Evaluation Of Treatment Efficacy Research Articles

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

Endoscopic band ligation or endoscopic tissue adhesive injection in the treatment of gastric varices: Which is better?

The combination of endoscopic ultrasound with endoscopic treatment of type 1 gastric variceal hemorrhage may improve the robustness and generalizability of the findings in future studies. Moreover, the esophageal varices should also be included in the evaluation of treatment efficacy in subsequent studies to reach a more convincing conclusion.

Comparing a Low-Fluence Picosecond 1064 nm Nd:YAG Laser with a 532 nm Nd:YAG Laser for the Treatment of Pigmented Lesions in Chinese Patients: A Retrospective Analysis

The use of low-fluence picosecond (LFPS) 1064 nm neodymium-doped yttrium aluminum garnet (Nd:YAG) lasers, referred to as laser toning, is increasingly acknowledged as an effective treatment for pigmentation disorders in the Asian skin phenotype. This study aimed to conduct a comparative analysis on the effectiveness and safety of utilizing LFPS 1064 nm Nd:YAG lasers against picosecond 532 nm Nd:YAG lasers in treating pigmented lesions among Chinese patients. A retrospective photographic analysis and chart reviews were performed on 31 subjects exhibiting Fitzpatrick skin types III–VI who underwent LFPS 1064 nm Nd:YAG or picosecond 532 nm Nd:YAG treatments at a single tertiary center. Utilizing VISIA Complexion Analysis, comparative photographs were taken. Two independent physicians evaluated treatment efficacy using a visual analog scale (VAS) to assess the percentage of pigmentary clearance in standard photographs. Solar lentigines were the most prevalent pigmentary disorder, followed by post-inflammatory hyperpigmentation (PIH), nevus zygomaticus, melasma, freckles, and nevus of Ota. The clinical effectiveness of picosecond 532 nm and LFPS 1064 nm laser treatments proved comparable for lesions on the face, with mean VAS scores of 2.2 ± 1.1 and 1.8 ± 0.8, respectively. There were two cases of PIH in the picosecond 532 nm group, which resolved within one month. Overall, the LFPS 1064 nm laser demonstrates promise as a safe and efficient therapeutic modality for managing pigmented lesions in Chinese patients.

Autologous osteoperiosteal transplantation is effective in the treatment of single cystic osteochondral lesions of the talus and the prognostic impact of age should be emphasized.

To investigate the clinical efficacy and prognostic factors associated with autologous osteoperiosteal transplantation for the treatment of single cystic osteochondral lesions of the talus (OLT). The clinical data of patients with single cystic OLT undergoing autologous osteoperiosteal transplantation at the Department of Foot and Ankle Surgery of our hospital between 2018 and 2022, including complete follow-up, were retrospectively analyzed. Imaging data from each patient were imported into Mimics software to measure the surface area, volumeand depth of the lesions. Then, the talus nine-compartment partitioning method was used to partition the injury site. Preoperative and final follow-up assessments were performed using the American Orthopaedic Foot and Ankle Society (AOFAS) score, visual analogue scale (VAS) for painand 36-item Short-Form Health Survey (SF-36) to evaluate treatment efficacy and analyze prognostic factors. Of the 31 patients with single cystic OLT with a complete set of follow-up data, there were 17 males and 14 females, with a mean age of 43.3 ± 13.6 years, a mean follow-up time of 30.1 ± 14.0 monthsand a mean illness duration of 30.4 ± 20.0 months. The postoperative final follow-up AOFAS score was 90.7 ± 5.5; this represented significant improvement when compared to the preoperative score of 57.0 ± 8.5 (P < 0.001). The final postoperative follow-up VAS score was 18.5 ± 8.3; this was significantly better than the preoperative score of 57.8 ± 8.7 (P < 0.001). The physical component summary (PCS)score and mental component summary (MCS)score on the SF-36 scale showed significant improvement at the final postoperative follow-up when compared to preoperative scores (p < 0.001). No other complications were observed during follow-up, such as wound infection or pain at the donor site. One of the patients showed less improvement, which may be related to premature weight-bearing or re-sprained ankle after surgery. There was no significant correlation between the duration of illness, genderand the location, depth, surface areaand volume of the OLTand the postoperative scores. However, patient age showed a significant negative correlation with the postoperative SF-36 PCS and MCS scores. Autologous osteoperiosteal transplantation for single cystic OLT demonstrated good clinical efficacy with a low incidence of complications. Furthermore, age represents an important factor influencing prognosis. Level II.

