS statistical software . Participants’ general characteristics, post-traumatic growth, the multidimensional scale of perceived social support, and self-determination were analyzed using descriptive statistics (frequency and percentage). Differences in post-traumatic growth according to the general characteristics were analyzed using the independent t-test and one-way ANOVA, and the Scheffé post-test was used. Correlations among post-traumatic growth, the multidimensional scale of perceived social support, and self-determination were examined using Pearson’s correlation coefficients. Stepwise multiple regression analysis was performed to analyze factors affecting post-traumatic growth. Out of the 120 participants, 114 fully participated in the study (response rate: 95%) . The meap = 0.018), religion , and donor type . Regarding the total post-traumatic growth scores according to the participants’ general characteristics, there was a significant difference in gender  and type of donor . The results of the post hoc tests with ANOVA showed that spousal donors showed significantly higher changes in self-perception scores than family donors and non-blood relationship donors. Regarding changes in relationships with others according to the participants’ general characteristics, there were significant differences depending on the type of donor  and donation recommendation . Regarding spiritual changes according to the participants’ general characteristics, there were significant differences in age group , gender , marital status , religion , and type of donor . Post hoc testing showed that the spousal donors showed significantly higher spiritual changes than the family donors. In the subdomains of post-traumatic growth, regarding differences in change in self-perception according to participants’ general characteristics, there were significant differences in gender . The next most powerful factor affecting post-traumatic growth was the type of donor , followed by the social support of donors’ significant others . The explanatory power of these variables was 19.5% . The variance inflation factor values were all less than 10, with no multicollinearity, and the Durbin–Watson value was sufficient to satisfy the independence of residuals , spousal donor of donor type , female gender , and family support . Factors affecting changes in relationships with others’ scores of post-traumatic growth were more significant social support of donors’ significant others  and the relatedness of self-determination . Additionally, factors affecting a higher identification of new possible scores of post-traumatic growth were more highly significant for social support of donors’ significant others . Factors affecting higher spiritual change scores of post-traumatic growth were higher relatedness of self-determination , female gender , religion , and spousal donor of donor type . This explained variance ranging from 5.4% (identifying new possibilities) to 36.9% .Factors affecting higher changes in self-perception scores of post-traumatic growth were the relatedness of self-determination  out of 6, a level higher than the midpoint. This score is higher than the average score measured for post-traumatic growth in patients with breast cancer  and kidnA novel finding of this study was that the main factor affecting post-traumatic growth was the relatedness of self-determination. In self-determination theory, relatedness is one of the basic psychological needs that leads to self-determination by inducing intrinsic and extrinsic motivations, along with autonomy and competence as feelings of belongingness and connectedness with others . This reDonor type is an additional factor that was found to influence post-traumatic growth, and living kidney donors who donated their kidneys to their spouses (spouse donors) experienced more post-traumatic growth than living kidney donors who donated their kidneys to their families. Spouse donors can have a higher partnership satisfaction with the kidney recipient compared to non-spouse donors, and this high partnership satisfaction can have a positive effect on the body and mind of living kidney donors . HoweverIt is also important to note that social support, especially from other living donors and healthcare providers, affects living donors’ post-traumatic growth. Social support may affect the post-traumatic growth of living kidney donors because social networks may mitigate stress and provide donors with opportunities to adopt a new view associated with increased growth . InformaIn this study, living kidney donors experienced post-traumatic growth, and the factors affecting post-traumatic growth were the relatedness of self-determination, donation type (spouse donor), and social support. Therefore, it will be helpful to use strategies that increase the relatedness of self-determination and social support when developing an intervention program in which living kidney donors can experience post-traumatic growth.This study had several limitations. First, the generalization of the research results requires caution, as the participants were living kidney donors who visited a university hospital. Next, because this study investigated the post-traumatic growth of living kidney donors using a cross-sectional method, continuous changes in post-traumatic growth could not be identified. In addition to self-determination and social support, there may be additional variables that affect post-traumatic growth, but these were not considered in this study.This study suggests that living kidney donors experience post-traumatic growth, and that self-determination and social support affect post-traumatic growth. These results show that various physical and psychological problems that can occur in living kidney donors can be solved using post-traumatic growth. Therefore, health care providers should provide various types of information and psychological support to kidney donors, and help the relatedness of self-determination of living kidney donors to become sufficient enough so that living kidney donors can experience post-traumatic growth.


Material resources that affect daily living conditions may be salient for cognitive aging in low-income settings, but evidence is limited on this topic. We investigated relationships between long-term trends in household material resources and subsequent cognitive function among 4,580 adults aged ≥40 in a population-representative cohort in Agincourt sub-district, South Africa, from 2001-2015. Household material resources  were assessed biennially from 2001-2013. We evaluated mean resources, volatility in resources, and change in resources over this period in relation to cognitive function in 2014/2015. Higher mean household resources and larger improvements over time in resources were positively associated with subsequent cognitive function, independent of confounders. Findings were largely driven by modern amenities for food preparation, transportation, and communication outside of the household. Access to these amenities may support cognitive aging through boosting nutrition and cognitive reserve and should be investigated further.


The delayed sleep-wake phase is commonly observed in major depressive disorder (MDD) and thought to be associated with functional impairments. This study aimed to evaluate the relationship between the delayed sleep-wake phase, cognitive dysfunction, social dysfunction, and quality of life in patients with MDD.This cross-sectional design included 33 outpatients with MDD. Objective sleep-wake rhythm was assessed by actigraphy. Functional impairments were evaluated by the Japanese version of the Brief Assessment of Cognition in Schizophrenia (BACS-J), World Health Organization Disability Assessment Schedule (WHO-DAS), and Euro QOL 5 dimensions (EQ5D).r = –0.489, p = 0.010), WHO-DAS score , and EQ5D score . No significant correlation was found between the other actigraphic sleep parameters  and functional impairments.Actigraphic assessment of the delayed sleep-wake phase (midpoint of sleep) was significantly correlated with the composite score of the BACS-J (Our study’s results suggested that the delayed sleep-wake phase is associated with cognitive dysfunction, social dysfunction, and deteriorated quality of life in patients with MDD. Clinicians should pay attention to the sleep-wake rhythm in patients with MDD in clinical settings. Sleep disturbances are common in major depressive disorder (MDD). More than 90% of patients with MDD have sleep disturbances . PreviouCognitive dysfunction is a common functional impairment of various psychiatric disorders, including schizophrenia , bipolarFew studies have suggested the relationship between sleep disturbances and cognitive dysfunction in patients with MDD . HoweverThis study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of Kyorin University. Written informed consent was obtained from the patients.Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition . All participants were instructed to wear the watch for 14 consecutive days and remove the device only for water-based activities. We used sleep parameters derived from actigraphy software including bed in time, sleep latency, total sleep time, sleep efficiency, and wake-up time. The midpoint of sleep was calculated with the average time point between bed in time and wake-up time. Subjective sleep disturbances was assessed by the Insomnia Severity Index (ISI), which is a 7-item brief screening measure of insomnia and as an outcome measure in treatment research . We alsoCognitive function was assessed using the Japanese version of the Brief Assessment of Cognitive in Schizophrenia (BACS-J) . The BACSocial functional impairment was assessed using the World Health Organization Disability Assessment Schedule (WHO-DAS), a self-administered questionnaire. Items were assessed on a 5-point scale (1 = none to 5 = extreme or cannot do), with a higher subscale score indicating social functional impairment. Quality of life (QOL) was assessed using the Euro QOL 5 dimensions (EQ5D) , a self-We also evaluated patients’ demographics and characteristics .p-value <0.05 was considered to indicate a statistically significant difference.The Pearson correlation coefficient was used to compare the actigraphic parameters and BACS-J, WHO-DAS, and EQ5D scores. The Mann-Whitney U-test was used to compare BACS-J, WHO-DAS, and EQ5D scores between patients who took psychotropic medications  and those who did not. All statistical analyses were performed using SPSS, version 28 for Windows . A r = –0.56, p = 0.003) and the composite score . There was no significant correlation between other actigraphic sleep parameters  and z-scores of the BACS-J  and EQ5D scores (r = 0.47, p = 0.013; Among the objective sleep parameters in the actigraphic assessment, only the midpoint of sleep showed a significant negative correlation with verbal fluency (e BACS-J . There wPrevious studies have reported a significant association between delayed sleep-wake phase disorder and depression. Several studies suggested that depressive symptoms were common in patients with delayed sleep-wake phase disorder , 23. CirAlthough the exact mechanisms linking delayed sleep-wake rhythm and mood disturbances are not yet fully understood, there is a complex and bidirectional relationship between delayed sleep-wake rhythm and the pathophysiology of MDD. A delayed sleep-wake rhythm may cause depressive symptoms in some patients, while residual depressive symptoms may cause delayed sleep-wake rhythm in others. A previous study reported that circadian misalignment in patients with delayed sleep phase disorder is strongly associated with depressive symptoms even after controlling for other confounding factors , which iA previous systematic review and meta-analysis suggested that cognitive dysfunctions across the domains of attention, executive function, and memory were core features of cognitive dysfunction in patients with MDD compared to healthy controls . A previFew studies have investigated the relationship between sleep disturbances, social dysfunction, and quality of life in MDD patients. A previous observational study suggested that MDD patients with subjective sleep disturbances had more severe social dysfunction than MDD patients without sleep disturbances . AnotherConversely, our study only suggested an association between the midpoint of sleep and functional impairment in the objective actigraphic assessment. Because no other study investigated the association between functional impairment and subject/objective sleep problems simultaneously, it is difficult to understand this discrepancy. Previous forced desynchrony studies in the laboratory might support our result that delayed sleep-wake rhythm could worsen functional impairments, including sleepiness and cognitive dysfunction , 31. TheThis study has several limitations. First, the sample size of this study was small, which caused limitation of statistical power. Second, because this study had a cross-sectional design, it is difficult to draw any conclusion about the causal relationships between sleep disturbances and cognitive dysfunctions. Third, because we did not use the controls, it is difficult to interpret the result of this study. Fourth, because many patients refused to participate in this study, there could be selection bias in our study sample, which might affect the results of this study. For example, the average total sleep time might be longer, and wake up time might be later in our study patients than in typical MDD patients and normal controls. Fifth, we performed the BACS-J between 10 AM and 1 PM depending on the patients’ visiting schedule at the hospital, which might affect scores of BACS-J, especially in patients with delayed sleep-wake rhythm. Sixth, although we evaluated the influences of psychotropic medications on daytime functions, we could not detect a significant statistical association between psychotropic medications and daytime functions, possibly due to the small sample size or that the psychotropic medications affected daytime functions. Seventh, although we used actigraphy as an objective sleep parameter, a polysomnographic assessment would be desirable to confirm the relationship between sleep disturbances and functional impairment in MDD patients.In conclusion, this study’s results suggested that the delayed sleep-wake phase is associated with cognitive dysfunction, social dysfunction, and deteriorated QOL in patients with MDD. To prevent relapse of depressive symptoms, clinicians should pay attention to the disturbance of sleep-wake rhythm in patients with MDD in clinical settings.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics Committee of Kyorin University. The patients/participants provided their written informed consent to participate in this study.YT: drafting the manuscript, organizing and implementation of this study, and interpreting the data. YT, YK, KW, and TH: recruiting the participants. MI: collecting the data. YT, MI, and YN: statistical analysis. All authors took part in revising the manuscript and approved the final version of the manuscript.


The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature. The Internet of Things is a vigorously flourishing communication technology that introduces a new spectrum of smart communications . It is eHowever, the Routing Protocol for Low-Power and Lossy Networks (RPL) is gaining significant attention. The Internet Engineering Task Force (IETF) designed an IPV6-based RPL protocol to expedite the routing mechanisms of resource-constrained networks . This prIn the present era, combinations of deep-learning- and fog-computing-based schemes are considered fascinating solutions to overcome such challenges . Fog comWe have designed a fog-assisted deep-learning-empowered IDS, which is called the Cu-DNNGRU, to examine suspicious events in RPL-based resource-constrained smart industries.For the purpose of training, the proposed model was integrated with N-BaIoT, which significantly enhanced the detection capabilities of the designed framework.The established framework contains a combined sequence of the Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU classifiers for comparison purposes, and they were trained and evaluated with the same dataset and performance metrics.The performance of the designed framework was also evaluated in comparison with some well-known benchmarked schemes.The authors also employed ten-fold cross-validation to show unbiased results.The simulation results support the validation of the proposed framework in terms of threat detection efficiency, accuracy, precision, resource consumption, and computational complexity.The significant contributions of this research work can be listed as follows:This research study is organized systematically. RPL-based resource-constrained smart industries are attaining significant attention where notable efforts are being made towards its security. Here, we addressed some meaningful research studies surrounding this domain.Authors  have proSingle-Hidden Layer Feed-forward Neural Network (SLFN) is one of the best classifiers for responsive intrusion detection in industrial environments. It, with the LSTM classifier, brings additional strength to the system.Researchers have adopted these two classifiers to design a multifunctional threat classification mechanism. Furthermore, the IoT-ID20 dataset is used for training purposes . AnotherThe authors present a deep learning-based IDS developed on a custom dataset. The proposed scheme utilizes Multilayer Perceptron (MLP), Decision Tree (DT), and LSTM classifiers to enable efficient intrusion detection. While performing simulations, the proposed framework has projected splendid performance on analytical performance metrics . ResearcThe authors designed an IDS composed of two charismatic technologies, deep learning and fog computing, where both technologies are assigned specific roles. Deep learning participates in intrusion detection activity, whereas fog computing provides an ideal infrastructure to implement that deep learning-based intrusion detection system. Fog computing also offers a systematic architecture in which different tasks are divided among various communication nodes according to their resource occupancy. RPL-based communication networks are just an application area for which we have proposed this IDS. This way, a comprehensive mechanism is formulated where both these technologies, i.e., deep learning and fog computing, rub shoulders together to perform intrusion detection in RPL-based resource-constrained smart industries.In the proposed detection framework, DNN participates with four layers of neurons bearing 400, 300, 200, and 50 layers of neurons, whereas GRU contributes with two layers carrying 200 and 100 neurons, respectively. As shown in x-values numbers from 1 to n as an input and computes the predicted y-hat value. Vector x contains the values of the features in one of the m examples from the training set. In each iteration, the neuron calculates a weighted average of the values