Stand-alone Approach Research Articles

Vector Field Heterogeneity as a Novel Omnipolar Mapping Metric for Functional Substrate Characterisation in Scar-related Ventricular Tachycardias

BackgroundVector Field Heterogeneity (VFH) is a novel omnipolar metric to quantify local propagation heterogeneities that may identify functionally critical sites for ablation in scar-related VT. ObjectiveAssess the diagnostic value of VFH to identify abnormal propagation patterns during ventricular substrate mapping and compare VFH in VT isthmus sites (IS), low-voltage bystander (LVA) and normal voltage areas (NVA). MethodsSubstrate maps acquired with a 16-pole grid catheter in patients with scar-related VT were segmented into sites corresponding to IS, LVA and NVA (defined as omnipolar voltages >&<1.5mV) based on corresponding VT activation maps. For each 4×4 electrode-array acquisition, omnipolar-derived vector maps of the directions of electrical propagation were computed offline and a VFH value per clique ranging from 0 (perfect planar wave) to 1 (maximal disorganisation) derived. Results16 patients were studied (56.3% ischemic) evaluating 9 endocardial/7 epicardial substrate maps. VFH metric at IS was 0.57±0.26, LVA 0.52±0.34, NVA 0.16±0.24. VFH at IS was higher (more disorganised) than at LVA (p<0.001). VFH in NVA was significantly lower (i.e. more homogenous) compared to IS and LVA (p<0.001, respectively). Within the isthmus, highest heterogeneity was recorded at entry (0.61±0.24), lowest at exit sites (0.44±0.27). ConclusionVFH mapping identified a significant increase in electrical heterogeneity at IS compared to bystanders with highest heterogeneity at the entrance. Absolute differences were small and substantial overlap between sites were recorded precluding its use as a stand-alone mapping approach. VFH may be integrated in existing mapping strategies as complementary information to support identification of ablation targets.

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.

Targeted hematopoietic stem cell depletion through SCF-blockade

BackgroundHematopoietic stem cell transplantation (HSCT) is a curative treatment for many diverse blood and immune diseases. However, HSCT regimens currently commonly utilize genotoxic chemotherapy and/or total body irradiation (TBI) conditioning which causes significant morbidity and mortality through inducing broad tissue damage triggering infections, graft vs. host disease, infertility, and secondary cancers. We previously demonstrated that targeted monoclonal antibody (mAb)-based HSC depletion with anti(α)-CD117 mAbs could be an effective alternative conditioning approach for HSCT without toxicity in severe combined immunodeficiency (SCID) mouse models, which has prompted parallel clinical αCD117 mAbs to be developed and tested as conditioning agents in clinical trials starting with treatment of patients with SCID. Subsequent efforts have built upon this work to develop various combination approaches, though none are optimal and how any of these mAbs fully function is unknown.MethodsTo improve efficacy of mAb-based conditioning as a stand-alone conditioning approach for all HSCT settings, it is critical to understand the mechanistic action of αCD117 mAbs on HSCs. Here, we compare the antagonistic properties of αCD117 mAb clones including ACK2, 2B8, and 3C11 as well as ACK2 fragments in vitro and in vivo in both SCID and wildtype (WT) mouse models. Further, to augment efficacy, combination regimens were also explored.ResultsWe confirm that only ACK2 inhibits SCF binding fully and prevents HSC proliferation in vitro. Further, we verify that this corresponds to HSC depletion in vivo and donor engraftment post HSCT in SCID mice. We also show that SCF-blocking αCD117 mAb fragment derivatives retain similar HSC depletion capacity with enhanced engraftment post HSCT in SCID settings, but only full αCD117 mAb ACK2 in combination with αCD47 mAb enables enhanced donor HSC engraftment in WT settings, highlighting that the Fc region is not required for single-agent efficacy in SCID settings but is required in immunocompetent settings. This combination was the only non-genotoxic conditioning approach that enabled robust donor engraftment post HSCT in WT mice.ConclusionThese findings shed new insights into the mechanism of αCD117 mAb-mediated HSC depletion. Further, they highlight multiple approaches for efficacy in SCID settings and optimal combinations for WT settings. This work is likely to aid in the development of clinical non-genotoxic HSCT conditioning approaches that could benefit millions of people world-wide.

