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The application of EPSiT in pilonidal sinus disease: an international Delphi consensus study endorsed by the Association of Laparoscopic Surgeons of Great Britain and Ireland (ALSGBI)
No full textBackground:
Endoscopic Pilonidal Sinus Treatment (EPSiT) is a novel, minimally invasive surgical
technique that has shown promise in the treatment of pilonidal sinus disease. Despite
the apparent benefits and call for increased use, widespread uptake has been slow.
This study aims to gather and understand expert international opinions on EPSiT and
develop recommendations for its application in the surgical community.
Methods:
Expert international panellists were identified and recruited to participate. A three round modified Delphi consensus consisting of forty-three questions regarding the
application of EPSiT was posed. A combination of a 5-point Likert scale, binary
‘Yes/No’ scale and Multiple Choice Questions were used. The consensus threshold
was set at 70% agreement. When consensus was not achieved or further insight was
required, statement questions were posed. The study has been performed in
accordance with ACcurate COnsensus Reporting Document (ACCORD) explanation
and elaboration guidelines.
Results:
Twenty experts from six countries participated in all rounds with a 100% response rate.
Our experts agreed on 28 statements including the absence of absolute
contraindications to EPSiT; Administering intravenous induction antibiotics routinely
but not post-operative oral antibiotics; recommending laser epilation; offering re-EPSiT
to the informed patient after 1st and 2nd procedure failures and that EPSiT should be
incorporated into surgical training programmes.
Conclusion:
This is the first study to provide an international expert consensus on the specific
application of EPSiT in primary and recurrent adult and paediatric patients with
pilonidal sinus disease. The findings of this study contribute to the development of
protocols for EPSiT in pilonidal sinus disease management, addressing key areas of
consensus and controversy and promoting procedure uptake
Bias correction of quadratic spectral estimators
No full textThe three cardinal, statistically consistent, families of nonparametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample each can be subject to nonignorable bias. astfalck2024debiasing developed a method that offers significant bias reduction for finite samples for Welch’s estimator, which this article extends to the larger family of quadratic estimators, thus offering similar theory for bias correction of lag-window and multitaper estimators as well as combinations thereof. Importantly, this theory may be used in conjunction with any and all tapers and lag-sequences designed for bias reduction, and so should be seen as an extension to valuable work in these fields, rather than a supplanting methodology. The order of computation is larger than O (n log n) which is typical in spectral analyses, but not insurmountable in practice. Simulation studies support the theory with comparisons across variations of quadratic estimators
CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks
No full textDNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq). Cleavage Under Targets & Tagmentation (CUT&Tag) is a rapidly expanding technique that enables the profiling of such interactions in situ at high sensitivity. However, thorough evaluation and benchmarking against established ChIP-seq datasets are lacking. Here, we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Combining multiple new and published CUT&Tag datasets, there was an average recall of 54% known ENCODE peaks for both histone modifications. We tested peak callers MACS2 and SEACR and identified optimal peak calling parameters. Overall, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments, providing a benchmarking framework for the experimental design and analysis of CUT&Tag studies
USD-YOLO: An enhanced YOLO algorithm for small object detection in unmanned systems perception
No full textIn the perception of unmanned systems, small object detection faces numerous challenges, including small size, low resolution, dense distribution, and occlusion, leading to suboptimal perception performance. To address these issues, we propose a specialized algorithm named Unmanned-system Small-object Detection-You Only Look Once (USD-YOLO). First, we designed an innovative module called the Anchor-Free Precision Enhancer to achieve more accurate bounding box overlap measurements and provide a smarter processing mechanism, thereby improving the localization accuracy of candidate boxes for small and densely distributed objects. Second, we introduced the Spatial and Channel Reconstruction Convolution module to reduce redundancy in spatial and channel features while extracting key features of small objects. Additionally, we designed a novel C2f-Global Attention Mechanism module to expand the receptive field and capture more contextual information, optimizing the detection head’s ability to handle small and low-resolution objects. We conducted extensive experimental comparisons with state-of-the-art models on three mainstream unmanned system datasets and a real unmanned ground vehicle. The experimental results demonstrate that USD-YOLO achieves higher detection precision and faster speed. On the Citypersons dataset, compared with the baseline, USD-YOLO improves mAP50-95, mAP50, and Recall by 8.5%, 5.9%, and 2.3%, respectively. Additionally, on the Flow-Img and DOTA-v1.0 datasets, USD-YOLO improves mAP50-95 by 2.5% and 2.5%, respectively
Association between income, employment status, and asthma outcomes: a systematic review and meta-analysis
No full textBackground: Health inequalities are deeply entrenched in society, and finding ways to reduce these, therefore, represents a major health policy challenge. Focusing on the two highest weighted Index of Multiple Deprivation domains, namely income and employment, we sought to synthesise the evidence on the association between these major determinants of socioeconomic status and asthma outcomes.