of the vector x based on its current weight vector w and adds bias. Finally, the result of this calculation is passed through a non-linear activation function.In the proposed IDS, the deep learning-based algorithm purely focuses on the intrusion detection activity and effectively interrogates anomalous events in RPL-based resource-constrained smart industries. The proposed Cu-DNNGRU is an amalgamation of two prestigious classifiers, Deep Neural Networks (DNN) and Gated Recurring Unit (GRU). A deep Neural Network (DNN) is an enhanced version of an Artificial Neural Network (ANN) with extra layers. The layers of neurons are organized in a sequence of multiple layers, where neurons receive the neuron activations from the previous layer as input and perform a simple computation. Each neuron receives a set of The Gated Recurrent Unit (GRU) is an advanced version of the Recurrent Neural Network (RNN) and is quite similar to the LSTM. GRU also utilizes gates to regulate the information flow. They choose which information should be sent to the output and are referred to as the two vectors. Its specialty is storing old data rather than getting rid of it since it is not essential to the forecast. GRU consists of an update gate Equation  is used.Equation  to storeFinally, Equation  is used Algorithm 1 Proposed hybrid detection framework.1:Input: Dataset = 2:Output: 3:Split the 4:foreach layer of do5:   6:   7:   8:   9:   10: 11: 12:end for13:14:15:while True do16: 17:if the value predicted = 0 then 18:  Return Benign19:else 20:  Return attack type21:end if 22:end whileThe complete workflow of the proposed detection scheme is depicted in Algorithm 1.The proposed threat detection mechanism is massively privileged by fog computing in terms of operational architecture. In resource-constrained smart industries, the operational role needs to be assigned according to the resource occupancies of the concerned nodes. Hence, fog computing provides an impressive infrastructure where communication nodes are categorized into various layers that are indulged in the cloud layer, the fog layer, and the edge layer, respectively. Starting from the bottom, the edge layer comprises a scattered dimension of RPL nodes identically tied up in organic clusters. The second layer is the fog layer, which administers the functionalities of the edge layer and offers substantial durability to the system by assisting with optimal routing streams within the network. The fog layer is then in coordination with the cloud layer which supervises the functionalities of the fog layer and performs superior functionalities such as data storage, extinguishable administration, etc. That phenomenon squarely tends to yield highly productive management of system resources. The deep learning-based threat investigation approach aggregately works in coordination with fogging. The threat detection model is originally trained on a comprehensive dataset to make it conceived with various generic impressions of security threats. Henceforth, the framework is implemented on the fog and cloud layer. The fog layer persuasively flags all the suspicious events from the edge layer. However, the fog nodes also possess a probabilistic risk of being compromised. Such uncongenial circumstances may question overall security, reliability, and efficiency of the whole communication network. That phenomenon stimulates the need of a backup plan to cater these unexpected misshaping. Hence, we have introduced two layers of security that leverage an extended security ecosystem. This fog layer serves underneath the cloud layer, so the unaddressed security concerns are then consequentially dismantled by the cloud layer. The overall proposed network model can be witnessed in The dataset is a substantial element of every DL-based intrusion detection scheme . SelectiPre-processing involves the standard mechanism to organize the data in a usable form, such as gratuitous spaces and eliminating non-value entries. The N-BaIoT dataset is pre-processed to achieve great utility through the sklearn pre-processing label encoder. The deep learning algorithm solely reckoned on numeric values; the sklearn label encoder has converted all non-numeric values to numeric entities. Dataset normalization converts all numeric columns to the same scale without changing the range of values. It is only necessary to normalize datasets with a wide range of values. Minimax Scalar is used to normalize the N-BaIoT dataset, which is generally scaled to a predetermined range between zero and one. The suggested model performs better and yields more valuable results with a normalized dataset.The proposed scheme’s empirical performance test is conducted on an 8th-generation computer machine furnished with a 3.33 GHz processor, 16 GB RAM, and a Windows 10 operating system. The Graphical Processing Unit (GPU) used for simulations is Geforce-1060, equipped with Python as a programming language and Numpy, Tensorflow, Pandas, Keras, and Scikitlearn libraries. The experimental setup can also be overviewed in The proposed DNNGRU framework has been evaluated on a comprehensive performance matrix including accuracy, precision, recall, ad f-Score as simulation parameters. The accuracy of a system is calculated by the accumulative summation of the True Positives (TP), the True Negatives (TN), the False Positives (FP), and the False Negatives (FN). The recall is considered an essential element in ascertaining the system’s performance. It denotes the average number of correct analyses released by an algorithm. In some cases, the term precision swapped places with recall because it affirms the accumulative projected by a framework.The performance of the proposed Cu-DNNGRU is evaluated concerning other competitive classifiers, i.e., Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU, under a reasonable performance matrix equipped with accuracy precision, recall, and f-Score. DNN-GRU was able to effectively learn from the dataset, as evidenced by the results produced in terms of accuracy vs. loss, as shown in The proposed Cu-DNNGRU is evaluated on a ten-fold cross-validation under an investigative variety of performance parameters such as accuracy, precision, recall, and F1-score. It can be seen in Accommodating a broader variety of assessment metrics, comprising the True Positive Rate (TPR), True Negative Rate (TNR), and Matthews Correlation Coefficient (MCC), the proposed Cu-DNNGRU is evaluated in comparison with Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. We have further investigated the performance of the proposed Cu-DNNGRU on rational performance metrics, including False Positive Rate (FPR), False Negative Rate (FNR), False Detection Rate (FDR), and False Omission Rate (FOR). It can be seen in Furthermore, a confusion matrix summarizes the performance of a DL-based classification algorithm. Calculating a confusion matrix can provide a more accurate perspective of the categorization model’s accomplishments. The proposed Cu-DNNGRU is evaluated in terms of confusion metrics as well, where it illustrates distinguished strengths over Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU, as shown in The operating performance of the DL classifiers can also be measured using the Receiver Operating Characteristics (ROC) Curve. An algorithm is used to determine the best possible threshold for a specific classification algorithm to increase the number of accurate results while minimizing false positives. TPR and FPR trade-offs can be determined by employing several measurements of probability thresholds, such as ROC Curves. The proposed Cu-DNNGRU is extensively evaluated along with Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. Moreover, the training time is a crucial metric for assessing a system’s overall performance since it measures how long it takes for a plan to acquire the intrinsic sustainability of its absolute features. The proposed Cu-DNNGRU is further compared with some state-of-the-art DL classifiers from the literature. The core objective of this extended performance evaluation is to obtain a comprehensive analytical idea regarding the performance of the proposed framework with its competitive algorithms. The performance comparison is conducted on the core performance parameters, i.e., accuracy, precision, recall, and F1-score. This research study is drafted about intrusion detection in RPL-based resource-constrained smart industries. We have proposed a Fog-assisted DL-enabled intrusion detection framework (Cu-DNNGRU) to interrogate a diversified array of potential security threats in smart industries. The under-contention model is trained on the N-BaIoT dataset, and its performance is evaluated on a reasonable spectrum of performance parameters equipped with accuracy, precision, recall, and F1-score. The proposed framework is then compared with several distinguished DL classifiers such as Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU for comprehensive performance analysis. The performance is further investigated along with some benchmarked DL algorithms from the literature. The systematic simulation results validate the effectivity of the proposed model with 99.39% accuracy, 99.09% precision, 98.89% recall, and 99.21% F1-score. The designed framework has overwhelmed existing competitive schemes with dominant performance towards efficient intrusion detection against less consumption of system resources. Finally, we aim to train the proposed model on different datasets and enhance its detection strengths in the future.


