Psychiatric Treatment Conducted via Telemedicine Versus In-Person Modality in Posttraumatic Stress Disorder, Mood Disorders, and Anxiety Disorders: Systematic Review and Meta-Analysis.

BACKGROUND: Telemedicine has played a vital role in providing psychiatric treatment to patients during the rapid transition of services during the COVID-19 pandemic. Furthermore, the use of telemedicine is expected to expand within the psychiatric field. The efficacy of telemedicine is well described in scientific literature. However, there is a need for a comprehensive quantitative review that analyzes and considers the different clinical outcomes and psychiatric diagnoses. OBJECTIVE: This paper aimed to assess whether individual psychiatric outpatient treatment for posttraumatic stress disorder, mood disorders, and anxiety disorders in adults using telemedicine is equivalent to in-person treatment. METHODS: A systematic search of randomized controlled trials was conducted using recognized databases for this review. Overall, 4 outcomes were assessed: treatment efficacy, levels of patient satisfaction, working alliance, and attrition rate. The inverse-variance method was used to summarize the effect size for each outcome. RESULTS: A total of 7414 records were identified, and 20 trials were included in the systematic review and meta-analysis. The trials included posttraumatic stress disorder (9 trials), depressive disorder (6 trials), a mix of different disorders (4 trials), and general anxiety disorder (1 trial). Overall, the analyses yielded evidence that telemedicine is comparable with in-person treatment regarding treatment efficacy (standardized mean difference -0.01, 95% CI -0.12 to 0.09; P=.84; I2=19%, 17 trials, n=1814), patient satisfaction mean difference (-0.66, 95% CI -1.60 to 0.28; P=.17; I2=44%, 6 trials, n=591), and attrition rates (risk ratio 1.07, 95% CI 0.94-1.21; P=.32; I2=0%, 20 trials, n=2804). The results also indicated that the working alliance between telemedicine and in-person modalities was comparable, but the heterogeneity was substantial to considerable (mean difference 0.95, 95% CI -0.47 to 2.38; P=.19; I2=75%, 6 trials, n=539). CONCLUSIONS: This meta-analysis provided new knowledge on individual telemedicine interventions that were considered equivalent to in-person treatment regarding efficacy, patient satisfaction, working alliance, and attrition rates across diagnoses. The certainty of the evidence regarding efficacy was rated as moderate. Furthermore, high-quality randomized controlled trials are needed to strengthen the evidence base for treatment provided via telemedicine in psychiatry, particularly for personality disorders and a range of anxiety disorders where there is a lack of studies. Individual patient data meta-analysis is suggested for future studies to personalize telemedicine. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021256357; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256357.

Knowledge and attitude toward biological warfare among health-related students: A cross-sectional questionnaire-based survey.

Purpose: Many types of research have been published on the history of biological warfare, the agents used, and the medical implications. However, no studies measure how people are aware of the magnitude of these health problems and international threats. The present study aimed to produce and make background about biological warfare information for health college students to be used as a basis for future studies or research and prepare the hospitals' bases for similar disasters. Methods: This observational, cross-sectional, descriptive study was conducted among undergraduate students (N = 626) enrolled in health-related colleges at Jazan University, Saudi Arabia. A preliminary survey of 30 participants was then undertaken to improve the questionnaire's understanding and validity. The questionnaire encompassed three primary sections, including (1) sociodemographic characteristics, (2) knowledge, and (3) awareness. Sociodemographic characteristics consisted of age, gender, college type, academic level, and specialty. All data were gathered using an online self-reported questionnaire using Google Forms and participants were recruited using a random sampling strategy. Results: The total participants were 626 students; 514 were females, whereas 112 were males. Knowledge and attitude indices were 3.8650 ± 0.48 and 4.06 ± 0.51 (maximum is 5). The indices showed variable statistical differences among sociodemographic factors. With adjusted and crude odds ratios of 0.53 and 0.54, attitude score is the sole significant (P = 0.05) predictor of knowledge as analyzed using logistic regression. Conclusion: The results of the present study are the first of their kind in the region and can be used to shape public awareness among specialists and decision-makers, especially in light of the recent pandemic.

Soft ordered double quantitative approximations based three-way decisions and their applications.