Responsiveness of the condition-specific OVAMA questionnaire to measure symptoms and appearance in patients with vascular malformations.

Evidence-based guidelines for the treatment of vascular malformations are not readily available, possibly due to the diversity in methods used to evaluate treatment efficacy in clinical research, complicating the aggregation and comparison of study results. The OVAMA (Outcome Measures for VAscular Malformations) questionnaire was developed to uniformly measure symptoms and appearance, i.e., condition-specific core outcome domains, in patients with vascular malformations. However, the OVAMA questionnaire needs to be responsive to changes in these constructs in order to assess whether the disease status has altered since treatment. To assess the responsiveness of the OVAMA questionnaire in patients with vascular malformations. In a prospective longitudinal study, patients completed the OVAMA questionnaire at baseline and eight weeks follow-up since treatment or watchful waiting policy. Additionally, patients completed the Global rating of change (GRC) scales at follow-up. Responsiveness was evaluated following the criterion approach of testing predefined hypotheses about expected relationships between the OVAMA questionnaire and GRC scales, measuring the same constructs. The OVAMA questionnaire was considered responsive if ≥ 75% of the hypotheses were confirmed. Between July 2020 and September 2022, 89 patients were recruited in a vascular anomaly centre in the Netherlands, of which 63 patients completed the questionnaires at baseline and follow-up. In total, fifteen constructs of the OVAMA questionnaire were assessed for five hypotheses. Of these 75 hypotheses, 63 (84%) hypotheses were confirmed and thereby providing evidence that the OVAMA questionnaire is responsive to change. Our study found convincing evidence that the OVAMA questionnaire is responsive to changes in symptoms and appearance in patients with vascular malformations. In addition to determining a baseline of symptoms and appearance, the OVAMA questionnaire can now be used to evaluate the effect of treatment from the patient's perspective. The responsive OVAMA questionnaire allows for uniform evaluation and comparison of the effects of treatment on the condition-specific core outcome domains, tackling heterogeneity in outcome measurement and improving the clinical research of vascular malformations.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications.

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies

Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and – importantly – it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.

Determining meaningful thresholds for evaluating treatment efficacy in patients with alopecia areata

AbstractBackgroundThe Severity of Alopecia Tool (SALT) is a clinician‐reported outcome measure of scalp hair loss in alopecia areata (AA).ObjectivesTo characterise the magnitudes of change in SALT scores corresponding to meaningful treatment benefits from the patient's perspective.MethodsAnchor‐based methods for the estimation of meaningful within‐patient change thresholds were applied to pooled data from a randomised, double‐blind trial of ritlecitinib. Anchors included a patient‐reported measure of change in AA severity, the Patient Global Impression of Change (PGI‐C) and three items comprising the Patient Satisfaction with Hair Growth (P‐Sat) questionnaire. After reviewing Pearson correlations between change‐from‐baseline SALT scores and each anchor to confirm adequate association, potential thresholds were computed as mean change‐from‐baseline SALT scores among patients who reported moderate improvement on the PGI‐C and/or moderate satisfaction on each of three P‐Sat items at week 24.ResultsSix hundred and fifty participants (86% adults, 14% adolescents) had mean (standard deviation) SALT scores of 90.6 (14.3) at baseline, suggesting a sample with primarily severe AA. Correlations between SALT change‐from‐baseline scores and the patient‐reported items supported their use as anchors. Estimates based on patients reporting moderate improvement in AA (n = 102) on the PGI‐C and those reporting moderate satisfaction on the P‐Sat item related to the amount of hair growth at week 24 (n = 122) were −42.2 (26.1) and −43.1 (26.8), respectively. Supportive estimates based on the remaining P‐Sat items were similar in magnitude.ConclusionsAmong patients with severe AA, SALT change‐from‐baseline scores of 42 or 43 represent meaningful improvements. While the achievement of low SALT scores of ≤10–≤20 have been used to characterise efficacy in clinical trials, the amount of change required to meet this endpoint far exceeds the estimates in this study. The treatment goals of individual patients must be considered when evaluating benefit in both clinical trials and clinical practice.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes.