Dementia patients often experience behavioral and psychological symptoms (BPSD), which severely affect their quality of life and activities of daily living. Non-pharmacological interventions are effective in treating BPSD, according to multiple clinical trials and systematic reviews. However, the optimal non-pharmacological treatment remains controversial. Therefore, the study aims to evaluate and compare multiple non-pharmacological methods for treating BPSD in order to identify the optimal non-pharmacological intervention.This study aims to perform a systematic review and network meta-analysis of evidence on non-pharmacological interventions in the treatment of BPSD, which may potentially guide future research and clinical decisions.In order to select potentially relevant randomized controlled trials (RCTs), 10 academic databases and 3 clinical trial registries will be systematically searched from inception until the 1 October 2022. Two researchers will independently extract information from eligible articles. The primary outcome is the severity of BPSD. Herein, Pairwise and Bayesian network meta-analyses will be conducted utilizing STATA 15.0 and ADDIS 1.16.8. Evidence quality will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE).Results from this study will be published in peer-reviewed journals or conference reports.In this study, we aim to comparatively assess the efficacy of various non-pharmacological treatments for BPSD. Findings from this review will help clinicians to make evidence-based treatment decisions.https://www.crd.york.ac.uk/prospero/], identifier [CRD42022352095].[ Globally, approximately 50 million people are living with dementia, and the number is expected to reach 152.8 million by 2050 –5. It isAntipsychotics and anticonvulsants have demonstrated significant efficacy in the treatment of certain aspects of BPSD but are also associated with a higher rate of adverse events . In thisNetwork meta-analysis (NMA) is a statistical method that can synthesize direct and indirect evidence to rank different interventions from multiple trials in a network , 15. ThiThe purpose of this study is to evaluate the efficacy of non-pharmacological interventions in the treatment of BPSD and provide a clinical reference for health policy decision-makers.This systematic review (SR) protocol has been registered on PROSPERO (CRD42022352095). The protocol will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) guidelines , accordiThe study will not involve direct participant participation. Only randomized controlled trial (RCT) data from the databases will be used.Randomized controlled trials on non-pharmacological therapies of BPSD, regardless of language or publication type, will be considered.There will be no limitations on participants’ gender, age, or ethnicity. Regardless of the subtype or method of diagnosis, individuals must be diagnosed with dementia. Any physical or psychiatric comorbidities are allowed.Non-pharmacological interventions such as physical exercise , 20, musDifferent non-pharmacological therapies will form the basis for the control group, which will include the placebo group, waiting-list group, treatment as usual group, conventional-based medicine group, etc.Exclusion criteria for studies are as follows:1.Non-randomized clinical studies, quasi-RCTs, cluster RCTs, case studies, qualitative studies, conference abstracts, animal studies, letters, comments, and duplicated articles.2.A lack of research information.Clinical trials.gov, and ChiCTR). Furthermore, we will manually search the included papers’ references and similar published SRs for related studies.Studies published since inception to 1 October 2022 will be retrieved from the following databases: CNKI, WF, VIP Database, SinoMed, Web of Science (WOS), Embase, PubMed, PsycINFO, the CENTRAL, and AMED. To minimize publication bias, we will also retrieve data from clinical registries  will screen titles, keywords, and abstracts for relevance. Duplicate and ineligible studies will be excluded from the review process. All remaining studies will be examined for inclusion once the full text has been reviewed. In case of disagreements, a third reviewer (F-RL or L-XJ) will countercheck and arbitrate.Two independent investigators (Y-QL and Z-HY) will extract data using a pre-designed extraction form including (1) identification information ; (2) general information ; (3) participants ; (4) interventions and control; (5) outcomes; and (6) main results. We will contact respective authors concerning missing data, if any. The primary outcome is the severity of BPSD assessed using the Neuropsychiatric Inventory (NPI) . The NPI(i)Depression scores will be calculated using the Cornell Scale for Depression in Dementia (CSDD). It is widely used to assess depressive symptoms in patients with dementia. It is administered through interviews with patients and caregivers . The CSD(ii)The Cohen-Mansfield Agitation Inventory (CMAI) will be used to evaluate agitation. It is one of the most commonly used scales to evaluate behavioral symptoms of dementia and is focused on measuring agitation and aggression . It cons(iii)Apathy will be measured using the Apathy Evaluation Scale-Clinical version (AES-C). Clinicians often use this scale to assess and quantify the emotional, behavioral, and cognitive domains of apathy . The sca(iv)Activities of daily living will be measured using the Abilities of Daily Living (ADL) scale . The ADL(v)Quality of life will be assessed using the Quality of Life in Alzheimer’s Disease (Qol-AD) scale . The Qol(vi)Adverse events associated with intervention approaches will be described directly in order to assess safety.1Two assessors (Y-QL and Z-HY) will independently appraise the quality of the selected RCTs using the Risk of Bias Assessment Tool (Rob 2.0) from the Cochrane Collaboration . There aI2 statistics and p-value. A fixed-effects model will be applied if p > 0.1 and I2 < 50%; otherwise, the random effects model will be used . As outcomes, we will use pre-post differences or endpoint scores for the included RCTs. The relative risk (RR) of dichotomous data will be presented with a 95% confidence interval (CI), while the weighted mean differences (WMD) of continuous data will be presented with 95% CI. The statistical heterogeneity will be identified and measured by  be used .p < 0.05. To evaluate the convergence of the results, we will analyze the potential scale reduction factor (PSRF), with a PSRF value close to 1 indicating successful convergence  and Markov Chain Monte Carlo (MCMC) algorithm to perform Bayesian network analysis . Furthervergence .I2 ≥ 50% and p < 0.05  system will be used to assess the quality of evidence . EvidencThere is often great concern about cognitive decline when an individual develops dementia. Numerous non-pharmacological interventions, such as physical activity interventions, cognitive training, cognitive stimulation, and combinations of these interventions, have been proven effective in improving cognitive function in patients with dementia –46. HoweNetwork meta-analysis allows for the comparison of multiple interventions to obtain an efficacy ranking based on direct and indirect comparisons , allowinPROSPERO registration will include protocol amendments. We will document and publish any modifications to this protocol along with the results of the systematic review.Studies published since inception to 1 October 2022 will be retrieved from each databases.In December 2022, this study will be completed, and the timeline will be as follows:–Complete the data extract by the end of November 2022.–Complete the data analysis and the manuscript writing by the end of December 2022.Every aspect of the work must be examined and resolved by the authors to ensure its accuracy and integrity. As no private or confidential patient data will be included, ethical clearance is not required. The privacy and rights of the individual will not be compromised.Y-QL, Z-HY, L-XJ, and F-RL: conception and design. Y-QL and Z-HY: collection and assembly of data, data analysis, and interpretation. All authors wrote of the manuscript and approved the submitted version.


Identifying signs of medical distress prior to in-hospital cardiac arrest (IHCA) is important to prevent IHCA and improve survival. The primary objective of this study was to investigate the association between signs of medical distress present within 60 minutes prior to cardiac arrest and survival after cardiac arrest.The register-based cohort study included adult patients (≥18 years) with IHCA in the Swedish Registry of Cardiopulmonary Resuscitation (SRCR) from 2017-01-01 to 2020–07-15. Signs of distress prior to IHCA were defined as the medical signs arrhythmia, pulmonary oedema, hypotension, hypoxia or seizures present within 60 minutes prior to cardiac arrest (pre-arrest signs). Using multivariable logistic regression, the association between these pre-arrest signs and 30-day survival was analysed in both unadjusted and adjusted models. The covariates used were demographics, comorbidities, characteristics and treatment of cardiac arrest.In total, 8525 patients were included. After adjusting for covariates, patients with arrhythmia had a 58% higher probability of 30-day survival. The adjusted probability of 30-day survival was 41% and 52% lower for patients with hypotension and hypoxia prior to IHCA, respectively. Pulmonary oedema and seizures were not associated with any change in 30-day survival.Among signs of medical distress prior to in-hospital cardiac arrest, arrhythmia was associated with a higher 30-day survival. Hypotension and hypoxia were associated with lower survival after IHCA. These findings indicate that future research on survival after cardiac arrest should take pre-arrest signs into account as it impacts the prerequisites for survival. In Sweden over 2000 in-hospital cardiac arrest (IHCA) occur per year and the prognosis is often poor.Predictors of poor outcome after IHCA include male sex, age 60 or older and several comorbidities.12.By understanding how the medical circumstances preceding cardiac arrest may affect survival, physicians may be able to prevent cardiac arrest preceded by medical signs of distress (hereinafter referred to as pre-arrest signs). When a cardiac arrest has occurred, the combined knowledge of factors associated with poor outcome including pre-arrest signs, will support the physician in evaluating the decision of discontinuing futile treatment. Therefore, the primary objective of this study was to investigate the association between medical signs of distress present within 60 minutes prior to the cardiac arrest and 30-day survival. The secondary objective was to investigate the association between the pre-arrest signs and shockable first rhythm.The study design was a population-based retrospective observational study. It is reported according to the STROBE guidelines.The SRCR is a national