Real-world Settings Research Articles

Subjective intoxication predicts alcohol-related consequences at equivalent alcohol concentrations in young adults using ecological momentary assessment and alcohol sensors.

Subjective intoxication (SI) when drinking may serve as an internal barometer of whether to continue drinking or engage in potentially unsafe behavior. Mobile assessments offer the potential to use SI as a prospective risk indicator during drinking episodes; little evidence exists for the validity of real-time SI measures. We test the correspondence of SI with estimated blood alcohol concentration and transdermal alcohol concentration (TAC) in young adults' natural settings. We provide a novel test of whether SI features (peak and mean SI) uniquely predict consequences adjusting for alcohol concentration. Two hundred twenty-two heavy-drinking young adults (Mage = 22.3, 64% female, 79% non-Hispanic White, 84% undergraduates) participated in a 6-day study that used ecological momentary assessment of drinking and TAC sensors. SI was assessed every 30 min during drinking episodes. Multilevel modeling was used to test hypotheses. Momentary SI and estimated blood alcohol concentration had moderate associations at the moment and day levels (standardized βs = 0.5-0.6); SI was moderately associated with TAC at the day level (βs = 0.5). Associations between SI and alcohol concentration varied widely between persons and across days. Day-level SI features predicted consequences when adjusting for alcohol concentration (incidence rate ratios, IRRs = 1.29-1.70). Our two-item SI measure shows evidence of validity in real-world settings with heavy-drinking young adults. SI was significantly correlated with alcohol concentration and was a unique predictor of consequences. The strength of these associations varied greatly across persons and days. Real-time SI measurement may be useful in preventive interventions, but continued research is needed into when and for whom momentary SI is most predictive of risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

EFFECTS OF A PRESCRIBED, SUPERVISED EXERCISE PROGRAMME ON HEALTH-RELATED QUALITY OF LIFE IN ONCOLOGY PATIENTS: A RETROSPECTIVE OBSERVATIONAL COHORT STUDY

INTRODUCTION & AIMS Individuals diagnosed with cancer experience significant physical and psychosocial consequences from oncological treatment. Based on current research, exercise therapy may be a valuable non-pharmacological approach to improve health-related quality of life (HRQoL). However, the effectiveness of individually tailored exercise programs to improve quality of life in real-world settings is understudied. The current study aims to analyse preliminary clinical data regarding the impact of tailored exercise supervised within an Australian private day hospital (LIFT) on HRQoL in individuals diagnosed with cancer. METHODS A retrospective longitudinal cohort study was conducted on adults diagnosed with cancer who attended the LIFT clinical service for at least 12 months between 2020 and 2023. QoL and symptoms were assessed using the Functional Assessment of Cancer Treatment – General Questionnaire (FACT-G). Data collected included diagnosis (type and stage), medical treatment, session frequency, and demographic data. RESULTS Overall, 40 patients (age=67.0±16.8 years) met the inclusion criteria. A within-group analysis demonstrated significant findings in the subscales of emotional wellbeing (p = 0.008) and functional wellbeing (p = 0.003), and significance was also found in the total FACT-G score (p = 0.0019). Interestingly, while not statistically significant, a clinically significant difference in HRQoL was found between individuals completing ≤1 or ≥ 2 supervised exercise sessions per week at LIFT cancer care centre, favouring those completing ≥ 2 sessions. CONCLUSION Preliminary analysis indicated that a “real-world” individualised supervised moderate-vigorous exercise within a day hospital has the potential to significantly improve HRQoL outcomes in individuals diagnosed with cancer.

Few-shot learning for skin lesion classification: A prototypical networks approach

Prototypical networks (PN) have emerged as one of multiple effective approaches for few-shot learning (FSL), even in medical image classification. This study focuses on implementing a PN for skin lesion classification to assess its performance, generalizability, and robustness when applied across 11 dermoscopic image domains. Unlike conventional FSL scenarios, where the performance is evaluated for unseen classes in the test set, our analysis extends this to evaluate PNs on a complete hold-out dataset with the same classes from a different domain. Differences in a patient’s age, lesion localization, or image acquisition systems variations mimic real-world cross-domain conditions in a clinic. Given the scarcity of medical datasets, this assessment is crucial for potentially translating such systems into real-world clinical settings to support physicians with the diagnosis. Our primary focus is two-fold: investigating whether a PN performs on par with a baseline classifier, even using only a limited number of reference samples from the hold-out test set (in-domain) and whether a PN can generalize to the same classes of unseen domains (cross-domain). Our analysis uncovers that a PN can perform on par with the baseline classifier in an in-domain setting, even with only a few support samples. However, in cross-domain scenarios, a PN exhibits improved performance only on specific domains, while others demonstrate similar or even decreased performance when confronted with a smaller number of images. Our findings contribute to comprehending potential opportunities and limitations of FSL in dermatological practice.