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

Baseline trait mindfulness moderates the efficacy of mindfulness interventions and active controls: A meta‐analysis of 177 randomised controlled trials

AbstractPrevious evidence suggests that benefits of mindfulness‐based interventions (MBIs) may depend on individuals' baseline trait mindfulness (BTM) levels. This meta‐analysis investigated moderating effects of BTM on changes in mental health outcomes and trait mindfulness in randomised controlled trials (RCTs) of MBIs. A total of 177 primary studies (total N = 13,486), comparing the treatment effects of MBIs against active, treatment‐as‐usual (TAU) and waitlist control groups, were synthesised via three‐level meta‐analysis. Lower BTM was associated with larger changes in mental health outcomes (B = −0.14, 95% CI [−0.21, −0.06], p &lt; .001) and trait mindfulness (B = −0.09 [−0.16, −0.02], p = .009) in MBIs and active controls combined. These associations were significantly larger than from those in TAU and waitlist controls. Moderating effects were less tangible for changes in trait mindfulness in MBIs and active controls individually and less tangible in various sensitivity analyses which, however, were confounded by client type across the RCTs. Individuals low in BTM may, by a small effect size, benefit more from MBIs and active control interventions. BTM may thus affect the evaluation of treatment efficacy. MBIs and active interventions could be offered specifically to persons low in BTM.

Application of MR images in radiotherapy planning for brain tumor based on deep learning

Purpose Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods. Methods 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint. Results Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and Dmean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1 mm: 93.96%±6.75%, 2%/2 mm: 99.87%±0.30%, 3%/3 mm: 100.00%±0.00%; and 1%/1 mm: 99.14%±0.80%, 2%/2 mm: 99.92%±0.08%, 3%/3 mm: 99.99%±0.01%. Conclusion MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.

Automatic height measurement of central serous chorioretinopathy lesion using a deep learning and adaptive gradient threshold based cascading strategy

Accurately quantifying the height of central serous chorioretinopathy (CSCR) lesion is of great significance for assisting ophthalmologists in diagnosing CSCR and evaluating treatment efficacy. The manual measurement results dominated by single optical coherence tomography (OCT) B-scan image in clinical practice face the dilemma of weak reference, poor reproducibility, and experience dependence. In this context, this paper constructs two schemes: Scheme Ⅰ draws on the idea of ensemble learning, namely, integrating multiple models for locating starting key point in the height direction of lesion in the inference stage, which appropriately improves the performance of a single model. Scheme Ⅱ designs an adaptive gradient threshold (AGT) technique, followed by the construction of cascading strategy, which involves preliminary location of starting key point through deep learning, and then employs AGT for precise adjustment. This strategy not only achieves effective location for starting key point, but also significantly reduces the large appetite of deep learning model for training samples. Subsequently, AGT continues to play a crucial role in locating the terminal key point in the height direction of lesion, further demonstrating its feasibility and effectiveness. Quantitative and qualitative key point location experiments in the height direction of lesion on 1152 samples, as well as the final height measurement display, consistently conveys the superiority of the constructed schemes, especially the cascading strategy, expanding another potential tool for the comprehensive analysis of CSCR.