quality registry started in 1990. Initially only including out-of-hospital cardiac arrests, in 2006 it was expanded to include IHCA. This registry is financed by the Swedish government and counties as well as the Swedish CPR council. In the SRCR, IHCA are defined as cardiac arrests that occur in patients treated in a hospital with an Intensive Care Unit and a resuscitation team. All hospitals in Sweden fulfilling these criteria participate in data registration. Continuous assessments, as part of the registry’s quality work, have demonstrated level of ascertainment exceeding 90% in the last 5 years. Patient data is registered based on Utstein-framework by the nurse/doctor responsible for the patient in the hospital wards.Relevant data retrieved from the SRCR included demographic, clinical characteristics of the cardiac arrest, pre-arrest signs, date of cardiac arrest and date of death. Demographic data included gender, age at cardiac arrest and comorbidities. Clinical characteristics of the cardiac arrest were variables such as witnessed arrest, telemetry monitoring, first rhythm, CPR before resuscitation team arrived, low-flow time (time from CPR start to ROSC), time from cardiac arrest to ROSC, total no. of defibrillations, if the patient was intubated during the cardiac arrest, if the patient received mechanical chest compressions, if adrenaline was administered and if anti-arrhythmic agents were administered.The pre-arrest signs were defined as medical signs of distress that were observed within 60 minutes prior to the cardiac arrest. The medical signs that are reported in the present study are listed with definitions according to the SRCR in The primary outcome was survival to 30 days after the cardiac arrest. The secondary outcome was shockable first rhythm.Baseline characteristics were presented as number (percentage) for categorical variables and mean (standard deviation) for continuous variables. The baseline characteristics were presented for the overall study population as well as subdivided into patient groups with and without the respective pre-arrest signs and one subgroup with patients without any pre-defined pre-arrest signs.The association between the pre-arrest signs and 30-day survival outcome was analysed using multivariable logistic regression and presented as odds ratio (OR). As some patients could have more than one pre-arrest sign, the unadjusted odds ratio for each pre-arrest sign was determined using a multivariable logistic regression with all pre-arrest signs. The adjusted models included the following covariates sequentially: demographics (age (as a continuous variable) and sex), comorbidities , heart failure, diabetes), cardiac arrest clinical characteristics , cardiac arrest treatment . The covariates selected were variables that presented differently in the patient group with a pre-arrest sign compared to the group without that pre-arrest sign, as seen in the baseline table. Patients with missing data for pre-arrest signs were excluded from the analysis.The association between the pre-arrest signs and shockable first rhythm was analysed using multivariable logistic regression and presented as crude and adjusted odds ratio. The adjusted model included the same covariates as the logistic regression model for 30-day survival. However, covariates related to cardiac arrest treatment were not adjusted for in the regression model for shockable first rhythm.All analysis were performed using R 4.0.2 . Odds ratio from the logistic regressions were presented with 95% confidence interval (CI).Of the 8525 patients included in the study, 38.5% were female, and the average age was 71.9 years (SD 13.4). The most common comorbidity was heart failure. Arrhythmia was the most frequent pre-arrest sign and seizure was the least common pre-arrest sign.Baseline characteristics for all patients included in the study as well as the subgroups with and without each pre-arrest sign and a separate subgroup without any pre-defined medical signs of distress are presented in The pre-arrest sign arrhythmia had a higher unadjusted probability of 30-day survival . Pre-arrest signs pulmonary oedema, hypotension and hypoxia had a lower unadjusted probability of 30-day survival (OR < 1), with hypoxia having the lowest probability . The pre-arrest sign seizure was not associated with the probability for 30-day survival, For the pre-arrest sign arrhythmia, sequential adjustment for comorbidities, cardiac arrest characteristics and cardiac arrest treatment attenuated the odds ratio for 30-day survival. Yet, the final adjusted odds ratio still indicated a higher probability for 30-day survival . The association between the pre-arrest signs hypotension and hypoxia and 30-day survival was not affected when adjusting for covariates . After adjusting for comorbidities, pulmonary oedema showed no association with 30-day survival, The presence of a shockable first rhythm was more likely for patients with arrhythmia . After adjusting for comorbidities pulmonary oedema was not associated with shockable first rhythm. Shockable first rhythm was less likely for the pre-arrest signs hypotension and hypoxia in the adjusted models, The primary objective of this study was to investigate the association between medical signs of distress presenting within 60 minutes prior to cardiac arrest and 30-day survival. Patients with arrhythmia as a pre-arrest sign was an independent predictor of 30-day survival, as compared to patients without arrhythmia preceding IHCA. Hypotension and hypoxia prior to cardiac arrest were associated with an almost halved adjusted probability for 30-day survival, compared to patients not having these medical signs. Pulmonary oedema preceding cardiac arrest was associated with a lower probability of 30-day survival, however this association was rendered non-significant after adjusting for comorbidities. The pre-arrest sign seizure did not affect 30-day survival.The results from this study support previous findings that IHCA caused by arrhythmia were associated with increased 30-day survival in patients >70 years.Pulmonary oedema was associated with lower unadjusted probability of 30-day survival, which was rendered non-significant after adjusting for comorbidities. Patients with pulmonary oedema had more comorbidities, previous myocardial infarction, heart failure and diabetes, as compared with patients without pulmonary oedema. A majority of the patients with pulmonary oedema had heart failure. Myocardial infarction and diabetes are often precursor conditions to heart failure.One of the secondary objectives of this study was to investigate the association between pre-arrest signs and shockable first rhythm. The adjusted probability of shockable first rhythm was 77% higher for patients with arrhythmia. Defibrillation at any time was also more frequent, which suggests that patients with the pre-arrest sign arrhythmia, are more prone to present with or convert to a shockable rhythm during CPR. Atrial fibrillation has been found to be an independent risk factor for ventricular fibrillation.Shockable first rhythm was 60–70% less likely for patients with hypotension or hypoxia in the adjusted models. Hypotension and acute respiratory insufficiency have been found to be related to PEA/asystole as the first rhythmAdditionally, the longer time intervals to achieve ROSC seen in the patients with hypotension or hypoxia compared to the patients with arrhythmia is possibly representative of a potential to improve advanced life-support treatment. The present study indicates that pre-arrest signs should be given more attention in understanding the underlying pathophysiology resulting in non-shockable rhythm and poor survival. These pre-arrest signs may be helpful in the development of effective treatments in the form of individualized CPR based on these signs and other factors as well. It is also possible that the best treatment for this patient group is established through improving the “Chain of prevention” within the hospital setting.26.The association between self-reported prodromal symptoms and survival have previously been investigated.An additional strength of this study is that the study population was obtained from a comprehensive national registry based in Sweden. Although this could limit the generalisability of the findings, the independent variables were objective and measurable and independent associations with survival were found. Therefore, the findings from this study could be generalised to cardiac arrests with these pre-arrest signs.A limitation of the current study is the lack of data on the variability within each pre-arrest sign such as the degree of hypoxia or hypotension. Additionally, the pre-arrest sign arrhythmia encompasses many different arrhythmias which have been shown to precede cardiac arrest.This study only investigated five medical signs prior to cardiac arrest. However, there may be other medicals signs that influence survival or even medical signs that present more than 60 minutes prior to the cardiac arrest, which could be important for improving preventive care for cardiac arrest.Due to the nature of the retrospective reporting to the registry prior to 2018, a large proportion of patients were excluded from logistic regression analysis due to missing data regarding pre-arrest signs. This missing data is a result of the presence or absence of the pre-arrest sign not being explicitly reported in the medical journals. However, several hundred observations could still be included in the analysis. Hence, the analysis still achieves an acceptable statistical power.Among signs of medical distress prior to in-hospital cardiac arrest, arrhythmia was associated with increased 30-day survival. Hypotension and hypoxia were associated with reduced survival after IHCA. These findings warrant that future research on survival after cardiac arrest should take pre-arrest signs into account as it impacts the prerequisites for survival.The authors have no conflicts of interest to declare.Meena Thuccani: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Araz Rawshani: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Resources. Kristofer Skoglund: Conceptualization, Methodology, Writing – review & editing, Resources, Visualization. Niklas Bergh: Conceptualization, Methodology, Writing – review & editing, Visualization. Per Nordberg: Conceptualization, Methodology, Writing – review & editing, Visualization. Malin Albert: Conceptualization, Methodology, Writing – review & editing, Visualization. Annika Rosengren: Conceptualization, Methodology, Writing – review & editing, Visualization, Resources, Funding acquisition. Johan Herlitz: Conceptualization, Methodology, Writing – review & editing, Visualization. Christian Rylander: Conceptualization, Methodology, Writing – review & editing, Visualization. Peter Lundgren: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Funding acquisition.