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

Real-world safety and effectiveness of anamorelin for cancer cachexia: Interim analysis of post-marketing surveillance in Japan.

Anamorelin was approved in Japan in 2021 to treat cancer cachexia associated with non-small cell lung, gastric, pancreatic, or colorectal cancers. Post-marketing surveillance is being conducted to evaluate the real-world safety and effectiveness of anamorelin. This prospective, observational surveillance registered all patients who started treatment with anamorelin after April 21, 2021. Hyperglycemia, hepatic impairment, conduction disorders, and their associated adverse events related to treatment were defined as main safety specifications. Body weight (BW) and appetite were assessed as effectiveness specifications. This analysis was based on data as of January 21, 2023. The safety and effectiveness analysis sets included 6016 and 4511 patients, respectively. Treatment-related adverse events in ≥1% of patients were hyperglycemia (3.9%) and nausea (2.6%). The incidences of hyperglycemia, hepatic impairment, conduction disorders, and their associated adverse events related to treatment were 4.8%, 1.2%, and 1.1%, respectively. The mean changes (standard error [SE]) in BW from baseline to weeks 3, 12, 24, and 52 were 0.64 (0.05) kg, 1.19 (0.12) kg, 1.40 (0.21) kg, and 1.42 (0.39) kg, respectively. The mean changes (SE) in Functional Assessment of Anorexia/Cachexia Treatment 5-item Anorexia Symptom Scale total scores from baseline to weeks 3, 12, 24, and 52 were 3.2 (0.09), 4.8 (0.18), 5.2 (0.30), and 5.3 (0.47), respectively, exceeding the clinically meaningful improvement score (2.0 points). The overall safety of anamorelin raised no new safety concerns, although continued caution may be required for hyperglycemia and nausea. Improvements in BW and appetite were also observed in real-world clinical settings.

Early response to Tezepelumab in type-2 severe asthma patients non-responders to other biological treatments: a real-life study.

ABSTRACT BACKGROUND. Biologic therapies play a crucial role in the treatment of severe asthma. Tezepelumab, a human monoclonal antibody (mAb), inhibits thymic stromal lymphopoietin, a pivotal factor in the pathophysiology of asthma. Although randomized clinical trials have demonstrated the efficacy of Tezepelumab, evidence gaps remain in real-world scenarios. OBJECTIVE. We sought investigate Tezepelumab's response in a clinical setting, focusing on patients who previously failed to other asthma mAbs. METHODS. Real-life study with severe uncontrolled asthma patients despite mAb treatment, requiring a switch to Tezepelumab. Follow-up was done four to six months after initiation of Tezepelumab. The primary endpoint was to evaluate the response in patients with poor response or intolerance to other mAbs. RESULTS. Nine patients were followed up during 7 months. Patients were predominantly middle-aged females with eosinophilic or eosinophilic-allergic phenotypes. Patients had a median failure rate of 2 mAbs (IQR 2-3), with an uncontrolled asthma (median of 2 severe exacerbations the previous year, airflow obstruction and 78% corticosteroid dependence). Tezepelumab demonstrated after 4 to 6 months of treatment reduce corticosteroid dependence (complete withdrawal in 2/7 patients), no exacerbations in 6/9, symptoms control improvement (Asthma Control Test score improved in 5/9) and modulate lung function (improving in 3/9 patients). These findings align with clinical trial results, suggesting Tezepelumab's potential in real-world settings. CONCLUSION. In real-world scenarios, despite the study's limitations, our results underscore Tezepelumab's promise as a therapeutic option for uncontrolled severe asthma, and may be useful for non-responders to other mAbs. Further studies are needed to corroborate these findings.

Persistence and Adherence to PCSK9 Inhibitor Monoclonal Antibodies Versus Ezetimibe in Real-World Settings.