Acupuncture for cognitive impairment after stroke: A systematic review and meta-analysis

ObjectiveAcupuncture as an alternative therapy for post-stroke cognitive impairment (PSCI) has emerged as a research focus. The inclusion of additional external treatments in many previous studies prevents a clear, direct assessment of acupuncture's impact on PSCI. In order to prevent patients from developing hypersensitivity to other treatments and misinterpreting acupuncture's true therapeutic value, this study establish stricter intervention criteria and exclude therapies beyond acupuncture. The review aimed to offering a clearer evaluation of acupuncture's efficacy and safety in PSCI treatment. MethodsThis research involved a comprehensive search for randomized controlled trials (RCTs) across eight databases, adhering to the Cochrane Systematic Reviewer's Handbook 5.1.0 for risk-of-bias and quality assessments. A meta-analysis was conducted using RevMan 5.3 software. ResultsThe inclusion of 18 publications, totaling 1361 patients, was achieved. The meta-analysis demonstrated a significantly higher overall efficacy of acupuncture for PSCI compared to controls (OR = 4.06, 95 % CI 2.86–5.76, Z = 7.82). Notable statistical differences were observed in the Montreal Cognitive Assessment scores (MD = 2.32, 95 % CI 1.68–2.97, Z = 7.10) and the Mini-Mental State Examination scores (MD = 2.02, 95 % CI 1.06–2.98, Z = 4.13) between the groups. Improvements in the Barthel Index scores were noted for the experimental group (MD = 5.70, 95 % CI 4.68–6.72, Z = 10.92). ConclusionIntegrating acupuncture with Western medications offers significant benefits for treating PSCI over Western medications alone. However, the long-term efficacy of acupuncture in PSCI treatment and its potential in reducing recurrence rates remain undetermined. Further high-standard RCTs are essential to explore acupuncture's effectiveness in PSCI treatment more thoroughly.

Echocardiographic Features in Patient Rheumatic Mitral Stenosis

Rheumatic mitral stenosis (RMS) is a progressive valvular heart disease that can lead to significant morbidity and mortality, especially in developing countries. Echocardiography plays a crucial role in the diagnosis, assessment, and management of patients with RMS. This study aims to review the utility echocardiography feature in patient RMS. We discuss the key echocardiographic parameters used for assessment and severity of RMS, and associated valvular abnormalities. Additionally, we explore the role of multimodality imaging, such as transesophageal echocardiography in enhancing the diagnostic accuracy and therapeutic decision-making in RMS. A thorough understanding of echocardiographic findings in RMS is essential for clinicians involved in the care of these patients, as it aids in risk stratification, treatment planning, and monitoring of disease progression. Integrating echocardiography is a widely used non-invasive method for comprehensive assessment, monitoring disease progression and evaluating treatment efficacy, ensuring optimal management for patient with RMS. Keyword: Echocardiography, Rheumatic Heart Disease, Mitral Stenosis.

Modelling and simulation of anisotropic growth in brain tumours through poroelasticity: A study of ventricular compression and therapeutic protocols

AbstractMalignant brain tumours represent a significant medical challenge due to their aggressive nature and unpredictable locations. The growth of a brain tumour can result in a mass effect, causing compression and displacement of the surrounding healthy brain tissue and possibly leading to severe neurological complications. In this paper, we propose a multiphase mechanical model for brain tumour growth that quantifies deformations and solid stresses caused by the expanding tumour mass and incorporates anisotropic growth influenced by brain fibres. We employ a sharp interface model to simulate localised, non-invasive solid brain tumours, which are those responsible for substantial mechanical impact on the surrounding healthy tissue. By using patient-specific imaging data, we create realistic three-dimensional brain geometries and accurately represent ventricular shapes, to evaluate how the growing mass may compress and deform the cerebral ventricles. Another relevant feature of our model is the ability to simulate therapeutic protocols, facilitating the evaluation of treatment efficacy and guiding the development of personalized therapies for individual patients. Overall, our model allows to make a step towards a deeper analysis of the complex interactions between brain tumours and their environment, with a particular focus on the impact of a growing cancer on healthy tissue, ventricular compression, and therapeutic treatment.

Automated 3D Perioral Landmark Detection Using High-Resolution Network: Artificial Intelligence-Based Anthropometric Analysis.