Research exploring telehealth expansion during the COVID-19 pandemic has demonstrated that groups disproportionately impacted by COVID-19 also experience worse access to telehealth. However, this research has been cross-sectional or short in duration; geographically limited; has not accounted for pre-existing access disparities; and has not examined COVID-19 patients. We examined virtual primary care use by race/ethnicity and community social vulnerability among adults diagnosed with COVID-19 in a large, multi-state health system. We also assessed use of in-person primary care to understand whether disparities in virtual access may have been offset by improved in-person access.Using a cohort design, electronic health records, and Centers for Disease Control and Prevention Social Vulnerability Index, we compared changes in virtual and in-person primary care use by race/ethnicity and community social vulnerability in the year before and after COVID-19 diagnosis. Our study population included 11,326 adult patients diagnosed with COVID-19 between March and July 2020. We estimated logistic regression models to examine likelihood of primary care use. In all regression models we computed robust standard errors; in adjusted models we controlled for demographic and health characteristics of patients.In a patient population of primarily Hispanic/Latino and non-Hispanic White individuals, and in which over half lived in socially vulnerable areas, likelihood of virtual primary care use increased from the year before to the year after COVID-19 diagnosis (3.6 to 10.3%); while in-person use remained stable (21.0 to 20.7%). In unadjusted and adjusted regression models, compared with White patients, Hispanic/Latino and other race/ethnicity patients were significantly less likely to use virtual care before and after COVID-19 diagnosis; Hispanic/Latino, Native Hawaiian/Pacific Islander, and other race/ethnicity patients, and patients living in socially vulnerable areas were also significantly less likely to use in-person care during these time periods.Newly expanded virtual primary care has not equitably benefited individuals from racialized groups diagnosed with COVID-19, and virtual access disparities have not been offset by improved in-person access. Health systems should employ evidence-based strategies to equitably provide care, including representative provider networks; targeted, empowering outreach; co-developed culturally and linguistically appropriate tools and technologies; and provision of enabling resources and services. In the United States (U.S.) to date, there have been over 870,000 deaths related to coronavirus disease 2019 (COVID-19) and more than 73 million cases since January 2020 –16.Symptoms of COVID-19 are highly variable in both extent and type, and can affect multiple organ systems –19. As mDuring the COVID-19 pandemic, efforts to slow the spread of COVID-19 and mitigate its adverse consequences have led to a remarkable transformation in U.S. healthcare delivery , 24. As Most early pandemic research has found disparities in access to telehealth for some groups, including groups disproportionately impacted by COVID-19 disease. One study by Chunara, Zhao, Lawrence, et al.  showed tWith adequate access to high-quality primary care, morbidity, adverse clinical outcomes, and negative social consequences  such as those of long-term COVID-19 disease can be reduced –39. PrimUsing a retrospective, observational cohort design, we examined the association between race/ethnicity, community social vulnerability, and use of virtual primary care among adult patients diagnosed with COVID-19 between March and July 2020 at a Providence health system site, comparing virtual primary care use in the year prior to and the year after COVID-19 diagnosis. To explore whether disparities in access to virtual primary care may have been offset by improved access to in-person primary care, we compared concurrent use of in-person primary care in the year before and after COVID-19 diagnosis by race/ethnicity and community social vulnerability among the same patients.n = 46); at the time of this study, the Providence Texas EHR was not fully integrated with the larger Providence EHR and therefore complete data were not available for Texas’ patients. Individuals were also excluded from the study if they had missing community social vulnerability (n = 31) or race/ethnicity (n = 1072) information, resulting in a final study sample comprised of 11,326 individuals.Data for this study came from the Providence health system electronic health record (EHR), which contains information on patient demographic and health characteristics, and healthcare utilization. Providence is a non-profit healthcare system operating across seven states: Alaska, California, Montana, New Mexico, Oregon, Texas, and Washington . IndividOur dependent variables included two binary variables for whether an individual used virtual primary care and in-person primary care, defined as having at least one virtual or in-person primary care visit, respectively. Primary care visits were identified in the EHR as visits that occurred with any provider delivering care in an outpatient department group designated as primary care. Visits were classified as virtual if they took place in a virtual office setting, or in-person if they took place in an in-person outpatient setting. A virtual office visit could include visits in which patients had audio-video or audio-only capability. Asynchronous messaging was not included in our definition of virtual primary care visits. In order to examine longer-range access to primary care (versus COVID-19 onset-related access), we censored visits that took place during the first 30 days after COVID-19 diagnosis when constructing each of our utilization outcomes, as these visits may have been associated with acute COVID-19 disease-related care.Data on study participants’ race and ethnicity was used to create a mutually exclusive race/ethnicity variable comprised of seven categories: non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaskan Native (AI/AN), non-Hispanic Native Hawaiian/Pacific Islander (NH/PI), non-Hispanic other, and Hispanic/Latino. If a study participant was recorded as being Hispanic/Latino and any other race, they were categorized as Hispanic/Latino, otherwise they were categorized based on the most up-to-date race/ethnicity information available in the EHR.Community social vulnerability was defined using the Social Vulnerability Index (SVI) from the CDC, which categorizes an area as socially vulnerable via factors such as poverty, lack of access to transportation, and crowded housing . The SVIThe post-COVID-19 diagnosis period was identified based on a study participant’s first positive COVID-19 diagnosis. From this we created a binary variable that identified whether an observation occurred before or after the COVID-19 diagnosis date.Demographic variables included age group, calculated as the study participant’s age at the date of their COVID-19 diagnosis ; and sex . Health status variables included a proxy variable for COVID-19 severity, which was a variable indicating whether a study participant was diagnosed with COVID-19 in an inpatient healthcare setting ; and five binary variables indicating whether a study participant had chronic conditions including diabetes, hypertension, coronary artery disease, chronic kidney disease, and/or congestive heart failure . These were the five most common chronic conditions among the study sample that were flagged by Providence as potentially leading to worse COVID-19 disease and outcomes, and are relevant to pre-COVID-19 disease primary care needs and utilization.We examined demographic and health characteristics of our study sample overall and by virtual primary care use status by computing cell sizes and percentages for categorical variables. We descriptively examined primary care use outcomes by computing cell sizes and proportions of the study sample who used each type of primary care, and by computing means and standard deviations for each type of primary care during the study period overall, and separately for the year before and after COVID-19 diagnosis.We employed logistic regression and calculated average marginal effects to explore changes in the probabilities of each primary care outcome from the year before to the year after COVID-19 diagnosis by race/ethnicity and community social vulnerability. To do this, we created interaction terms for the post-COVID-19 period and each category of race/ethnicity, and interaction terms for the post-COVID-19 period and each binary SVI theme variable. Following the approach employed by other researchers studying COVID-19 and/or telehealth disparities –49, and Table With respect to the other demographic and health characteristics of the sample, the majority of individuals (81.3%) were under the age of 65, and there was an almost even split between male (47.8%) and female (52.2%) sex individuals. Older individuals and female sex individuals were more likely to use virtual primary care during the study period. About one quarter of individuals (24.0%) were diagnosed with COVID-19 in an inpatient setting, and about one in five individuals (22.4%) had hypertension; a smaller proportion had diabetes (14.8%), coronary artery disease (10.4%), chronic kidney disease (9.2%), and congestive heart failure (4.8%). Individuals who were diagnosed with COVID-19 in an outpatient setting were more likely to use virtual primary care during the study period, as were individuals without chronic health conditions.Table p < 0.05], while individuals identified as non-Hispanic other and Hispanic/Latino race/ethnicity had lower likelihoods of using virtual primary care in the year prior to COVID-19 diagnosis . None of the interaction terms for time period and race/ethnicity were statistically significant, indicating that these differences in virtual primary care use by race/ethnicity persisted in the year following COVID-19 diagnosis. Table p < 0.01), non-Hispanic NH/PI , non-Hispanic other , and Hispanic/Latino  race/ethnicity having lower likelihoods of using in-person primary care compared with non-Hispanic White individuals. Again, none of the interaction terms were significant, indicating that these differences persisted in the year after COVID-19 diagnosis.Similar to the differences identified in virtual care utilization, in the year prior to COVID-19 diagnosis, there were significant differences in the likelihood of use of in-person primary care by race/ethnicity, with individuals identifying as non-Hispanic AI/AN .In ancillary models that adjusted for demographic and health characteristics, nearly all differences in virtual and in-person primary care use by race/ethnicity were similar in magnitude and significance. The only difference that was slightly reduced was the difference in virtual primary care use for non-Hispanic