Estimating the Effect of Genital vs Extragenital Warts on Immunocompetent Indian Adult Patients; Quality of Life: A Comparative Cross-Sectional Study.

Introduction: Extragenital warts, often known as EGWs, affect between 7% and 10% of the population. Despite the plethora of research on the impact of genital warts (GWs) on "Quality Of Life", EGWs have received little attention. The purpose of this study was to conduct a cross-sectional investigation with the objective of contrasting the effects of GWs and EGWs on the health-related quality of life and other characteristics. Participants and Procedures: A cross-sectional clinical study was piloted at a tertiary care center. Participants in the study included two groups of healthy adults, each group consisting of 100 adult subjects. Those diagnosed with EGWs were included in group A, while patients diagnosed with GWs made up group B. The "Dermatology Life Quality Index" questionnaire was used to evaluate various parameters. Observations were compared for significance. Results: The majority of the subjects in both the groups were observed to have less than 10 warts. The Dermatology Life Quality Index score for the EGWs had an average of 8.66 ± 2.31 score; GWs had an average of 5.12 ± 3.25. This mean variance was statistically significant. The level of the dissatisfaction was highly significantly different among the groups and the subjects being more in the EGW group dissatisfied. Conclusion: The findings of this investigation indicate that EGWs have a significant and detrimental effect on the Quality Of Life. Medical experts must teach people how to prevent the disease's spread and recurrence due to its persistence. They must also consider the psychological and societal repercussions of the disease while discussing therapy choices.

Assessment of Right Ventricular Function by Strain Imaging in Patients Presenting with Acute Myocardial Infarction: An Original Research.

Objective: This study sought to determine the relationship between right ventricular (RV) function and clinical variables and prognosis in individuals with acute myocardial infarction (AMI) utilizing strain imaging. Materials and Methods: A prospective observational research involving 150 patients who had been admitted with AMI was carried out. Utilizing two-dimensional speckle-tracking strain imaging, RV function was assessed. Age, sex, risk factors, and comorbidities were recorded as clinical parameters. A 12-month follow-up was conducted to assess major adverse cardiovascular events (MACE). Results: 65% of the study's participants were men, with a mean age of 58.2 years. When compared to a healthy control group, individuals with AMI had significantly lower RV longitudinal strain (RVLS) (P 0.001). RVLS and left ventricular ejection fraction had a statistically significant connection (r = 0.642, P 0.001). Patients with compromised RVLS had a greater rate of MACE over the follow-up period compared to those with maintained RV function (P = 0.014). Conclusion: In conclusion, strain imaging offers useful information for evaluating RV function in patients with AMI. Reduced left ventricular performance and a higher likelihood of unfavorable clinical outcomes are linked to impaired RVLS. Utilizing strain imaging to detect RV dysfunction early can help direct treatment plans and enhance patient outcomes.

Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence.

BACKGROUND: Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients. OBJECTIVES: To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19. METHODOLOGY: A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithm was utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out. RESULT: A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification. CONCLUSION: The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 µg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

Unveiling a Shield of Hope: A Novel Multiepitope-Based Immunogen for Cross-Serotype Cellular Defense against Dengue Virus.

Dengue virus (DENV) infection continues to be a public health challenge, lacking a specific cure. Vaccination remains the primary strategy against dengue; however, existing live-attenuated vaccines display variable efficacy across four serotypes, influenced by host serostatus and age, and predominantly inducing humoral responses. To address this limitation, this study investigates a multiepitope-based immunogen designed to induce robust cellular immunity across all DENV serotypes. The chimeric immunogen integrates H-2d specific MHC-I binding T-cell epitopes derived from conserved domains within the DENV envelope protein. Immuno-informatics analyses supported its stability, non-allergenic nature, and strong MHC-I binding affinity as an antigen. To assess the immunogenicity of the multiepitope, it was expressed in murine bone-marrow-derived dendritic cells (BMDCs) that were used to prime mice. In this experimental model, simultaneous exposure to T-cell epitopes from all four DENV serotypes initiated distinct IFNγ-CD8 T-cell responses for different serotypes. These results supported the potential of the multiepitope construct as a vaccine candidate. While the optimization of the immunogen design remains a continuous pursuit, this proof-of-concept study provides a starting point for evaluating its protective efficacy against dengue infection in vivo. Moreover, our results support the development of a multiepitope vaccine that could trigger a pan-serotype anti-dengue CD8 response.