3D facial stereophotogrammetry, as a convenient, non-invasive and highly reliable evaluation tool, has shown great potential in pre-operative planning and treatment efficacy evaluation of plastic surgery in recent years. However, it requires manual identification of facial landmarks by trained evaluators to obtain anthropometric data, which consumes large amount of time and effort. Automatic 3D facial landmark localization may facilitate fast data acquisition and eliminate evaluator error. In this paper, we propose a novel deep-learning method based on dimension-transformation and key-point detection for automated 3D perioral landmark annotation. The 3D facial model is transformed into 2D images on which High-Resolution Network is implemented for key point detection. The 2D coordinates of key points are then mapped back to the 3D model using mathematical methods to obtain the 3D landmark coordinates. This program was trained with 120 facial models and validated in 50 facial models. Our approach achieved satisfactory accuracy of 1.30 ± 0.68 mm error in landmark detection with an average processing time of 5.2 ± 0.21 seconds per model. And subsequent analysis based on these landmarks showed an error of 0.87 ± 1.02 mm for linear measurements and 5.62 ± 6.61° for angular measurements. This automated 3D perioral landmarking method could serve as an effective tool that enables fast and accurate anthropometric analysis of lip morphology for plastic surgery and aesthetic procedures.

E025 From risk to reward: a case of systemic antineutrophilic cytoplasmic antibody associated vasculitis

Abstract Background/Aims The background and aims of this case involve a 70-year-old female patient with a history of complex medical conditions, including bronchiectasis and dementia, who initially presented with respiratory symptoms and fever. The primary aim was to understand the underlying cause of her deteriorating condition, which did not respond to initial antibiotic treatment for suspected pneumonia. Subsequent diagnostic efforts, including extensive vasculitis workup, were undertaken to identify the source of her symptoms, which led to the discovery of positive PR3 results and elevated inflammatory markers. The key goal was to determine the most effective treatment strategy, considering the patient’s unique medical history, balancing the need for immunosuppressive therapy against the potential risks of exacerbating underlying infections. The case ultimately aimed to demonstrate the successful management of the patient’s complex clinical presentation through a multidisciplinary approach, including immunosuppressive therapy and supportive care, resulting in significant improvements in her neurological and functional status. Methods We conducted a comprehensive assessment of a 70-year-old patient with ANCA-associated vasculitis (AAV) involving the central nervous system (CNS) and cutaneous manifestations. Lab tests targeted PR3-positive ANCA, and imaging assessed disease extent. A multidisciplinary team designed a tailored treatment plan with cyclophosphamide, closely monitoring progress. Ethical considerations were adhered to, and a literature review provided context. Data analysis evaluated treatment efficacy, leading to a discussion of lessons learned in managing complex AAV cases. Results We documented the remarkable trajectory of a 70-year-old patient with ANCA-associated vasculitis (AAV) presenting with central nervous system (CNS) involvement and cutaneous manifestations. With a meticulous and tailored treatment plan involving the judicious use of cyclophosphamide, we witnessed a profound recovery marked by significant improvements in both motor and cognitive function. This case underscores the importance of balancing potent immunosuppression with the imperative to curb disease progression and mitigate treatment-associated repercussions in complex AAV presentations. The patient’s restoration to baseline functioning serves as a compelling testament to the potential of precision medicine in managing intricate clinical scenarios of AAV. Our findings emphasize the need for continued awareness and consideration of individualized treatment approaches in similar cases, offering hope and insights for improved outcomes in challenging AAV presentations. Conclusion This case offers valuable lessons on the complexities and challenges involved in diagnosing and managing a multi-faceted condition like AAV. It emphasizes the urgent need for a timely and accurate diagnosis, especially when presented with atypical cases involving the central nervous system and cutaneous manifestations. Moreover, the case enriches our understanding of AAV and contributes insights into its nuanced management. It calls for a sophisticated, patient-centered approach, informed by cutting-edge research, to optimize patient outcomes in this challenging clinical scenario. Disclosure A.J. AlZeera: None. D. Gurdeep: None.

Comparison of Scheimpflug Corneal Tomography and Anterior Segment Optical Coherence Tomography Measurements in Corneal Cystinosis: A Case Series.