Stand-alone transarterial flow diversion for treatment of carotid cavernous fistulas

ObjectiveCarotid-cavernous fistulas (CCFs) are rare arteriovenous communications allowing blood to flow from the carotid artery into the cavernous sinus. Although currently coil and/or liquid embolization remain the preferred treatment methods for CCFs, flow diverters (FD) stents represent a viable alternative to traditional embolization techniques. This study explores both the technical aspects and rationale behind using FD stents as a stand-alone treatment approach for CCFs. MethodsThe study includes records of 7 patients with CCFs treated at two comprehensive stroke centers from 2019 to 2023. Patients treated with FD stenting in conjunction with coil and/or liquid embolization were excluded from the study. ResultsFive patients were diagnosed with direct CCFs and 2 patients with indirect CCFs. Six patients were treated with the Surpass Evolve FD stent and 1 patient with the Pipeline FD stent. DSA follow-up was performed for an average duration of 14.4 months following FD placement. Complete fistula obliteration with no residual shunting was observed in all patients. Furthermore, all patients experienced a complete resolution of symptoms following treatment. ConclusionsThe authors’ experiences suggest the efficacy and safety of FD stenting as a stand-alone treatment option for CCFs. Compared to embolization, FD stents can better preserve the parent vessel and promote healing with less associated mass effect. Despite being a retrospective self-assessment with a relatively small sample size, to the authors’ knowledge, this study represents the largest individual case series of patients with CCF treated with stand-alone FD stenting.

An efficient data fusion model based on Bayesian model averaging for robust water quality prediction using deep learning strategies

Accurate monitoring of dissolved oxygen (DO) levels is critical for stakeholders to effectively safeguard water resources and aquatic ecosystem health. This research presents an innovative data fusion framework based on Bayesian model averaging (BMA) by the combination of several neuroscience models (deep learning methodologies) including multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX). BMA has the capacity to greatly enhance the results attained by standalone approaches. In this study, two feature selection methods such as mutual information (MI) and recursive feature elimination (RFE) applied to select effective predictors and investigate the importance of each input parameters. The techniques were evaluated based on four different metrics and, finally, to demonstrate the usefulness and effectiveness of the newly implemented strategy, it was applied proficiently at the USGS stations, 01427510 and 02336152 within the USA. The findings from analyzing data based on two stations confirmed that the suggested approach (BMA) outperformed other methods such as MLPNN, RNN, CNN, LSTM, and SARIMAX when it came to predicting the levels of DO on a daily basis. In terms of RMSE, MAE and R2, BMA yielded 0.272 mg/L, 0.216 mg/L, and 0.975 using MI technique and 0.320 mg/L, 0.261 mg/L, and 0.965 using RFE method at 01427510 USGS station, respectively. Similarly, based on RMSE, MAE and R2, BMA produced DO prediction by RMSE = 0.352 mg/L, 0.264 mg/L, and 0.968 by MI approach and RMSE = 0.378 mg/L, 0.282 mg/L, and 0.963 via RFE process at 02336152 USGS station, respectively. After analyzing different combinations of input that obtained from feature selection paradigms, it was observed that the variables with the greatest impact on daily dissolved oxygen levels are the dissolved oxygen levels in the previous time period and the water temperature. On the other hand, the pH and turbidity have minimal influence on the daily DO. Finally, the results of this study confirmed that the BMA tool can be efficiently applied to predict DO concentration based on deep learning model outputs.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

Detection and classification of surface defects on hot-rolled steel using vision transformers