Natural Pigments-Based Two-Component White Light Emitting Systems.

In this paper, a new class of two component white light emitting systems viz, JaB (java plum + beetroot) {I}, and CaB (carrot + beetroot) {II} were developed through resonance energy transfer (RET) phenomenon by using a fruit (java plum) and two vegetable (carrot and beetroot) extracts. In these white light emitting systems, java plum and carrot are the donors while beetroot is the acceptor. The primary fluorescent pigments present in the natural extracts (i.e., anthocyanin in java plum, ß-carotene in carrot, and betanin in beetroot) were responsible for the white light emission. The CIE (Commission Internationale d'Eclairage) coordinates for I and II were {0.32, 0.34} and {0.33, 0.33}, respectively, in solution phase. Interestingly, the white light emission (WLE) was also achieved in agar-agar gel medium. In gel medium, the CIE values were {0.31, 0.34} and {0.33, 0.32} for I and II, respectively. The donor-acceptor distance (r) for I and II were found to be 0.5 and 0.4 nm, respectively. Furthermore, the rate of energy transfer was also quantified with the values of 2.78 × 109 s-1 for JaB (I) and 1.02 × 108 s-1 for CaB (II) systems. The mechanistic investigation of the two white light systems was further supported by DFT studies.

Mandating indoor air quality for public buildings.

If some countries lead by example, standards may increasingly become normalized.

The Labyrinthine Journey: Unveiling Obstacles in addressing Mental Health Challenges in Prisons and its Confluence with Medico-Legal and Pharmacological Perspectives.

This paper delves into the intricate realm of mental health issues within prisons including other correctional facilities, the intersectionality with legal and medical aspects, and the potential of pharmacology as a viable treatment modality. The prevalence and diverse array of mental disorders among incarcerated individuals are thoroughly examined, underscoring the imperative for all-encompassing interventions. The legal structure, hurdles encountered in delivering mental healthcare, and the indispensability of interdisciplinary cooperation are scrutinized. Furthermore, the effectiveness and moral implications of pharmaceutical interventions in correctional environments are deliberated upon. Conclusive suggestions are put forth to enhance mental healthcare provisions in prisons. The research paper endeavors to penetrate the labyrinthine complexities of mental health predicaments within correctional institutions, with a specific emphasis on the convergence of medico-legal facets and the plausible impact of pharmacological interventions. The study strives to elucidate the intricate nature of mental health challenges among incarcerated populations, considering the intricate interplay of socio-cultural, environmental, and psychological factors that contribute to their pervasiveness. By delving into these interconnected dimensions, the research aims to unlock prospective remedies capable of efficaciously meeting the mental health requisites of incarcerated individuals.

Labyrinthine and Neuroscientific Impact of Schizophrenia in the Criminal Justice System.

Schizophrenia, a complex neuropsychiatric condition, manifests with severe neurobiological and psychosocial symptoms, including psychosis, cognitive dysfunction, and social withdrawal. Neuroscience links these symptoms to synaptic malfunctions and neurotransmitter dysregulation, leading to a profound disconnection from reality. The disorder significantly affects cognitive, affective, and behavioral functions, causing considerable neuropsychological distress and functional impairments. The interplay of schizophrenia with the criminal justice system is complex, often exacerbating psychiatric stigma and introducing challenging neuroethical dilemmas. From neuroscientific perspective, schizophrenia symptoms are classified into 'positive' (hyperfunctioning or distortion of normal mental processes) and 'negative' (reduction or loss of mental functions). Each category presents distinct medico-legal challenges. Studies, including those from the Clinical Antipsychotic Trials of Intervention Effectiveness, highlight the importance of identifying neurobiological and psychosocial factors that increase the risk of criminal justice involvement, stressing the necessity of addressing concurrent disorders like substance use disorders. This convergence underscores the need for a delicate balance between therapeutic interventions and legal responsibility, advocating for policy reforms and neuroscience-based research initiatives. Such efforts are crucial for improving the management of schizophrenia within the criminal justice system, focusing on both the medical and societal aspects of the disorder.

Tapered chiral nanoparticles as broad-spectrum thermally stable antivirals for SARS-CoV-2 variants.