The cardiovascular disease risk reduction benefits of proprotein convertase subtilisin/kexin type 9 inhibitor monoclonal antibodies (PCSK9i mAb) and ezetimibe are dependent on remaining on treatment and being persistent and adherent. We estimated the percentage of patients on therapy, persistent and adherent at 182 and 365days among US adults with health insurance who initiated a PCSK9i mAb (n = 16,588) or ezetimibe (n = 83,086) between July 2015 and December 2019. Using pharmacy fill claims, being on therapy was defined as having a day of medication supply in the last 60 of 182 and 365days following treatment initiation, being persistent was defined as not having a gap of 60days or more between the last day of supply from one prescription fill and the next fill, and being adherent was defined by having medication available to take on ≥ 80% of the 182 and 365days following treatment initiation. We estimated multivariable-adjusted risk ratios for being persistent and adherent comparing patients initiating PCSK9i mAb versus ezetimibe using Poisson regression. At 182days following initiation, 80% and 68% were on therapy and 76% and 64% were persistent among patients who initiated a PCSK9i mAb and ezetimibe, respectively. Among patients who were on therapy and persistent at 182days following initiation, 88% and 81% of those who initiated a PCSK9i mAb and ezetimibe, respectively, were on therapy at 365days. Among those on therapy and persistent at 182days following initiation, being persistent and being adherent at 365days were each more common among PCSK9i mAb versus ezetimibe initiators (persistent: 82% versus 76%, multivariable-adjusted risk ratio 1.07; 95% confidence interval [CI] 1.06-1.08; adherent: 74% versus 71%, multivariable-adjusted risk ratio 1.02; 95% CI 1.01-1.03). These data suggest approaches to increase persistence and adherence to PCSK9i mAb and ezetimibe should be implemented prior to or within 182days following treatment initiation.

Detection of Lane & Speed Breakers for Autonomous Vehicles using CNN

Abstract: The advancement of autonomous vehicles necessitates robust systems for real-time detection of lane markings and speed breakers to ensure safe and efficient navigation. In this paper, we propose a novel approach for lane and speed breaker detection utilizing Convolutional Neural Networks (CNN). Our method achieves remarkable accuracy in identifying lane markings and speed breakers from input images captured by onboard cameras. By leveraging the capabilities of CNN, our system demonstrates superior performance in diverse environmental conditions, including varying lighting, weather, and road surface conditions. We present a comprehensive analysis of the CNN architecture employed in our system, highlighting its ability to extract meaningful features from raw image data and make accurate predictions. The training process involves a large dataset comprising annotated images of lane markings and speed breakers, enabling the CNN to learn discriminative patterns for effective detection. Furthermore, we evaluate the performance of our proposed system through extensive experimentation, quantifying its accuracy, precision, and recall rates across different scenarios. The results indicate the effectiveness and reliability of our approach in real-world settings, showcasing its potential for integration into autonomous vehicle platforms. Overall, this paper contributes to the field of autonomous driving by presenting a robust and efficient solution for lane and speed breaker detection, leveraging the power of machine learning algorithms, specifically CNN, to enhance the safety and autonomy of vehicles on the road.

Detection of Virtual Private Networks Encrypted Traffic using Machine and Ensemble Learning Algorithms

Abstract: The widespread adoption of Virtual Private Networks (VPNs) for secure communication over public networks has highlighted the need for accurate detection of encrypted network traffic. This study proposes a comprehensive approach utilizing machine and ensemble learning algorithms to address this detection challenge. Leveraging the ISCX VPN dataset, the project aims to develop a robust system capable of distinguishing between VPN and non-VPN traffic with high accuracy. Key stages include data acquisition, preprocessing, feature extraction, and model training using Decision Tree, Naive Bayes, Random Forest, and Adaboost algorithms. Through rigorous experimentation, the most effective algorithmic approach is identified. The project also offers insights into the diverse nature of encrypted network communication and its implications for network security. The anticipated outcomes include improved understanding and management of VPN traffic, enhancing overall network security and performance. This research contributes to advancing network security practices by offering practical solutions for encrypted traffic detection in real-world settings.

Innovations in public health surveillance: An overview of novel use of data and analytic methods.