This study proposes a vision transformer to detect visual defects on steel surfaces. The proposed approach utilizes an open-source image dataset to classify steel surface conditions into six fault categories namely, crazing, inclusion, rolled in, pitted surface, scratches and patches. The defect images are first subject to resizing and then fed into a vision transformer subject to different hyperparameter configurations to determine the most optimal setting to render highest classification performance. The performance of the model is evaluated for different hyperparameter configurations, and the most optimal configuration is examined using the associated confusion matrices. It was observed that the proposed model presents a high overall accuracy of 96.39 % for detection and classification of steel surface faults. The study presents a descriptive insight into the vision transformer architecture and in addition, compares the performance of the current model with the results of other approaches suggested for application in literature. Vision transformers can serve as standalone approaches and suitable alternatives to the widely used convolution neural networks (CNNs) by actuating complex defect detection and classification tasks in real-time, enabling efficient and robust condition monitoring of a wide range of defects.

Integrating AHP and GTMA for Enhanced Multi-Criteria Decision Making in Logistics Provider Selection

The selection of logistics providers is a critical task for organizations aiming to enhance their supply chain performance and maintain a competitive edge. This paper proposes an integrated approach that combines the Analytic Hierarchy Process (AHP) and Graph-Theoretic Matrix Approach (GTMA) for effective multi-criteria decision-making (MCDM) in the selection of logistics providers. AHP is widely used for evaluating criteria and alternatives based on pairwise comparisons, while GTMA enhances decision-making by incorporating complex interdependencies among criteria. The integrated AHPGTMA approach is demonstrated through a case study involving the selection of a third-party logistics provider (3PLP). The proposed method improves the decision-making process by simplifying complex relationships among criteria while ensuring an efficient and robust selection. This integrated model presents a more comprehensive and reliable framework for logistics provider selection, overcoming limitations observed in stand-alone AHP and ANP approaches. Future research may focus on the application of fuzzy logic to further enhance decision accuracy in uncertain environments.

Pipeline embolization device as a standalone curative approach for recurrent sigmoid sinus DAVF.

Dural arteriovenous fistulas (dAVFs) can occur as complications after surgical procedures, especially following the resection of meningiomas near the dural sinus. This case report presents a 74-year-old male who developed a recurrent sigmoid dAVF following meningioma resection. Initially treated with transvenous embolization and middle meningeal artery embolization, the dAVF recurred with worsening clinical symptoms. Conventional treatment options, including sinus sacrifice and transarterial embolization, were unsuitable due to the critical role of the patient's dominant right sigmoid sinus in cerebral venous drainage. Consequently, a reconstructive approach was employed using a pipeline embolization device (PED) construct. The PED successfully occluded the dAVF while preserving the function of the sigmoid sinus. A follow-up angiogram confirmed stable occlusion and normalization of intracranial venous drainage. This case underscores the potential of flow diversion as a viable treatment option for dAVFs, particularly in scenarios where preserving venous sinus function is paramount.

Hybrid operational modal analysis of an operative two-bladed offshore wind turbine

As with any strategic structure, vibration-based structural health monitoring techniques are often used to ensure the structurally safe operation of offshore wind turbines. Among such techniques, Operational Modal Analysis (OMA) methods allow the identification of modal properties, such as natural frequencies, mode shapes and damping, which variation might be caused by damage or operational/environmental factors. This paper investigates the application of OMA techniques on a two-bladed offshore wind turbine, which poses multiple challenges: fundamental OMA assumptions about the applied loads are violated by environmental and operational loads; the closely spaced modes of an offshore wind turbine are hard to identify; and an operative two-bladed offshore wind turbine is a time-variant system. Within this study, three OMA procedures to overcome some of the preceding challenges are discussed: (1) a standard frequency domain decomposition method; (2) a proposed enhanced transmissibility-based approach with a post-processing technique based on the Kurtosis index; and (3) a proposed refined hybrid OMA procedure that combines a transmissibility-based approach, the dedicated post-processing technique based on the Kurtosis index, and the frequency domain decomposition method. A numerical model representative of an operative two-bladed offshore wind turbine is used to compare the three procedures. Based on the comparison, the hybrid method is proven to be a promising new OMA-based procedure that outperforms the stand-alone transmissibility-based approach and the frequency domain decomposition method in identifying the modal properties of a two-bladed offshore wind turbine.