To report the clinical course and compare the utility of Scheimpflug tomography (ST) and anterior segment optical coherence tomography (AS-OCT) for central corneal thickness (CCT) and corneal densitometry (CD) assessment in patients with corneal crystals owing to nephropathic cystinosis. A retrospective chart analysis of three patients with nephropathic cystinosis and the presence of corneal cystine crystals in both eyes was performed. All patients underwent clinical examination and anterior segment photography, ST, and AS-OCT scans. Corneal densitometry was exported from built-in proprietary software for ST and from custom-made validated software for AS-OCT. Anterior segment optical coherence tomography images were rescaled to grayscale units from 0 (maximum transparency) to 100 (minimum transparency) to match built-in ST densitometry readings. Furthermore, the mean pixel intensity, representative of CD, was calculated from the pixels corresponding to the segmented cornea. All three patients had pathognomonic cystine crystals deposits in the cornea and were treated with cysteamine medications that resulted in clinical improvement. The CCT measured using ST exhibited a range from 560 to 958 μm. Conversely, when assessed with AS-OCT, the CCT varied within the range of 548 to 610 μm. Both examinations could be performed, but in the more severe cases, AS-OCT showed far greater utility to estimate CD. In four of six eyes examined, ST showed disproportionate CCT values, compared with the AS-OCT, whereas reliable CD measurements were only available in AS-OCT. The AS-OCT could be considered a baseline ocular measurement in cystinosis and in the evaluation of disease progression and treatment efficacy.

Validation of Biomarkers and Patient-Reported Outcome Measures to Improve the Diagnosis and Treatment of Eustachian Tube Dysfunction

Patients who suffer from Eustachian tube dysfunction (ETD) experience a significant decline in quality of life. To enhance patient outcomes, it is crucial to establish an accurate diagnosis, assess treatment effectiveness, and evaluate the severity of the condition. Substantial progress has recently been made in identifying biomarkers (BMs) and developing ETD clinical outcome assessments (COAs). This study examines the current state of BMs and COAs in ETD, including the methods evaluated historically and their potential clinical applications. The research in this area emphasizes the importance of using objective measures, such as middle ear pressure (MEP) and patient-reported outcome measures (PROMs), for diagnosing and evaluating ETD. Further, I discuss how electronic PROMs (ePROMs) and other digital health technologies can guide the development of innovative PROMs that ETD. Despite significant advances in COAs for ETD, much work remains to validate and improve them before their implementation in clinical practice. There has been less focus on vertigo as a symptom in recent assessments of ETD, suggesting that further examination of this aspect is needed. By validating BMs and COAs for ETD, we could enhance long-term patient outcomes and quality of life through improved diagnostic accuracy and evaluation of treatment efficacy.

Non-invasive efficacy assessment of pulsed dye laser and photodynamic therapy for port-wine stain.

Port wine stain (PWS) is a congenital vascular malformation that commonly occurs on the face and neck. Currently, the main treatments for port wine stain are pulsed dye laser (PDL) and photodynamic therapy (PDT). However, the efficacy evaluation of PWS mostly relies on the subjective judgement of clinicians, and it is difficult to accurately respond to many small changes after treatment. Therefore, some non-invasive and efficient efficacy assessment methods are also needed. With the continuous development of technology, there are currently many visualisation instruments to evaluate PWS, including dermoscopy, VISIA-CR™ system, reflectance confocal microscopy (RCM), high-frequency ultrasound (HFUS), optical coherence tomography (OCT), Photoacoustic imaging (PAI), laser speckle imaging (LSI) and laser Doppler imaging (LDI). Among them, there are simple and low-cost technologies such as dermoscopy and the VISIA-CR™ system, but they may not be able to observe the deeper structures of PWS. At this time, combining techniques such as HFUS and OCT to increase penetration depth is crucial to evaluate PWS. In the future, the combination of these different technologies could help overcome the limitations of a single technology. This article provides a systematic overview of non-invasive methods for evaluating treatment efficacy in port wine stains and summarises their advantages and disadvantages.