The incessant mutations of viruses, variable immune responses, and likely emergence of new viral threats necessitate multiple approaches to novel antiviral therapeutics. Furthermore, the new antiviral agents should have broad-spectrum activity and be environmentally stable. Here, we show that biocompatible tapered CuS nanoparticles (NPs) efficiently agglutinate coronaviruses with binding affinity dependent on the chirality of surface ligands and particle shape. L-penicillamine-stabilized NPs with left-handed curved apexes display half-maximal inhibitory concentrations (IC50) as low as 0.66 pM (1.4 ng/mL) and 0.57 pM (1.2 ng/mL) for pseudo-type SARS-CoV-2 viruses and wild-type Wuhan-1 SARS-CoV-2 viruses, respectively, which are about 1,100 times lower than those for antibodies (0.73 nM). Benefiting from strong NPs-protein interactions, the same particles are also effective against other strains of coronaviruses, such as HCoV-HKU1, HCoV-OC43, HCoV-NL63, and SARS-CoV-2 Omicron variants with IC50 values below 10 pM (21.8 ng/mL). Considering rapid response to outbreaks, exposure to elevated temperatures causes no change in the antiviral activity of NPs while antibodies are completely deactivated. Testing in mice indicates that the chirality-optimized NPs can serve as thermally stable analogs of antiviral biologics complementing the current spectrum of treatments.

Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs.

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

A sucrose non-fermenting-1-related protein kinase 1 gene from wheat, TaSnRK1α regulates starch biosynthesis by modulating AGPase activity.

Major portion of wheat grain consist of carbohydrate, mainly starch. The proportion of amylose and amylopectin in starch greatly influence the end product quality. Advancement in understanding starch biosynthesis pathway and modulating key genes has enabled the genetic modification of crops resulting in enhanced starch quality. However, the regulation of starch biosynthesis genes still remains unexplored. So, to expand the limited knowledge, here, we characterized a Ser/Thr kinase, SnRK1α in wheat and determined its role in regulating starch biosynthesis. SnRK1 is an evolutionary conserved protein kinase and share homology to yeast SNF1. Yeast complementation assay suggests TaSnRK1α restores growth defect and promotes glycogen accumulation. Domain analysis and complementation assay with truncated domain proteins suggest the importance of ATP-binding and UBA domain in TaSnRK1α activity. Sub-cellular localization identified nuclear and cytoplasmic localization of TaSnRK1α in tobacco leaves. Further, heterologous over-expression (O/E) of TaSnRK1α in Arabidopsis not only led to increase in starch content but also enlarges the starch granules. TaSnRK1α was found to restore starch accumulation in Arabidopsis kin10. Remarkably, TaSnRK1α O/E increases the AGPase activity suggesting the direct regulation of rate limiting enzyme AGPase involved in starch biosynthesis. Furthermore, in vitro and in vivo interaction assay reveal that TaSnRK1α interacts with AGPase large sub-unit. Overall, our findings indicate that TaSnRK1α plays a role in starch biosynthesis by regulating AGPase activity.

Delineation of groundwater potential zones and its extent of contamination from the hard rock aquifers in west-Bengal, India.

This study evaluates the groundwater potential and quality in the parts of Chhotanagpur Gneissic Complex situated in the East Indian Shield. The region has faced groundwater development challenges for several decades. Therefore, in the study area, it is crucial to address the depletion of both groundwater quality and quantity, as this facilitates the identification of potential uncontaminated groundwater zones. The present study interprets the groundwater potential zones (GWPZ) utilizing an analytical hierarchical process (AHP) integrated with hydrogeochemical analysis. Several thematic maps were prepared to delineate the GPWZ. It has been found that ∼0.6% of the study area has a very good potential zone, 14.4% has good, 52% has moderate, and approximately 32% and 0.9% have low to very low prospective groundwater resources, respectively. The authentication of results was found to be excellent (91.4%) with the Area Under Curve (AUC). Analysis of hydrogeochemical data suggests that Mixed Ca-Na-HCO3, Mixed Ca-Mg-Cl, Ca-HCO3, and Na-Cl are the dominant water types in the study area. The principal component analysis suggests that Na+, Mg2+, Cl-, NO3-, and SO42- significantly contribute to groundwater chemistry. The K-means clustering and hierarchical cluster analysis classified groundwater samples into three clusters based on the hydrogeochemical characteristics. It is inferred that silicate weathering and reverse ion reactions through rock-water interaction control geogenic processes for groundwater chemistry. It is also inferred that regions with poor to unsuitable water quality indexes also have low GWPZ. Further, groundwater for irrigation is also accessed and found unsuitable at some locations. This research contributes to comprehending groundwater characteristics in analogous geological regions globally. Additionally, it assists in implementing preventive actions to mitigate groundwater contamination, consequently lowering health risks and formulating sustainable plans for the future.