Exercise Training for Chronic Pain: Available Evidence, Current Recommendations, and Potential Mechanisms.

Chronic pain conditions pose a significant global burden of disability, with epidemiological data indicating a rising incidence. Exercise training is commonly recommended as a standalone or complementary approach for managing various chronic pain conditions like low back pain, osteoarthritis, rheumatoid arthritis, fibromyalgia syndrome, and neuropathic pain. Regardless of the specific condition or underlying cause (e.g., autoimmune disease, chronic inflammation), exercise training consistently leads to moderate to large reductions in pain. Moreover, exercise yields numerous benefits beyond pain alleviation, including small-to-moderate improvements in disability, quality of life, and physical function. Despite its efficacy, there is a lack of comprehensive research delineating the optimal intensity, duration, and type of exercise for maximal benefits; however, evidence suggests that sustained engagement in regular exercise or physical activity is necessary to achieve and maintain reductions in both clinical pain intensity ratings and the level that pain interferes with activities of daily living. Additionally, the precise mechanisms through which exercise mitigates pain remain poorly understood and likely vary based on the pathophysiological mechanisms underlying each condition.

The Role of Information Governance in Mitigating Financial Crime Risks in Stablecoin Transactions

This study investigates the role of information governance in mitigating financial crime risks in stablecoin transactions. Using a variety of analytical techniques, including simple linear regression, sentiment analysis with NLP tools, logistic regression, and machine learning models, the research evaluates the impact of information governance on innovation, user trust, financial crimes, and the effectiveness of combined compliance measures. The findings indicate that strict information governance regulations reduce innovation but enhance market stability and user trust. Robust governance correlates strongly with increased user adoption, while higher anonymity features in stablecoins are linked to a higher incidence of financial crimes. Integrating KYC/AML compliance with transaction monitoring significantly improves the detection and prevention of financial crimes compared to standalone approaches. These insights provide valuable guidance for policymakers, regulatory authorities, and financial institutions to develop strategies that balance innovation with enhanced security and compliance in the stablecoin market.

Radiologic Outcomes after Operative Management of Traumatic Spine Fractures: Stand-Alone Posterior Stabilization versus Combined Anteroposterior Approach.

Previous research emphasizes correcting deformities resulting from spine fractures by restoring sagittal alignment and vertebral height. This study aims to compare radiologic outcomes, including sagittal index (SI) and loss of vertebral body height (LVBH), between stand-alone posterior stabilization (group I) and the posteroanterior/combined approach (group II) in the operative management of traumatic thoracic or lumbar spine fractures. In this retrospective single-center study, all patients with traumatic spine fractures (T1-L5) undergoing surgical stabilization between January 1, 2015, and May 31, 2021, were included. Two spine surgeons independently assessed imaging, recording the SI and LVBH values at baseline, after each surgical intervention, and during follow-up (at least 3 months posttreatment). The mean SI and LVBH values between the assessing surgeons were utilized. Linear mixed-effects regression models, adjusted to baseline values, compared the SI and the LVBH values between the two groups. In all, 71 patients (42 men), with the median age of 38 years (interquartile range [IQR]: 28-54) and median follow-up of 4 months (IQR: 3-17), were included. Thirty-two patients were in group I and 39 patients were in group II. Forty fractures included the thoracolumbar junction (T12 or L1), 15 affected the thoracic spine, and 14 the lumbar spine. The regression model revealed superior sagittal alignment in group II, with an adjusted mean difference for SI of -4.24 (95% confidence interval [CI]: -7.13 to -1.36; p = 0.004), and enhanced restoration of vertebral body height with an adjusted mean difference for LVBH of 0.11 in the combined approach (95% CI: 0.02-0.20; p = 0.02). Nine postoperative complications occurred in the entire cohort (4 in group I and 5 in group II). Combined posteroanterior stabilization for spine fractures improves deformities by enhancing sagittal alignment and increasing vertebral body height, with acceptable morbidity compared with the stand-alone posterior approach.