other race/ethnicity individuals . None of the interaction terms for time period and community social vulnerability theme were significant, indicating that these differences persisted in the year after COVID-19 diagnosis. In ancillary models, differences in in-person primary care use by community social vulnerability were similar in magnitude and significance.In our main models that included community social vulnerability, race/ethnicity, and interactions for the post-COVID-19 period, use of virtual primary care was not significantly different by community social vulnerability in either the year before or after COVID-19 diagnosis. However, differences in use of in-person primary care by community social vulnerability were observed. For example, individuals residing in areas categorized as vulnerable based on minority status and language, and housing type and transportation had decreased likelihoods of using in-person primary care in the year before COVID-19 diagnosis  primary care. This is particularly important given additional surges in cases and the emergence of new COVID-19 variants, all of which signal that the pandemic is not likely to end soon and that virtual care will remain an important care modality. Furthermore, there is a growing “care debt” that has the potential to lead to deleterious downstream consequences such as complications from unmanaged health conditions and incapacitation of an already overwhelmed healthcare system . To creaEvidence-based strategies exist for creating more equitable telehealth and primary care infrastructure . First, Next, developing representative provider networks can improve capacity of and access to care, while at the same time improving quality of care for underserved individuals and communities. Racial/ethnic concordance between patients and providers is associated with improved use of preventive services, satisfaction with care, patient-provider communication quality, and patient participation in care and decision-making , 61. In Designing culturally appropriate tools and technology that enable and improve access requires adaptations to systems predominantly designed for White, English-speaking individuals. To that end, health systems can collect and incorporate input on telehealth tools and technologies from racialized groups and those with limited English proficiency , as evidPolicy solutions are also needed to address systemic barriers to care, such as inequitable distribution of healthcare and enabling resources. A recent survey found that limited broadband connectivity and related technology  has created barriers to telehealth during the pandemic . This isOther policy and systems-level solutions that have been shown to improve access to primary care among underserved populations and communities include expanding scope of practice laws for and increasing the use of non-physician clinicians; expanding the supply of non-hospital-based clinics such as Federally Qualified Health Centers (FQHCs); increasing the availability of after-hours primary care services; and removing cost-related barriers to primary care such as cost-sharing .This study has some limitations worth noting. First, our study sample is limited to Providence patients in six mostly Mid−/Western U.S. states, which may limit generalizability to Southern and North−/Eastern states. That said, this study provides data on patients across a large, multi-state geographic area that includes both rural and urban areas, enhancing generalizability compared with existing research on smaller geographic areas and largely urban centers. Next, our sample is comprised of patients who tested positive for COVID-19, yet evidence has demonstrated disparities in COVID-19 testing rates among racialized groups and those with limited English proficiency, even as they experience higher COVID-19 infection rates –76. TherThe pandemic has further illuminated the persistent inequities that lead to poorer access to care and health outcomes among racialized groups and vulnerable communities. Our study adds to the mounting body of evidence that lays bare these inequities. Using data from a large health system across multiple states, we found disparities in utilization of virtual and in-person primary care by both race/ethnicity and community social vulnerability among individuals diagnosed with COVID-19, some of the same groups of people who have been hit hardest by COVID-19 infections, morbidity, economic consequences, and mortality. The importance of primary care, together with widespread telehealth expansion brought about by the COVID-19 pandemic highlight both an urgent need and unprecedented opportunity to address these disparities, but only if solutions are purposefully designed and implemented to address their root causes , 34.


The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care.This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice.CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA  2020 guidelines.Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies.There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine.PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539 Artificial intelligence (AI) was first described in the 1950s as a theory of human intelligence being exhibited by machines, including but not limited to learning, reasoning, and problem-solving -4. AI-asThe number of AI-related articles  in the health care literature has increased dramatically from 6802 articles in 2016 to 21,160 in 2020. However, only a minority of these are prospective clinical studies, and there are few randomized controlled trials (RCTs). Several systematic reviews have been conducted to summarize the performance of recent AI-assisted tools in specific clinical settings, such as AI-assisted adenoma detection during colonoscopy , AI-assiTo date, no systematic review has been restricted to RCTs regarding the clinical performance of AI-assisted tools in real-life practice. As RCTs represent the best clinical evidence to examine the effects of an intervention while controlling for unmeasured confounding factors, a comprehensive search of all RCTs studying AI-assisted tools in clinical practice would provide information regarding areas of opportunity for AI to affect real-world patient care . We condThe systematic review was conducted following the updated PRISMA  2020 guidelines . We compAfter removing duplicates, two study authors (TYTL and MFKC) independently screened the title, abstract, and full text (if available) of each article to determine their eligibility. Unresolved disagreements were resolved by consulting the senior author (JJYS). Discrepancies were resolved by consensus. The complete manuscript was downloaded if the study met the inclusion criteria. We included studies that met the following inclusion criteria: (1) application of AI-assisted tools in clinical practice, which is defined as diagnosis, treatment, and prognostication on medical conditions that are seen and managed in daily clinical practice in hospitals or clinics. This does not include cellular or tissue cultures, animal studies, or experimental conditions such as induced cardiac arrhythmia and metabolic abnormalities. We classified the tool as AI-powered if the expressions, “artificial intelligence,” “AI,” “machine learning,” “deep learning,” “deep neural network,” and “neural network” were used to describe the tool within the articles or other publicly available information resources; (2) patients or health care providers must be involved; (3) study design must be an RCT; and (4) control group must be without AI assistance. The exclusion criteria were as follows: (1) studies without implementation of clinical AI-assisted tools for patient management; (2) studies that were not conducted as original RCTs, for example, secondary analysis of a published RCT; and (3) clinical outcome not clearly defined. Reasons for exclusion were also recorded.After identifying relevant studies, the same two authors (TYTL and MFKC) independently extracted the data from each included study. Study design  and AI-assisted tool characteristics  were documented. If AI development–related data were not available in the included articles, previously published articles of the same AI-assisted tool were reviewed to obtain the relevant information. Study end points (performance metrics used in primary and secondary end points) were listed. Clinically relevant end points were defined as whether the AI-assisted tools led to subsequent clinical interventions focusing on specific end points: (1) further diagnostic workup and investigation of the medical conditions, (2) changes in treatment strategy, (3) requirement of hospitalization, (4) escalation of care to the intensive care unit, and (5) influence on survival and mortality. Two independent researchers (TYTL and MFKC) resolved disagreements through discussion. If there were unresolved disagreements, consultation from senior author (JJYS) was sought.Risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials . We specThe search performed on July 14, 2021, yielded 11,839 articles ; of these, 6823 (57.63%) were screened after removal of duplicates . After screening the titles and abstracts, 6676 articles were excluded, because they did not fulfill the inclusion criteria. A total of 147 full manuscripts were individually assessed, of which 34 (23.1%) met the inclusion criteria. In addition, 4 more articles were identified by examining the references of the listed articles and manual searches . A totalThere were very few RCTs on AI-assisted medicine published until 2017. There was 1 RCT published in 2009, and the remaining 38 were published in the past 5 years  and China . Furthermore, 18% (7/39) of the RCTs were published as conference abstracts only. Of these 39 publications, 16 (41%) were related to gastroenterology, whereas other specialties included anesthesiology , cardiology , endocrinology , psychiatry , neurology , orthopedics , oncology , surgery , ophthalmology , respiratory medicine , family medicine , and emergency medicine . Furthermore, 50% (13/26) of the AI-assisted tools used clinical or biochemical data for analysis . No AI-assisted tool used both biosignal and clinical data combined as source data in the algorithm.Most AI-assisted tools relied on static data  input to build the algorithm instead of dynamic data input . Static data refer to a snapshot of image or data of patients at a specific time point, whereas dynamic data are those captured continuously over a certain period during the study. For example, still images of the intestinal lumen captured during colonoscopy for AI-assisted adenoma detection are static data, whereas hourly captured vital signs and selected available laboratory tests for AI-assisted prediction of severe sepsis are dynamic data .Approximately half of the studies  reported the AI-assisted tools development process. Of these, three AI-assisted