Exploring the interplay between particulate matter capture, wash-off, and leaf traits in green wall species.

The study investigated inter-species variation in particulate matter (PM) accumulation, wash-off, and retention on green wall plants, with a focus on leaf characteristics. Ten broadleaf plant species were studied in an experimental green wall. Ambient PM concentrations remained relatively stable throughout the measurement period: PM1: 16.60 ± 9.97 µgm-3, PM2.5: 23.27 ± 11.88 µgm-3, and PM10: 39.59 ± 25.72 µgm-3. Leaf samples were taken before and after three rainfall events, and PM deposition was measured using Scanning Electron Microscopy (SEM). Leaf micromorphological traits, including surface roughness, hair density, and stomatal density, exhibited variability among species and leaf surfaces. Notably, I.sempervirens and H.helix had relatively high PM densities across all size fractions. The study underscored the substantial potential of green wall plants for atmospheric PM removal, with higher Wall Leaf Area Index (WLAI) species like A.maritima and T.serpyllum exhibiting increased PM accumulation at plant level. Rainfall led to significant wash-off for smaller particles, whereas larger particles exhibited lower wash-off rates. Leaf micromorphology impacted PM accumulation, although effects varied among species, and parameters such as surface roughness, stomatal density, and leaf size did not consistently affect PM deposition. The composition of deposited particles encompassed natural, vehicular, salt, and unclassified agglomerates, with minimal changes after rainfall. Air Pollution Tolerance Index (APTI) assessments revealed that I.sempervirens displayed the highest air pollution tolerance, while O.vulgare had the lowest. APTI showed a moderate positive correlation with PM deposition across all fractions. The study concluded that the interplay of macro and micromorphology in green wall plant species determines their PM removal potential. Further research is needed to identify the key leaf characteristics for optimal green wall species selection for effective PM removal.

Lagrangian solver for coupling hydrodynamics with biokinetic conversion modelling in anaerobic digesters.

Conventional anaerobic digestion models used in wastewater treatment plants suffer from inaccuracies due to the limited consideration given to hydrodynamics within the digester tank. A solution to this is to combine computational fluid dynamics simulations with anaerobic models. This paper introduces a novel methodology in the form of a software toolbox that implements the standard anaerobic digestion model no.1 in C++ and can interface with particle-based Lagrangian simulations. This method provides significantly more insights into the biochemical conversion process by accounting for the impact of the hydrodynamics on the biochemical reactions. The paper presents the background of the method along with a conceptual and numerical verification. It also presents a case study of a 3D lab scale digester comparing the results from the solver with the standard anaerobic digestion model. This integrated approach can be used by operators and designers for optimisations and also for predictive modelling.

Microwave Doping of Sulfur and Iron in ß12 Borophene.

Borophene, a 2D material exhibiting unique crystallographic phases like the anisotropic atomic lattices of ß12 and X3 phases, has attracted considerable attention due to its intriguing Dirac nature and metallic attributes. Despite surpassing graphene in electronic mobility, borophene's potential in energy storage and catalysis remains untapped due to its inherent electrochemical and catalytic limitations. Elemental doping emerges as a promising strategy to introduce charge carriers, enabling localized electrochemical and catalytic functionalities. However, effective doping of borophene has been a complex and underexplored challenge. Here, an innovative, one-pot microwave-assisted doping method, tailored for the ß12 phase of borophene is introduced. By subjecting dispersed ß12 borophene in dimethylformamide to controlled microwave exposure with sulfur powder and FeCl3 as doping precursors, S- and Fe doping in borophene can be controlled. Employing advanced techniques including high-resolution transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy, confirm successful sulfur and iron dopant incorporation onto ß12 borophene is confirmed, achieving doping levels of up to 11 % and 13 %, respectively. Remarkably, S- and Fe-doped borophene exhibit exceptional supercapacitive behavior, with specific capacitances of 202 and 120 F g-1 , respectively, at a moderate current density of 0.25 A g-1 .

Emulgel: A Cutting Edge Approach for Topical Drug Delivery System.