The classical theory of rough set was established by Pawlak, which mainly focusses on the approximation of sets characterized by a single equivalence relation over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multigranulation rough sets approach can better deal with the problems, where data might be spread over various locations. In this article, we present the idea of soft preference and soft dominance relation for the development of soft dominance rough set in an incomplete information system. Subsequently, several important structural properties and results of the proposed model are carefully analyzed. After employing soft dominance based rough set approach to it for any times, we can only get six different sets at most in an incomplete information system. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Based on soft dominance relation, we introduce logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximations in an incomplete information. Meanwhile, to measure the uncertainty of soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximation and some of their interesting properties are examined. Thereafter, a novel multi attribute with multi decision making problem approach based on logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough sets approach are developed to solve the selection of medicine to treat the coronavirus disease (COVID-19). The basic principle and the detailed steps of the decision making model (algorithms) are presented in detail. To demonstrate the applicability and potentiality of the proposed model, we present a practical example of a medical diagnosis is given to validate the practicality of the technique.

Psychiatric treatment conducted via telemedicine versus in-person consultations in mood, anxiety and personality disorders: a protocol for a systematic review and meta-analysis.

INTRODUCTION: Major advancements in technology have led to considerations how telemedicine (TM) and other technology platforms can be meaningfully integrated in treatment for psychiatric disorders. The COVID-19 pandemic has placed a further focus on use of TM in psychiatry. Despite the widespread use of TM, little is known about its effect compared with traditional in-person (IP) consultation. The objective of this systematic review is to examine if individual psychiatric outpatient interventions for adults using TM are comparable to IP in terms of (1) psychopathology outcomes, (2) levels of patient satisfaction, (3) working alliance and (4) dropout from treatment. METHODS AND ANALYSIS: This review will only include randomised controlled trials for adult participants with mood disorders, anxiety or personality disorders. The primary outcome is psychopathology, and secondary outcomes include patient satisfaction, treatment alliance and dropout rate. Systematic searches were conducted in MEDLINE, APA PsycINFO, Embase, Web of Science and CINAHL. The inverse-variance method will be used to conduct the meta-analysis. Effect sizes will be calculated as standardised mean difference (Hedges' g) for the primary outcome, mean difference for patient satisfaction and working alliance, and risk ratio for the dropout rate. Effect sizes will be supplemented with 95% CI. We will calculate the I² statistic to quantify heterogeneity and Chi-square statistic (χ²) to test for heterogeneity for the primary outcome. Potential clinical and methodological heterogeneity moderators will be assessed in subgroup and sensitivity analysis. The risk of bias will be assessed by Cochrane Risk of Bias Tool V.2, and confidence in cumulative evidence will be assessed by Grading of Recommendations Assessment, Development and Evaluation. ETHICS AND DISSEMINATION: No ethical approval is required for this systematic review protocol. Data sets will be deposited in the Zenodo repository. The findings of this study will be published in a peer-review scientific journal. PROSPERO REGISTRATION NUMBER: CRD42021256357.

COVID-19 pneumonia level detection using deep learning algorithm and transfer learning.

The first COVID-19 confirmed case was reported in Wuhan, China, and spread across the globe with an unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, developing smart, fast, and efficient detection techniques is significant. To this end, we have developed an Artificial Intelligence engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT scan images of the confirmed COVID-19 patient using Morphological approaches. The second phase classifies the pneumonia level of the confirmed COVID-19 patient. We use a modified Convolution Neural Network (CNN) and k-Nearest Neighbor; we also compared the results of both models to the other classification algorithms to precisely classify lung inflammation. The experiments show that the CNN model can provide testing accuracy up to 95.65% compared with exiting classification techniques. The proposed system in this work can be applied efficiently to CT scan and X-ray image datasets. Also, in this work, the Transfer Learning technique has been used to train the pre-trained modified CNN model on a smaller dataset than the original dataset; the modified CNN achieved 92.80% of testing accuracy for detecting pneumonia on chest X-ray images for the relatively extensive dataset.

Framework for the analysis of renewable energy grid policies in the context of COVID-19.

COVID-19 is a severe global pandemic that has caught the whole world unprepared. In the absence of a clear timeline for this pandemic to end, it is need of the hour to investigate the effect of this pandemic on both previous and anticipated investments. Global economic unrest has hindered the ramping deployment of Renewable energy projects. The most quick actions that may be taken to mitigate the effects and to up-rise the investment portfolio policies are a very critical tool in hands of government for a very immediate effect have also been made without keeping the context of COVID-19 into account. New variants of diff rent nature are being discovered and every now and then new lock downs are happening. In this context different policies have to be evaluated under the pandemic scenario. A case study of a large scale renewable energy project for a higher education institute in Pakistan is being used to measure the difference during COVID and pre COVID times. This paper provides a framework to investigate the impact of COVID on renewable energy system projects under current net-metering, net-billing and self-consumption policies. A recent investment in a photovoltaic system is assessed based on previously projected financial benefits versus the pandemic effected ones. This research concludes that investing in photovoltaic systems are still a viable option even in an extreme pandemic situation with less than 0.5 years increase in payback period, and the government can still provide a stimulus for investing in green energy by implementing net-metering policies on a larger scale.