Innovative data sources and methods for public health surveillance (PHS) have evolved rapidly over the past 10 years, suggesting the need for a closer look at the scientific maturity, feasibility, and utility of use in real-world situations. This article provides an overview of recent innovations in PHS, including data from social media, internet search engines, the Internet of Things (IoT), wastewater surveillance, participatory surveillance, artificial intelligence (AI), and nowcasting. Examples identified suggest that novel data sources and analytic methods have the potential to strengthen PHS by improving disease estimates, promoting early warning for disease outbreaks, and generating additional and/or more timely information for public health action. For example, wastewater surveillance has re-emerged as a practical tool for early detection of the coronavirus disease 2019 (COVID-19) and other pathogens, and AI is increasingly used to process large amounts of digital data. Challenges to implementing novel methods include lack of scientific maturity, limited examples of implementation in real-world public health settings, privacy and security risks, and health equity implications. Improving data governance, developing clear policies for the use of AI technologies, and public health workforce development are important next steps towards advancing the use of innovation in PHS.

COMMERCIALISED DURIAN PLANTATION: DEVELOPMENT AND DESIGN OF WEB AND MOBILE APPLICATION

Durian farmers face many challenges in managing their plantations efficiently and effectively. These challenges include unpredictable weather patterns, labour shortages, pest and disease outbreaks, market fluctuations, and the complexity of managing farm operations. The commercialisation of durian plantations represents a significant shift in the agricultural landscape, driven by increasing demand for this prized tropical fruit. This project focuses on developing and designing a comprehensive web and mobile application explicitly tailored for commercialised durian plantations in Malaysia. Employing the Rapid Application Development (RAD) approach, the project encompasses four main phases: analysis, rapid design, prototyping, testing, and deployment. By leveraging digital technology, the application aims to address these challenges by streamlining various aspects of durian plantation management, including crop monitoring, inventory management, pest and disease tracking, and yield forecasting. Through user-friendly interfaces and intuitive functionalities, the application seeks to empower durian growers with actionable insights and decision-making tools to navigate these challenges and enhance their operations' productivity, efficiency, and sustainability. An evaluation component was conducted to assess the usability and effectiveness of the application in real-world plantation settings. Overall, the response to the evaluation indicated that both applications are valuable and easy to use, justifying their integration into durian plantation management practices. The significance of this project lies in its potential to revolutionise durian plantation management practices, contributing to the broader goals of sustainable agriculture and economic development in the durian industry. Findings from the project evaluation will inform future directions for refinement and expansion of the application, ensuring its continued relevance and impact in supporting the growth and competitiveness of commercialised durian plantations.

EVALUATING MACHINE LEARNING MODELS FOR PREDICTING SLEEP DISORDERS IN A LIFESTYLE AND HEALTH DATA CONTEXT

EVALUATING MACHINE LEARNING MODELS FOR PREDICTING SLEEP DISORDERS IN A LIFESTYLE AND HEALTH DATA CONTEXT

Privacy-Preserving Data Sharing Platform

In today's data-driven healthcare landscape, the secure sharing of sensitive medical information is essential for improving patient care, facilitating medical research, and advancing healthcare outcomes. However, ensuring the integrity, confidentiality, and privacy of patient data poses significant challenges, particularly in the context of big data environments. This presents a comprehensive framework for privacy-preserving data sharing in healthcare, leveraging a combination of cryptographic techniques, encryption, and secure computation protocols. The framework encompasses various privacy-preserving mechanisms, including Differential Privacy with Data Perturbation, Secure Multi-Party Computation (SMPC), and Homomorphic Encryption, to protect sensitive healthcare data from unauthorized access and disclosure. By implementing state-of-the-art privacy-preserving techniques, the framework aims to enable secure data sharing among multiple parties while complying with regulatory requirements such as HIPAA and GDPR. Additionally, the paper discusses the project scope, which includes cryptography, encryption, decryption, integrity, confidentiality, privacy, policies, procedures, security, and secure data sharing infrastructure. The proposed framework provides a practical solution for healthcare organizations and research institutions to collaborate on data-driven initiatives while safeguarding patient privacy and maintaining trust. Evaluation of the framework's effectiveness and performance metrics is conducted to validate its feasibility and efficacy in real-world healthcare settings. Keywords: Privacy-preserving data sharing, Differential Privacy, Data Perturbation, Secure Multi-Party Computation (SMPC)

Short-term efficacy of vedolizumab in patients with inflammatory bowel disease in real-life settings in Bosnia and Herzegovina.

Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), necessitates effective management strategies. This study aims to evaluate the real-world efficacy of vedolizumab, a newer biological therapy, in treating IBD in Bosnia and Herzegovina. A retrospective observational study was conducted across 6 medical centers, involving 139 IBD patients, 76 with UC and 63 with CD. Patients were assessed for clinical remission and other outcomes at the 26-week mark post vedolizumab treatment initiation. At 26 weeks, clinical remission was achieved in 82.9% of UC patients and 85.7% of CD patients. Mucosal healing was observed in 38.1% of CD patients. The efficacy of vedolizumab did not significantly differ based on prior anti-tumor necrosis factor (TNF) exposure. Notably, the clinical scoring tools for predicting vedolizumab response showed limited applicability in this cohort. Vedolizumab demonstrated high efficacy in treating both UC and CD in a real-world settings in Bosnia and Herzegovina, underscoring its potential as a significant therapeutic option in IBD management.

The clinical perspective on late-onset depression in European real-world treatment settings

The clinical phenotype of the so-called late-onset depression (LOD) affecting up to 30% of older adults and yielding heterogeneous manifestations concerning symptoms, severity and course has not been fully elucidated yet. This European, cross-sectional, non-interventional, naturalistic multicenter study systematically investigated socio-demographic and clinical correlates of early-onset depression (EOD) and LOD (age of onset ≥ 50 years) in 1410 adult in- and outpatients of both sexes receiving adequate psychopharmacotherapy. In a total of 1329 patients (94.3%) with known age of disease onset, LOD was identified in 23.2% and was associated with unemployment, an ongoing relationship, single major depressive episodes, lower current suicidal risk and higher occurrence of comorbid hypertension. In contrast, EOD was related to higher rates of comorbid migraine and additional psychotherapy. Although the applied study design does not allow to draw any causal conclusions, the present results reflect broad clinical settings and emphasize easily obtainable features which might be characteristic for EOD and LOD. A thoughtful consideration of age of onset might, hence, contribute to optimized diagnostic and therapeutic processes in terms of the globally intended precision medicine, ideally enabling early and adequate treatment allocations and implementation of respective prevention programs.

Ensemble feature ranking approach for software fault prediction

Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy.

Understanding Ergonomics in Nanotechnology Workspaces: Ergonomics in Nanotechnology

The success of ergonomics in the future will be determined by how well the field handles the difficulties posed by new fields that call for scientific study and how successfully the findings are applied in real-world settings. The field of nanotechnology has advanced more quickly than our understanding of the potential consequences of such advancements. As a result, many of the same questions that surround any new technology are also raised by nanotechnology, such as toxicity and the effects of nanomaterials on the environment. Employees in businesses connected to nanotechnology may be exposed to materials that are specifically manufactured with nanoscale sizes, shapes, and physicochemical qualities. At this time, nothing is known about the main pathways of exposure, the possible exposure thresholds, and the material toxicity of nanomaterials. This finding suggests that both our understanding of nanomaterials and our capacity to guard against the risks associated with nanotechnology are lacking. The employee's brain or other organs may get infected with the nanoparticles due to the nanomaterial's incredibly tiny sizes and properties. Hence, accurate hazard assessment of the relevant work areas should be a part of effective exposure control systems. This review explains about managing nanotechnology exposures in the workplace, as well as other important controls as handling or working with nanotechnologies and nanomaterials, ergonomic approaches which would be effective to improve safety and health issues in the nanotechnology industry.

Shining a spotlight on the inclusion of disabled participants in clinical trials: a mixed methods study