tools in 8 studies, namely, GI-Genius, EndoScreener, and CC-Cruiser, were developed using data from multiple centers, whereas others were developed using data from a single center. A total of 35 studies reported the AI developer. Of these, 18 (51%) AI-assisted tools were developed by industry and 17 (49%) were developed by academic institutions.The most common limitations listed by the authors among these studies were single-center study design  and small sample size . This limits the generalizability and statistical power of the AI-assisted tools in different studies. There were 7 studies which were underpowered because of small sample size. Of these, 5 (71%) studies included <100 participants. Another common limitation is the open-label design .Detailed assessment results of the risk of bias using the second version of the Cochrane risk-of-bias tool for randomized trials are reported in clinically relevant end point may be relatively narrow, we believe that the absence of such end points in the RCTs shows a clear deficit in the available evidence.Despite the plethora of claims for the benefits of AI in enhancing clinical outcomes, there is a paucity of robust evidence. In this systematic review, we identified only a handful of RCTs comparing AI-assisted tools with standard-of-care management in various medical conditions. Among these RCTs, two-thirds demonstrated improved primary or secondary end points compared with the standard-of-care management. However, not all of these end points are clinically relevant, that is, leading to a change in the management plan, improving the treatment results, shortening or avoiding hospital admissions, or reducing mortality. Although we acknowledge that our definition of a As expected, most of these studies came from economically advanced, industrialized countries, which constituted two-thirds of the RCTs included in this systematic review. China, as a single nation, accounted for one-third of the RCTs. China’s research in this area is empowered by immense amount of resources invested in AI or machine learning (ML), internet, its vast patient population, and the availability of a nationwide electronic health record for hospitalized patients . The geoMore biosignal-based AI-assisted tools have been studied than clinical data–based tools in RCTs. The most widely used were endoscopic images detecting adenoma during colonoscopy. The adenoma detection algorithm appears to be easier for cross-compatibility because of the distinct difference in appearance between adenoma (or polyp) and normal mucosa . There wClassical prediction models are typically clinical risk scores derived from regression-based statistical models, which could be considered an ML model that has been modified for clinical use. Ideally, RCTs should be designed with a control arm , a “standard-of-care” clinical risk score arm, and a novel AI-assisted tool arm. However, given the expense and effort of a clinical trial, the SPIRIT-AI  extension guidelines clearly state the importance of pre-existing evidence for AI intervention, with evidence that the AI-assisted tool produces better performance compared with the standard of care . AlthougThe findings of this systematic review are by no means to discourage the use of AI in medicine. AI or ML can detect signals in an immense data pool to develop algorithms for clinical decision. Unlike humans, AI or ML can process enormous quantities of data, perform consistently, and constantly improve its performance by learning from new data. However, for AI or ML to be implemented in daily clinical practice, assisting clinicians in making important decisions, proof-of-concept evidence is not sufficient. AI-assisted tools must demonstrate unequivocal improvement in clinically relevant outcomes in properly designed randomized controlled clinical trials in which AI-assisted management is compared with standard-of-care practice. Researchers should not only focus on demonstrating the robustness of the AI algorithm in concept studies but also on translating from code to bedside by conducting RCTs in real-life clinical settings. From our systematic review, automated polyp detection is the most widely implemented AI technology in clinical practice, which sets a good example of the pathway from algorithm development to the implementation of AI technology in real-life clinical practice. Another obstacle to the implementation of AI or ML in daily clinical practice is the regulation of these technologies . To gran


Although Legionnaires’ disease mainly affects the lungs, it can also present with other systemic involvement, including rare cardiac manifestations. Recognised presentations are endocarditis, myocarditis, pericarditis, and pericardial effusion. A 72-year-old British man presented with a six-day history of dry cough and a four-day history of fever during the peak of the COVID-19 pandemic. His electrocardiogram showed Mobitz type II atrio-ventricular block. Although all the cultures were negative, the chest X-ray demonstrated COVID-19 infection-like features. With high clinical suspicions and chest X-ray features, the polymerase chain reaction of the COVID tests was repeated three times and all were negative. He had a positive urinary Legionella antigen, and his bradycardia and heart block improved after treatment with amoxicillin/clavulanic acid, and clarithromycin. As the electrocardiogram showed Mobitz type II, a permanent pacemaker was implanted. The follow-up pacemaker check showed that he still required active pacing. In 2020, 295 cases of Legionnaires’ disease were reported to the National Surveillance Scheme with an average of 400 cases yearly from 2017 to 2019 [Medical history and demographicsA 72-year-old independent Caucasian man presented to the emergency department with a six-day history of dry cough and a four-day history of fever during the peak of the COVID-19 pandemic. His only past medical history was hypertension, for which he was on Lisinopril 5 mg once a day. His previous ECG from a year ago was a normal sinus rhythm. He denied prodromal symptoms such as vomiting and diarrhoea. There were no cardiac symptoms such as presyncope, syncope, chest pain, or palpitation. Moreover, he denied recent travel or COVID-19 contacts.On presentation to the acute medical unit, he was afebrile with a blood pressure of 145/87 and a heart rate of 84/min but was tachypnoeic and in respiratory distress with a respiratory rate of 21/min and hypoxia (oxygen saturation of 94% on 3 L of oxygen). Auscultation of the lungs revealed equal air entry bilaterally with very few fine bibasal crackles. The jugular venous pressure was not elevated and there was no calf tenderness or swelling. Given the COVID-19 pandemic, this became the presumptive diagnosis.InvestigationsInitial laboratory results revealed that he had neutrophil leucocytosis and lymphopenia with raised D-dimer and C-reactive protein Table . ArteriaThe electrocardiogram on admission showed 2:1 atrioventricular block with right bundle branch block (Figure Although the suspicion of pulmonary embolism was raised due to high D-dimer, computed tomography of the pulmonary angiogram excluded it but confirmed upper lobe consolidation on the left lung, suggesting infection. There was a mild right pleural effusion Figure .Community-acquired pneumonia screening and chlamydia testing were negative, with blood and sputum cultures showing no growth. Urinary Legionella antigen returned positive later. Repeated viral studies for COVID-19 infection were negative. In addition, the polymerase chain reaction results for influenza A, B, avian flu, and the immunoglobulin G antibody to pigeons were negative.The echocardiogram showed good left and right ventricular systolic function with an ejection fraction of above 60% and did not show regional wall motion abnormalities. As there will be no benefit from the management plan, cardiac magnetic resonance imaging was not performed.TreatmentThe initial treatment for the possible COVID-19 infection included oral steroids and injection of co-amoxiclav and oral clarithromycin for a possible secondary bacterial chest infection. After receiving the computed tomography pulmonary angiogram report and a negative COVID-19 result, steroids and antibiotics were stopped, and doxycycline was started to treat atypical pneumonia. On day 2 of doxycycline, his urinary Legionella antigen was positive. Therefore, he was treated with oral doxycycline for 14 days according to guidelines. Permanent pacemaker implantation was planned once the infection had settled.Outcome and follow-upA permanent pacemaker was later implanted as an outpatient for Mobitz type II heart block. At the follow-up, he was actively pacing. A follow-up chest X-ray showed that there was an improvement in consolidation changes, although the small right-sided pleural effusion remained unchanged. He has been followed up by the respiratory team for further investigations of his pleural effusion.While several infectious diseases are associated with cardiac conduction abnormalities, there are only two case reports describing Legionella-induced complete heart block and sinoatrial block, respectively . ConductAlthough the aerosolization of contaminated water is the known route of transmission for Legionella, there was no obvious exposure and no relevant travel history. The source of infection in this case is unclear. According to Public Health England, the culture of sputum is the gold standard for the investigation of legionella, but urinary antigen testing produces faster results, leading to lower mortality . Doing aThe prognosis of Legionella-associated cardiac conduction problems remains unknown. According to our case and other related case reports, it is temporary with rapid diagnostics and appropriate therapy. At the subsequent follow-up, our patient had paced 15% in the atrium and 15% in the ventricle. Even though pacemaker insertion was not necessary in other case reports where patients received aggressive supportive care with transcutaneous and transvenous pacing, our patient did require permanent pacemaker implantation, with presumably some underlying conduction abnormality exacerbated by the Legionella infection .In this article, we present the case of a 72-year-old male with symptoms of respiratory infection and Mobitz type II atrio-ventricular block, initially treated for suspected SARS-CoV-2 at the peak of the COVID-19 infection. On further investigations, it was concluded that cardiac complication was due to co-current Legionella infection.We strongly suggest that in cases of new-onset cardiac conduction disturbance in the setting of community-acquired pneumonia, clinicians should consider Legionella infection as a differential diagnosis. Prompt recognition and treatment of Legionella are crucial to minimise secondary complications such as cardiac involvement and prevent the reversible infectious heart block.