The tryptophan catabolite or kynurenine pathway in COVID-19 and critical COVID-19: a systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is accompanied by activated immune-inflammatory pathways and oxidative stress, which both induce indoleamine-2,3-dioxygenase (IDO), a key enzyme of the tryptophan (TRP) catabolite (TRYCAT) pathway. The aim of this study was to systematically review and meta-analyze the status of the TRYCAT pathway, including the levels of TRP and kynurenine (KYN) and the activity of IDO, as measured by the ratio of KYN/TRP. METHODS: This systematic review searched PubMed, Google Scholar, and Web of Sciences and included 14 articles that compared TRP and tryptophan catabolites (TRYCATs) in COVID-19 patients versus non-COVID-19 controls, as well as severe/critical versus mild/moderate COVID-19. The analysis was done on a total of 1269 people, including 794 COVID-19 patients and 475 controls. RESULTS: The results show a significant (p < 0.0001) increase in the KYN/TRP ratio (standardized mean difference, SMD = 1.099, 95% confidence interval, CI: 0.714; 1.484) and KYN (SMD = 1.123, 95% CI: 0.730; 1.516) and significantly lower TRP (SMD = - 1.002, 95%CI: - 1.738; - 0.266) in COVID-19 versus controls. The KYN/TRP ratio (SMD = 0.945, 95%CI: 0.629; 1.262) and KYN (SMD = 0.806, 95%CI: 0.462; 1.149) were also significantly (p < 0.0001) higher and TRP lower (SMD = - 0.909, 95% CI: - 1.569; - 0.249) in severe/critical versus mild/moderate COVID-19. No significant difference was detected in kynurenic acid (KA) and the KA/KYN ratio between COVID-19 patients and controls. CONCLUSIONS: Our results indicate increased activity of the IDO enzyme in COVID-19 and severe/critical patients. The TRYCAT pathway is implicated in the pathophysiology and progression of COVID-19 and may signal a worsening outcome of the disease.

Vaccination coverage among older adults: a population-based study in India.

Objective: To estimate the prevalence and explore the predictors of vaccine uptake among older adults in India. Methods: We used data from the national Longitudinal Ageing Study in India, a national household survey conducted during 2017-2018. Based on interviewees' self-reports, we calculated population-weighted estimates of the uptake of influenza, pneumococcal, typhoid and hepatitis B vaccines among 64 714 Indian adults aged 45 years or older. We performed multivariable binary logistic regression analysis to examine the sociodemographic and health-related predictors of uptake of the vaccinations. Findings: The coverage of each of the studied vaccinations was less than 2%. The estimated percentages of respondents reporting ever being vaccinated were 1.5% (95% confidence interval, CI: 1.4-1.6) for influenza, 0.6% (95% CI: 0.6-0.7) for pneumococcal disease, 1.9% (95% CI: 1.8-2.0) for typhoid and 1.9% (95% CI: 1.8-2.0) for hepatitis B. Vaccine uptake was higher among respondents with cardiovascular disease, diabetes or lung disease than those without any of these conditions. Uptake of influenza vaccine was higher among those with lung disease, while hepatitis B vaccine uptake was higher among those with cardiovascular disease or diabetes. Male sex, urban residence, wealthier household, more years of schooling, existing medical conditions and sedentary behaviours were significant predictors of vaccine uptake. Conclusion: Targeted policies and programmes are needed for improving the low vaccination coverage among older adults in India, especially among those with chronic diseases. Further research could examine vaccine access, vaccine hesitancy, and vaccine-related information and communication channels to older adults and their health-care providers.

Preparedness of Public Hospitals for the Coronavirus (COVID-19) Pandemic in Lahore District, Pakistan.