Novel multi-level optimization of district heating systems: Managing spatial scale and equipment portfolio design

The design of district heating systems often involves the consideration of the appropriate system scope, referred to as spatial scale. In order to achieve climate-neutral heating in neighborhoods, it is necessary to address spatial scale in the early planning stages. This study presents a novel optimization method aimed at sustainable dimensioning of the district heating system by optimizing the system type, scope, and equipment simultaneously. The method quantifies the spatial scale of targeted district heating system and assists in the decision between decentralized and centralized systems by using clustering algorithms and the genetic algorithm. The proposed approach was rigorously tested in a city-scale case study against centralized pipe network and standalone approaches. Our findings reveal that the proposed methodology outperforms traditional threshold methods and fixed-scope optimization through a three-group comparative study, culminating in a globally optimal solution for district heating systems.. Finally, the study offers insights into the intricate interplay between energy systems and district heating network scopes, underscoring the pivotal role of spatial scale considerations in the design of district heating systems toward climate-neutral paradigms.

Impact of Water, Sanitation, and Hygiene (WaSH) interventions on diarrhoeal infections among children in the tribal regions of Palghar: a quasi-experimental study

ABSTRACT This study tested the effect of a 4-week Water, Sanitation, and Hygiene (WaSH) intervention on diarrhoeal infections in children, and knowledge, awareness, practices (KAP) in the six tribal villages of Palghar. We performed a quasi-experimental study in N = 180 households among mothers with children under-five registered in the anganwadi centers. Households received either WaSH kits (control arm n = 90) or kits combined with education (intervention arm n = 90). We estimated the effect of this 4-week intervention by comparing the groups post-intervention for change in KAP and diarrhoea infection. The average overall KAP score of the intervention group increased significantly from 8.54 ± 1.65 to 11.89 ± 1.60 (p &amp;lt; 0.001), between pre-intervention (t0) and 4 weeks after intervention (t2). In the control group, the change in average overall KAP score was not statistically significant (8.39 ± 1.72 to 8.74 ± 1.76 (p = 0.764)), between the t0 and t2 assessments. Diarrhoeal infection was reduced by 6% in the intervention compared to 2% in the control group. These findings support that a combination of short- and long-term strategies was more effective in reducing diarrhoea among tribal children than standalone approaches.

A grid-based methodology for the assessment of time-dependent building damage at large scale

Leakage into underground constructions can result in time-dependent settlements in soft clays. In urban areas with spatial variability in geologic stratification, groundwater conditions and soil compressibility, differential settlements may occur, causing damage to buildings. Current methods for damage assessment that rely on 1D formulations for settlement prediction are not representative for drawdown-induced settlements in heterogeneous environments. Thus, in this paper, we propose a stand-alone approach to integrating spatially distributed, non-Gaussian settlement data into early-stage building damage assessments at a district scale. Deformations computed using a 2D coupled hydro-mechanical finite element model with an advanced constitutive model were then employed to get the time-dependent settlements computed as a 3D grid (along x- and y-directions) over a large area. Building damage was then calculated from these green-field simulations with typically used damage parameters for each building-specific settlement profile and comparing these with damage criteria. The approach was applied to 215 buildings in central Gothenburg, Sweden by simulating scenarios of 10 kPa and 40 kPa pore pressure drawdown in the lower (confined) aquifer. Several scenarios were studied, and the correlation between damage parameters and damage criteria was assessed. Finally, a sensitivity study on grid resolution was performed, as well as a validation against observed damage data. The proposed methodology offers an effective way for early-stage damage assessments at a large area for non-Gaussian settlements so that further investigations and mitigation measures can be targeted to the buildings and locations at the highest risk for damage.