Emulgel is considered an advanced leading form of topical drug delivery system. It possesses the quality of a dual control drug mechanism for drug release as it holds the properties of both gel as well as emulsion. Emulgel is capable of overcoming the problems of the conventional route of topical drug delivery, like low spreadability and stickiness with the delivery of hydrophobic drugs, enhanced bioavailability at the local site of action, no greasy texture, and ensuring patient compliance. An emulsion is used either w/o or o/w, and the drug can be incorporated into the suitable phase of the emulsion. After that, the emulsion is incorporated into the gel phase. Several factors like oil phase, gelling agent, and emulsifier can affect the efficacy and stability. This advancement is beneficial not only for dermatology but also for cosmetology as well. Currently, emulgel-based formulations are used for the delivery of anti-inflammatory, analgesic, anti-acne, and antifungal drugs with a wide array of exploration.

Anti-microbial efficacy of a scientifically developed and standardized herbal-alcohol sanitizer.

Hands are the primary mode of transmission of microbe-based infections, as they harbor normal microbiota and pathogenic microbes. SARS-CoV-2 has endangered lives worldwide, and WHO has recommended good hygiene practices, especially hand hygiene. In addition, other infectious diseases like diphtheria, measles, tuberculosis, HIV, malaria, etc. are spreading in the shadow of the COVID-19 pandemic. The anti-microbial efficiency of two in-house developed herbal-alcohol based hand sanitizers containing Azadirachta indica, Citrus limon, Zingiber officinale, and Aloe vera (HS1) and Zingiber officinale replaced with Ocimum sanctum (HS2) was evaluated. HS1, with Zingiber officinale, and HS2, with Ocimum sanctum, herbal sanitizers showcased in-vitro anti-viral activity on MDCK cells using the reference strain of influenza A virus, A/PR/8/34 (H1N1), and reduced 99.99% of microbial load within 30 s of contact time, estimated by the Antimicrobial Susceptibility Testing Method. On volunteers, HS1 and HS2 were more effective than alcohol-based WHO sanitizers. Moreover, HS2 sanitizer is more effective against viruses and has better efficiency and hedonic qualities in volunteers than HS1. These sanitizers don't irritate or dry up the skin and have a longer shelf life. Overall, findings reveal that herbal-alcohol-based sanitizers are promising hand hygiene products with the capability of reducing microbial load.

Exploring in-person self-led debriefings for groups of learners in simulation-based education: an integrative review.

BACKGROUND: Facilitator-led debriefings are well-established for debriefing groups of learners in immersive simulation-based education. However, there has been emerging interest in self-led debriefings whereby individuals or groups of learners conduct a debriefing themselves, without the presence of a facilitator. How and why self-led debriefings influence debriefing outcomes remains undetermined. RESEARCH AIM: The aim of this study was to explore how and why in-person self-led debriefings influence debriefing outcomes for groups of learners in immersive simulation-based education. METHODS: An integrative review was conducted, searching seven electronic databases (PubMed, Cochrane, Embase, ERIC, SCOPUS, CINAHL Plus, PsychINFO) for peer-reviewed empirical studies investigating in-person self-led debriefings for groups of learners. Data were extracted, synthesised, and underwent reflexive thematic analysis. RESULTS: Eighteen empirical studies identified through the search strategy were included in this review. There was significant heterogeneity in respect to study designs, aims, contexts, debriefing formats, learner characteristics, and data collection instruments. The synthesised findings of this review suggest that, across a range of debriefing outcome measures, in-person self-led debriefings for groups of learners following immersive simulation-based education are preferable to conducting no debriefing at all. In certain cultural and professional contexts, such as postgraduate learners and those with previous debriefing experience, self-led debriefings can support effective learning and may provide equivalent educational outcomes to facilitator-led debriefings or self-led and facilitator-led combination strategies. Furthermore, there is some evidence to suggest that self-led and facilitator-led combination approaches may optimise participant learning, with this approach warranting further research. Reflexive thematic analysis of the data revealed four themes, promoting self-reflective practice, experience and background of learners, challenges of conducting self-led debriefings and facilitation and leadership. Similar to facilitator-led debriefings, promoting self-reflective practice within groups of learners is fundamental to how and why self-led debriefings influence debriefing outcomes. CONCLUSIONS: In circumstances where simulation resources for facilitator-led debriefings are limited, self-led debriefings can provide an alternative opportunity to safeguard effective learning. However, their true value within the scope of immersive simulation-based education may lie as an adjunctive method alongside facilitator-led debriefings. Further research is needed to explore how to best enable the process of reflective practice within self-led debriefings to understand how, and in which contexts, self-led debriefings are best employed and thus maximise their potential use.