Background Pakistan reported more than a million cases during the coronavirus disease 2019 (COVID-19) pandemic, shuffling the already resource-constrained health system that is known for its high vulnerability and lack of adaption. Objective To find out the level of preparedness of public hospitals for the novel COVID-19 pandemic in Lahore district. Methods A descriptive cross-sectional study was conducted from April to July 2021 among all 18 public hospitals under Specialized Health Care and Medical Education (SHC&ME) in Lahore by administering World Health Organization (WHO) and SHC&ME modified and pre-tested interviewer based and observation checklist. The level of preparedness was assessed for 11 domains, and each domain was scored as a dichotomous variable (Yes and No). Hospital preparedness was labeled as 'acceptable,' 'insufficient,' and 'unacceptable.' Descriptive statistics were run by using SPSS version 26 (IBM Corp., Armonk, NY), and data are presented in the form of tables and bar graphs. Results Out of 18 hospitals, only three (17%) had an acceptable level of preparedness for COVID-19 (>70%). An unacceptable level of preparedness (<35%) was seen in one hospital (5%). Fourteen hospitals (78%) were insufficiently prepared (35-70%). Conclusion The study highlights the suboptimal preparedness in 83% of the public hospitals with a consistent pattern of deficiencies in surge capacity, logistics and resource management, essential services, including diagnostics, infection prevention, and control.

Addressing Behavioral Health of the Diverse Older Adults in the Various Health Care Setting during COVID pandemia

During COVID-19 pandemia being in a “golden age” group, having chronic health conditions make people more susceptible to the virus. The need to socially distance creates the sense of isolation, affects older adults routines, mass transportation, and some “non-essential” social services. Those factors in addition to the uncertainty and fear COVID-19 creates, and the potential for older adults to be more vulnerable to the virus and higher mortality rates among older adults may exacerbate depression and anxiety for which effective and safe treatment interventions are required. Virtual partial hospitalization program offers affordable and convenient opportunity to address mental health needs of older adults. There were a number of challenges with transitioning to and utilizing a telehealth platform for psychiatric evaluation in the long term care setting during the COVID 19 pandemic. First, staffing of facilities was stretched during the pandemic, making facilitation of Tele visits difficult. Without staff present through the entire visit to sort out technical difficulties, repeat questions and instructions for patients with hearing difficulties, and help patients complete questionnaires, evaluation was significantly limited. Additionally, the restrictions in place to mitigate viral transmission to residents and staff played unique roles in the psychiatric population. Isolation, lack of intellectual stimulation though group activities/dining, and halted family visits contributed to depression, anxiety, and agitation. Compounded with limited access to staff for therapy and medical management, we certainly observed a decline in motivation and functioning in our patients. In sum, while telehealth was successfully used in many settings during the COVID 19 pandemic, unique challenges in the geriatric psychiatry population residing in long term care facilities made implementation difficult. Lack of in-person visits and staffing shortages may have also contributed to escalation of psychiatric symptoms. Offering tele-psychiatry to diverse VA older adult patient population appears to be a valuable option although especially in the rural areas may present some challenges including irregularities of the internet connection, limited equipment availability as well as limited proficiency of the patient's utilization of the various telehealth platforms. However with appropriate training and equipment supplies those challenges can be resolved.

The tryptophan catabolite or kynurenine pathway in COVID-19 and critical COVID-19: a systematic review and meta-analysis (preprint)

Background: Coronavirus disease 2019 (COVID-19) is accompanied by activated immune-inflammatory pathways and oxidative stress, which both may induce indoleamine-2,3-dioxygenase (IDO), a key enzyme of the tryptophan (TRP) catabolite (TRYCAT) pathway. The aim of the current study was to systematically review and meta-analyze the TRYCAT pathway status including levels of TRP and kynurenine (KYN) and IDO activity, as assessed using the KYN/TRP ratio. Methods: This systematic review was performed in December 2021 and searched data in PubMed, Google Scholar, and Web of sciences. In our meta-analysis we included 14 articles which examine TRP and TRYCATs in COVID-19 patients versus non-COVID-19 controls, and severe/critical versus mild/moderate COVID-19. Overall, the analysis was performed on 1269 individuals, namely 794 COVID-19 patients and 475 controls. Results: The results show a significant (p <0.0001) increase in the KYN/TRP ratio (standardized mean difference, SMD=1.099, 95% confidence interval, CI: 0.714; 1.484) and KYN (SMD= 1.123, 95% CI: 0.730;1.516) and significantly lower TRP ((SMD= -1.002, 95%CI: -1.738; -0.266) in COVID-19 versus controls. The KYN/TRP ratio (SMD= 0.945, 95%CI: 0.629; 1.262) and KYN (SMD= 0.806, 95%CI: 0.462; 1.149) were also significantly (p <0.001) higher and TRP lower (SMD= -0.909, 95% CI: -1.569; -0.249) in severe/critical versus mild/moderate COVID-19. No significant difference was detected in the kynurenic acid (KA)/KYN ratio and KA between COVID-19 patients and controls. Conclusions: Our results indicate increased activity of the IDO enzyme in COVID-19 and in severe/critical patients. The TRYCAT pathway is probably implicated in the pathophysiology and progression of COVID-19 and may signal a worse outcome of the disease.