Dynamic resource allocation for energy-efficient downlink NOMA systems in 5G networks

Non-Orthogonal Multiple Access (NOMA) is a promising energy-efficient technology designed to satisfy the demands of future networks by efficiently sharing radio resources. In NOMA, the same radio resource is simultaneously assigned to two users at different power levels based on the NOMA-power domain. Resource allocation in NOMA presents a non-convex challenge, characterized as a non-deterministic polynomial (NP-hard) problem. This involves user and channel assignment and power allocation, making it an extraordinarily complex task to achieve an optimal solution. In this work, Simulated Annealing (SA) is proposed as an optimization technique for resource allocation in an energy-efficient downlink NOMA system. This resource allocation scheme addresses user and channel assignment, as well as power allocation, using SA as an efficient standalone approach to maximize energy efficiency in NOMA. SA is utilized to execute the assignment of users to channels, distribute the necessary power for each channel, and determine the power ratio among users sharing the same channel. The results obtained demonstrate a significant improvement in energy efficiency, outperforming the existing numerical methods by 22 %. The proposed SA scheme not only achieves a close optimal solution but also in less computational time, offering sufficient reliability in terms of energy efficiency enhancement when compared to the existing numerical method.

Targeting immunogenic cell stress and death for cancer therapy.

Immunogenic cell death (ICD), which results from insufficient cellular adaptation to specific stressors, occupies a central position in the development of novel anticancer treatments. Several therapeutic strategies to elicit ICD - either as standalone approaches or as means to convert immunologically cold tumours that are insensitive to immunotherapy into hot and immunotherapy-sensitive lesions - are being actively pursued. However, the development of ICD-inducing treatments is hindered by various obstacles. Some of these relate to the intrinsic complexity of cancer cell biology, whereas others arise from the use of conventional therapeutic strategies that were developed according to immune-agnostic principles. Moreover, current discovery platforms for the development of novel ICD inducers suffer from limitations that must be addressed to improve bench-to-bedside translational efforts. An improved appreciation of the conceptual difference between key factors that discriminate distinct forms of cell death will assist the design of clinically viable ICD inducers.

Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification

The accurate identification of pigments is of principal relevance in the field of cultural heritage conservation and restoration practices. In this preliminary study, a first attempt to set up a procedure for accurate red pigment identification, based on the assessment of the correlations existing between visible reflectance spectrometry (vis-RS) and Raman data, is presented. The proposed approach involved the realization of a library consisting of data acquired on a set of 35 pure red historical pigments supplied by ©Kremer Pigmente. In particular, vis-RS data, collected through a Konica Minolta CM-2600d spectrophotometer, were registered, together with the position of the Extrema Points (E.P.s) encompassing both the maximum and minimum points of the first derivative of the % spectral reflectance factor (SRF%) curves. Portable Raman spectroscopy measurements were collected by a B&amp;W Tek Inc. portable Raman spectrometer equipped with a 785 nm laser. For each tested pigment, the positions and relative intensities of the characteristic Raman peaks were considered. The library was then tested for the characterization of the red/reddish painted areas of the medieval wall painting located within The Norman Castle of Aci Castello (Catania, Italy), and was shown to be essential for the unambiguous identification of the pigment used. It is worth noting that this study represents the first novel attempt to establish a reliable and efficient methodology for pigment identification, offering promising prospects in reducing uncertainties and ambiguities arising from the application of a single stand-alone approach.

Applying a process-based therapy approach to compassion focused therapy: A synergetic alliance

Compassion Focused Therapy (CFT) targets underlying shame and self-criticism to shift one's style of self-relating and capacity to engage with a soothing-affiliate mode. CFT has a strong evidence-base and has been adapted for diverse presenting problems and populations. CFT can serve as a stand-alone treatment approach, or an effective addition to other evidence-based approaches. This theoretical article proposes that CFT is a complementary and synergetic match with a Process-Based Therapy (PBT). This article outlines the theoretical compatibility of the two approaches before presenting an applied case study to illustrate a CFT case formulation using a PBT framework. An illustrative treatment plan is also proposed using the Extended Evolutionary Meta-Model (EEMM). Directions for further research to understand the exact mechanisms of action of CFT and its potential synergetic effects on other treatment approaches are proposed.