Methods: In this systematic review and meta-analysis, we searched key concepts related to employment, income, and asthma outcomes using Medline and Embase for studies published between January 1, 2010 and April 3, 2025. Studies were eligible for inclusion if they were in English and described an association between income and/or employment and asthma outcomes, including exacerbations, hospital admissions and mortality, in people with asthma. Risk Of Bias In Non-randomized Studies - of Exposures (ROBINS-E), Risk of Bias (RoB) and adapted RoB tools were used to assess the risk of bias in the included studies. Using the restricted maximum likelihood method, we meta-analysed the rate of exacerbations and explored heterogeneity between age-related population groups: children (under 18 years) and adults (18 years and older). This study was registered with PROSPERO, CRD42024527300.
Findings: We identified 4153 potentially eligible studies, of which 3141 were screened. 30 studies met the inclusion criteria, with most having a low risk of bias. 19 studies reported income as the exposure and exacerbation as the outcome, of which ten were included in the meta-analysis. People in the lowest income group were more likely to experience an asthma exacerbation than those in the highest income group: OR 1·25; 95% CI 1·13–1·37 overall and when stratified by age: children (1·36 [1·23–1·50]) and adults (1·19 [1·05–1·33]). Only three studies investigated the role of unemployment and were narratively synthesised. While unemployment was associated with increased emergency care visits, its role in predicting exacerbations was less clear.
Interpretation: There is a need for upstream interventions aiming to reduce income inequalities and to investigate their impact on reducing asthma inequalities.
Funding: Health Data Research UK, Inflammation and Immunity Driver Programme
Learning steps: models of intent-driven lower limb prosthesis use
No full textWalking is often perceived as a simple and effortless process. However, it is generated by a complex and finely tuned neuromechanical system. Artificial devices that aim to seamlessly support or replace parts of it must adapt to the changing goals and environments of their users. Virtual environments provide an accessible and low-risk way to accelerate the early-stage development of these technologies. To capture the challenge inherent in this task, these simulations must aim to reproduce the complexities of our movement, our environment, and the dynamic control required assist it.
This thesis approaches this problem from two sides. First, we propose locomotion intent estimation models that regress biosignals to the desired future walking path. We investigate the sensitivity of our intent estimators to common error inducing factors such as electrode shifts, and propose ways to mitigate their effect. Second, we apply physics-based animation methods to create full-body motion corresponding to arbitrary walking path trajectories. We then use control policies optimised with reinforcement learning to trial prosthesis-assisted gait in non-steady-state locomotion settings. Conditioning the prosthesis' control on its user's walking intent is shown to mitigate the need for compensatory movements from the human agent.
The application of our gait synthesis agents to test prototype passive devices is also explored. We create a simulated model of a compliant prosthetic foot, and procedurally adapt our motion synthesis to tackle varying slopes and tripping hazards. We identify and quantify potential benefits provided by the compliant design and contrast it against data from an external experimental study.
In conclusion, we identify a series of challenges and solutions for dynamically simulating and evaluating intent-driven lower limb prosthetic interventions. Our methods contribute to a framework for refining assistive device design, facilitating the transfer of the next-generation assistive devices from the concept stage to real world systems.Open Acces
Hot electrons in nanoplasmonic devices
No full textLocalised surface plasmonic resonance is the oscillation of conduction electrons in metallic nanoparticles when it is illuminated with photons of a certain frequency. Nowadays, there is increasing effort to harness the energy or hot carriers from the metallic nanoparticles under plasmonic resonance. For example, researchers have used the plasmonic resonance effect to build photodetectors, photocatalysis, solar cells, etc. Despite the numerous experimental research, most theoretical studies are based on the classical approach such as solving Maxwell's equation. Little insights into the hot carriers are known as this is a quantum mechanical effect. To model the hot carriers, researchers have to solve Schrodinger's equation, which is computationally expensive. Researchers have attempted to model the hot-carrier processes using simplified wavefunctions such as the spherical-well approach, but this is less accurate and it is only limited to certain geometries of nanoparticles. Some other researchers uses ab-inito approaches to model these nanoparticles such as GW-perturbation method or time-dependent density functional theory calculations, but this can only be applied to very small (≤2000 atoms) nanoparticles or bulk-like nanoparticles. In this research project, we used a semi-classical approach, we used the tight-binding method to compute the wavefunction of the nanoparticles and used the quasi-static method to compute the perturbation induced by the photon. We used an efficient kernel polynomial method that scales linearly with the number of atoms. We managed to simulate the hot carrier distribution of nanoparticles with from 249 atoms(~0.5 nm) up to 1,000,000 atoms(~30 nm). Based on the finite-element quasi-static method, we managed to calculate the classical potential for arbitrary geometry. This allows us to calculate the hot-carrier generation rate of more complicated structures, such as Au@Pd core-shell nanoparticle surrounding a big Au nanoparticle and metallic Janus nanoparticles with a neck.Open Acces
Micromechanical model for hysteresis in fluid-saturated porous rocks under hydrostatic loading