BackgroundIt is crucial to include a wide range of the population in clinical trials for the outcome to be applicable in real-world settings. Existing literature indicates that under-served groups, including disabled people, have been excluded from participating in clinical trials without justification. Exclusion from clinical trials exacerbates disparities in healthcare and diminishes the benefits for excluded populations. Therefore, this study was conducted to investigate potential obstacles that prevent disabled people from participating in clinical trials in the United Kingdom (UK).MethodsThe study was carried out through an explanatory sequential mixed methods design. The Imperial Clinical Trials Unit devised and implemented an online questionnaire-based survey (with open/closed-ended questions) and an online focus group discussion. The target population were disabled people, family members/carers of disabled people and staff involved in clinical trials, whereupon the sample was recruited by convenience sampling methods via posters and emails through various networks. The Qualtrics XM survey system was used as the host platform for the online survey, and Microsoft Teams was used for an online focus group discussion. The focus group discussion was conducted to gain a deeper understanding of the themes identified from the survey responses. We analysed responses to the survey via descriptive analysis and used thematic analysis to synthesise the free-text answers from the survey and focus group discussion.ResultsWe received 45 responses to the survey questionnaire and 5 disabled people took part in a focus group discussion. Our findings highlighted the differences between the perspectives of researchers and those “being researched” and different types of barriers experienced by disabled people: opportunity barriers (inadequate recruitment strategy and ambiguous eligibility criteria), awareness barriers (perception of disability) and acceptance/refusal barriers (available support and adjustment, and sharing of trial results).ConclusionOur findings support perspectives drawn from the Ford Framework regarding the need to consider all barriers, not just up to the point of enrolment into trials but also beyond the point of inclusion in clinical trials. We support calls for the introduction of legislation on including disabled people in clinical trials, implementation of industry/community-wide participatory approaches and the development of guidelines, a combined public–private approach.

Antifungal Activity of Novel Indole Derivatives Containing 1,3,4-Thiadiazole.

In this study, 24 indole derivatives containing 1,3,4-thiadiazole were discovered and synthesized. The target compounds' antifungal efficacy against 14 plant pathogenic fungal pathogens was then determined in vitro. With an EC50 value of 2.7 μg/mL, Z2 demonstrated the highest level of bioactivity among them against Botrytis cinerea (B.c.), exceeding the concentrations of the control prescription drugs azoxystrobin (Az) (EC50 = 14.5 μg/mL) and fluopyram (Fl) (EC50 = 10.1 μg/mL). Z2 underwent in vivo testing on blueberry leaves in order to evaluate its usefulness in real-world settings. A reasonable protective effect was obtained with a control effectiveness of 93.0% at 200 μg/mL, which was superior to those of Az (83.0%) and Fl (52.0%). At 200 μg/mL, this chemical had an efficacy of 84.0% in terms of curative efficacy. These figures outperformed those of Az (69.0%) and Fl (48.0%). Scanning electron microscopy (SEM) experiments and light microscopy experiments showed that Z2 altered the integrity of the cell wall and cell membrane of the pathogenic fungus B.c., which led to an increase in the content of malondialdehyde (MDA), cellular leakage, and cellular permeability. Enzyme activity assays and molecular docking studies indicated that Z2 could act as a potential succinate dehydrogenase inhibitor (SDHI). It was hypothesized that Z2 could cause disruption of mycelial cell membranes, which in turn leads to mycelial death. According to the research, indole derivatives containing 1,3,4-thiadiazole were expected to evolve into new fungicides due to their significant antifungal effects on plant fungi.

Real-life experience on the effectiveness of conjunctival flap and amniotic membrane graft in the treatment of refractory fungal corneal ulcer.

To evaluate and compare the results of the conjunctival flap (CF) and cryopreserved amniotic membrane graft (AMG) in the management of fungal corneal ulcers either with complications or non-responsive to medical treatment. A retrospective observational study. Medical records of 30 patients with culture-positive fungal corneal ulcer treated with either CF or AMG (15 eyes in each group) in real world settings were retrieved for analysis. After the surgical procedure, patients were followed up on days 1, 7, 14, 21, 30, 60, 90, 120, and 180 to explore the outcomes of the operations along with complications. Infecting fungi were of genus Fusarium (n = 11), Aspergillus (n = 10), Mucor (n = 4) and Penicillium (n = 10). The most common indication was resistant ulcer with perforation. After the procedure, epithelization was completed in 11(73.33%) patients in the CF, and 13 patients in the (86.67%) AMG group. Visual acuity improvement was significantly better in the latter group (CF: 1 [6.67%] vs. AMG: 7 [46.67%], p = 0.023). Flap failure occurred in 4 patients (26.67%) from the CF and 2 (13.33%) from the AMG group. No significant differences were found between the two groups regarding success rate (p = 0.651), epithelialization time (p = 0.691), healing of corneal ulcer (p = 0.651), and postoperative stability (p = 0.651) of the flaps. CF and AMG are both effective for the management of refractory fungal corneal ulcers. However, AMG appears to improve visual acuity better than CF.