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Repetitive transcranial magnetic stimulation (rTMS) has been proven to be efficacious in the treatment of Major Depressive Disorder (MDD). We previously examined the effectiveness of rTMS for MDD in an applied clinical setting, AwakeningsKC Clinical Neuroscience Institute (CNI) and found high remission rates for patients diagnosed with MDD following rTMS treatment. An unexpected relationship with body composition and rTMS unit was discovered. This sub-study extends the previous investigation through a focused analysis of the effects of body composition on response to rTMS in the treatment of MDD. We utilized data collected from a retrospective review of medical records for patients diagnosed with MDD undergoing rTMS therapy at AwakeningsKC CNI. Patient Health Questionnaire 9 (PHQ-9) scores, time to remission status and body mass index (BMI) at baseline were considered while referencing two different rTMS instruments (MagVenture; NeuroStar). We found 23 (9%) of 247 participants met criteria for obese status (BMI?30) with an average baseline PHQ-9 score of 22±4, classified as “severe depression”. Obesity status was differentially impacted by the rTMS instrument used for treatment. Patients with obesity showed a shorter time to remission (mean 2.7±0.27 vs. mean 3.4±0.3 weeks) and proportionately greater remission rate (100% vs. 71%) when treated using the MagVenture relative to the NeuroStar instrument. Clinical response to rTMS therapy for MDD appears to be guided by individual factors including body composition and rTMS parameters such as the unit used for treatment. Further study of these influences could aid in the optimization of clinical response to rTMS.

We previously examined the efficacy of rTMS for major depressive disorder in an applied clinical practice. Clinical response was related to severity of depression as well as the rTMS instrument utilized suggesting a relationship to instrument or magnetic field parameters and individual factors. The effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder was further evaluated using Log-Rank statistics for time to remission outcomes. A follow-up retrospective medical records study was carried out on patients with major depressive disorder undergoing rTMS therapy at AwakeningsKC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. Cox Proportional Hazard with Log-Rank statistics were applied and the time course to clinical remission was evaluated over a 6-week period with respect to age, gender, and depression severity. Clinical response was observed referencing two different rTMS instruments (MagVenture; NeuroStar). Time to remission studies of 247 case reports (N=98 males; N=149 females) showed consistently greater clinically defined remission rates after 6 weeks of rTMS treatment for patients using the MagVenture vs NeuroStar instrument. Patients previously admitted for inpatient psychiatric hospitalization exhibited higher response rates when treated with the MagVenture rTMS unit. Stepwise Cox Proportional Hazards Regression final model of time to remission included rTMS unit, inpatient psychiatric hospitalization and obese body habitus. Response to rTMS in applied clinical practice is related to severity of psychiatric illness and may require consideration of magnetic field parameters of the rTMS unit with respect to individual factors such as sex or body composition.

Repetitive transcranial magnetic stimulation (rTMS) is a neurostimulatory technique used to modulate orbital frontal corticostriatal (OFC) activity and clinical symptomatology for psychiatric disorders involving OFC dysfunction. We examined the effectiveness of rTMS in the treatment of major depressive disorder in an applied clinical setting (Awakening KC CNI) to assess efficacy and optimize rTMS parameters within clinical practice. A retrospective review of medical records was carried out on patients with major depressive disorder undergoing rTMS therapy at Awakenings KC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. A detailed de-identified data set of clinical outcomes was compiled. Patient Health Questionnaire 9 (PHQ-9) total score, clinical remission rate and week achieved were evaluated over 6 weeks of treatment to assess clinical response referencing two different rTMS instruments (MagVenture; NeuroStar). Our survey included 247 participants from males (N=98) and females (N=149) with average baseline PHQ-9 scores of 21.7±4, classified as severe depression. Clinically rated remission rates of 72% were achieved in 3.1±1.0 weeks and associated with prior history of psychiatric hospitalization, suicide attempts and substance use disorder. Average baseline PHQ-9 scores decreased significantly over time with proportionately greater remission rates achieved for patients treated using the MagVenture over NeuroStar instrument. rTMS in applied clinical practice is efficacious over a wide range of settings and patients. Clinical response was related to severity of depression symptoms (e.g., prior hospitalization; suicide attempts) validating efficacy in critically ill groups. Clinical response may be impacted by rTMS instrument, magnetic field parameters or individual factors.

This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)) and the construction industry’s gradual transition into a circular economy (CE) system to foster the adoption of circular economy in the construction industry.

A critical and thematic analysis conducted on 115 scientific papers reveals a noticeable growth in adopting digital technologies to leverage a CE system. Moreover, a conceptual framework is developed to show the interrelationship between different I4.0 technologies to foster the implantation of CE in the construction industry.

Most of the existing bodies of research provide conceptual solutions rather than developing workable applications and the future of smart cities. Moreover, the coalescence of different technologies is highly recommended to enable tracking of building assets’ and components’ (e.g. fixtures and fittings and structural components) performance, which enables users to optimize the salvage value of components reusing or recycling them just in time and extending assets’ operating lifetime. Finally, circular supply chain management must be adopted for both new and existing buildings to realise the industry's CE ambitions. Hence, further applied research is required to foster CE adoption for existing cities and infrastructure that connects them.

This paper investigates the interrelationships between most emerging digital technologies and circular economy and concludes with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI into the operation of assets to direct future practical research applications