No full textThis research aims to show that frictional microcracks cause the dependence of rocks' elastic properties on both the perturbation amplitude and the loading history. These relationships are essential for estimating wave velocities and, thus, for analysing laboratory experiments on fluid-saturated rocks under varying stress levels. The first step towards achieving this objective is to propose a microstructural prototype in which hydrostatic remote loading induces local deviatoric stress. This stress is essential for triggering the slip of microcracks once they are closed. The prototype's geometry is based on the classical spherical assemblage of Hashin (J. Appl. Mech., 1962) and includes a fluid-filled central pore. Surrounding this pore is a spherical shell composed of a homogenised medium that is pervasively microcracked, according to the non-interactive model proposed by Kachanov (Mech. Maters., 1982). The properties of the homogeneous material surrounding the central pore are determined by solving the boundary-value problem with the confining pressure applied to the outer boundary of the shell. The second step is to enhance this microstructural model by incorporating a distribution of crack aspect ratios within the spherical shell. We demonstrate how oscillations in loading, with decreasing amplitudes, enable the evaluation of dynamic elastic properties, ultimately leading to the determination of wave velocities. The third step complements these findings by providing experimentalists with a numerical tool to help them improve their methods for evaluating the arrival time of shear waves in tri-axial laboratory experiments. To achieve this, we propose a coupled numerical approach that captures the transformation of the voltage applied to a piezoelectric actuator into an S-wave. This approach also accounts for the propagation of the wave through the testing machine and specimen, as well as the generation of voltage at the receiver, where a complex wave pattern is detected.Open Acces
Examining the impact of maternal experiences of domestic violence on the mental health of their adolescent children in India
No full textBackground
Domestic violence (DV) is experienced by one in three women in India and is linked to poor mental health outcomes. We hypothesize that maternal experiences of DV can have negative impacts on the mental health of their children. Previous studies have demonstrated this link in Western countries, however culturally specific manifestations of DV and mental health disorders and socio-cultural differences in parent-child relationships and home environments necessitate deeper understanding of the impacts of maternal experiences of DV on children in the Indian context.
Methods
This study presents a secondary analysis of data collected from a seven-center study in urban and rural India examining mental health disorders among adolescents aged 12–17 years and psychological, physical, and sexual abuse affecting their mothers. The Indian Family Violence and Control Scale (IFVCS) was used to examine experiences of DV among mothers and the Mini International Neuropsychiatric Interview–Kid (MINI-Kid) was used to examine mental health outcomes among adolescents. Multivariate analyses examined the associations between maternal DV and adolescent mental disorders.
Results
Data from 2,784 adolescent-mother pairs were analyzed. In bivariate analyses, maternal experiences of physical, psychological, and sexual abuse were significantly associated with adolescent common mental disorders including anxiety and depression (p < 0.05). After adjusting for adolescent gender, site, and education status in the multivariate analysis, physical, sexual, and any DV were significantly associated with adolescent anxiety disorders and common mental disorders. Physical abuse was significantly associated with adolescent depressive disorders.
Conclusions
These results suggest that exposure to maternal DV significantly impacts adolescent mental health in India and underscore the need to develop trauma-informed school programs and enhance DV prevention for women in India
Bayesian inference of transmission chains: opportunities and challenges for characterising SARS-CoV-2 transmission dynamics
No full textAssessing the transmissibility of a pathogen primarily involves quantifying the amount, speed, and patterns of transmission. During the COVID-19 pandemic, these dynamics varied by time and location, driven in part by the emergence of variants of concern (VOCs). Successive waves of infection prompted the continuous reassessment of transmission dynamics and public health policies. However, large-scale epidemic models faced challenges in rapidly evaluating changes in transmissibility and capturing transmission heterogeneities in high-risk settings, such as in hospitals and care homes.
The unprecedented availability of epidemic surveillance data enabled statistical inference of transmission chains, providing opportunities to reveal detailed insights into local transmission, but also to characterise broader epidemiological properties of SARS-CoV-2. Nevertheless, limited SARS-CoV-2 genetic diversity hindered the resolution of transmission trees. This thesis investigates the extent to which outbreak reconstruction tools can enhance our understanding of SARS-CoV-2 transmission dynamics and how they can be integrated into routine surveillance to improve public health response.
In Chapter 2, the reconstruction of household transmissions at the national level revealed that 20% of transmission events had negative serial intervals across VOCs. In Chapter 3, we developed a method to quantify group-level transmission assortativity and established guidelines on the minimum data required for transmission chains to elucidate transmission patterns. In Chapter 4, we assessed the contribution of healthcare workers (HCWs) and patients in nosocomial transmission by integrating epidemiological, genetic, and contact data under simulated real-time conditions. We found early on that HCWs were more likely to transmit to other colleagues than to patients.
Integrating outbreak reconstruction tools into surveillance systems, even in the absence of genetic data, holds significant potential to support real-time modelling and public health response. Leveraging diverse data streams, at various scales, to address different needs, our work demonstrated that Bayesian inference of transmission chains provides valuable insights into SARS-CoV-2 dynamics.Open Acces