The tryptophan catabolite or kynurenine pathway in COVID-19 and critical COVID-19: a systematic review and meta-analysis (preprint)

Coronavirus disease 2019 (COVID-19) is accompanied by activated immune-inflammatory pathways and oxidative stress, which both may induce indoleamine-2,3- dioxygenase (IDO), a key enzyme of the tryptophan (TRP) catabolite (TRYCAT) pathway. The aim of the current study was to systematically review and meta-analyze the TRYCAT pathway status including levels of TRP and kynurenine (KYN) and IDO activity, as assessed using the KYN/TRP ratio. This systematic review was performed in December 2021 and searched data in PubMed, Google Scholar, and Web of sciences. In our meta-analysis we included 14 articles which examine TRP and TRYCATs in COVID-19 patients versus non-COVID-19 controls, and severe/critical versus mild/moderate COVID-19. Overall, the analysis was performed on 1269 individuals, namely 794 COVID-19 patients and 475 controls. The results show a significant (p <0.0001) increase in the KYN/TRP ratio (standardized mean difference, SMD=1.099, 95% confidence interval, CI: 0.714; 1.484) and KYN (SMD= 1.123, 95% CI: 0.730;1.516) and significantly lower TRP ((SMD= - 1.002, 95%CI: -1.738; -0.266) in COVID-19 versus controls. The KYN/TRP ratio (SMD= 0.945, 95%CI: 0.629; 1.262) and KYN (SMD= 0.806, 95%CI: 0.462; 1.149) were also significantly (p <0.001) higher and TRP lower (SMD= -0.909, 95% CI: -1.569; -0.249) in severe/critical versus mild/moderate COVID-19. No significant difference was detected in the kynurenic acid (KA)/KYN ratio and KA between COVID-19 patients and controls. Our results indicate increased activity of the IDO enzyme in COVID-19 and in severe/critical patients. The TRYCAT pathway is probably implicated in the pathophysiology and progression of COVID-19 and may signal a worse outcome of the disease. One-sentence summary The current meta-analysis study revealed a significant increase in peripheral blood IDO activity and kynurenine levels and a significant reduction in tryptophan in COVID-19 versus controls and in severe/critical COVID-19 versus mild/moderate COVID-19.

Metabolic Syndrome: From Molecular Mechanisms to Novel Therapies.

The metabolic syndrome (MetS) consists of a cluster of metabolic abnormalities including central obesity, insulin resistance, glucose intolerance, hypertension, and atherogenic dyslipidemia [...].

Chronic disease management in heart failure: focus on telemedicine and remote monitoring.

In the context of the COVID-19 pandemic, many barriers to telemedicine disappeared. Virtual visits and telemonitoring strategies became routine. Evidence is accumulating regarding the safety and efficacy of virtual visits to replace in-person visits. A structured approach to virtual encounters is recommended. Telemonitoring includes patient reported remote vital sign monitoring, information from wearable devices, cardiac implantable electronic devices and invasive remote hemodynamic monitoring. The intensity of the monitoring should match the risk profile of the patient. Attention to cultural and educational barriers is important to prevent disparities in telehealth implementation.

A comprehensive study of upward fuzzy preference relation based fuzzy rough set models: Properties and applications in treatment of coronavirus disease

Abstract In this paper, we first introduce a new type of rough sets called α-upward fuzzified preference rodownward fuzzy preferenceugh sets using upward fuzy preference relation. Thereafter on the basis of α-upward fuzzified preference rough sets, we propose approximate precision, rough degree, approximate quality and their mutual relationships. Furthermore, we presented the idea of new types of fuzzy upward ?-coverings, fuzzy upward ?-neighborhoods and fuzzy upward complement ?-neighborhoods and some relavent properties are discussed. Hereby, we formulate a new type of upward lower and upward upper approximations by applying an upward ?-neighborhoods. After employing the upward ?-neighborhoods based upward rough set approach to it any times, we can only get the six different sets at most. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Subsequently, we presented the idea to combine the fuzzy implicator and t-norm to introduce multigranulation ( ? , T )-fuzzy upward rough set applying fuzzy upward ?-covering and some relative properties are discussed. Finally we presented a new technique for the selection of medicine for treatment of coronavirus disease (COVID-19) using multigranulation ( ? , T )-fuzzy upward rough sets.