Non-viral delivery of nucleic acid for treatment of rare diseases of the muscle.

Rare muscular disorders (RMDs) are disorders that affect a small percentage of the population. The disorders which are attributed to genetic mutations often manifest in the form of progressive weakness and atrophy of skeletal and heart muscles. RMDs includes disorders such as Duchenne muscular dystrophy (DMD), GNE myopathy, spinal muscular atrophy (SMA), limb girdle muscular dystrophy, and so on. Due to the infrequent occurrence of these disorders, development of therapeutic approaches elicits less attention compared with other more prevalent diseases. However, in recent times, improved understanding of pathogenesis has led to greater advances in developing therapeutic options to treat such diseases. Exon skipping, gene augmentation, and gene editing have taken the spotlight in drug development for rare neuromuscular disorders. The recent innovation in targeting and repairing mutations with the advent of CRISPR technology has in fact opened new possibilities in the development of gene therapy approaches for these disorders. Although these treatments show satisfactory therapeutic effects, the susceptibility to degradation, instability, and toxicity limits their application. So, an appropriate delivery vector is required for the delivery of these cargoes. Viral vectors are considered potential delivery systems for gene therapy; however, the associated concurrent immunogenic response and other limitations have paved the way for the applications of other non-viral systems like lipids, polymers, cellpenetrating peptides (CPPs), and other organic and inorganic materials. This review will focus on non-viral vectors for the delivery of therapeutic cargoes in order to treat muscular dystrophies.

Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study.

BACKGROUND/OBJECTIVES: An affordable and scalable screening model is critical for undetected glaucoma. The study evaluated the performance of an offline, smartphone-based AI system for the detection of referable glaucoma against two benchmarks: specialist diagnosis following full glaucoma workup and consensus image grading. SUBJECTS/METHODS: This prospective study (tertiary glaucoma centre, India) included 243 subjects with varying severity of glaucoma and control group without glaucoma. Disc-centred images were captured using a validated smartphone-based fundus camera analysed by the AI system and graded by specialists. Diagnostic ability of the AI in detecting referable Glaucoma (Confirmed glaucoma) and no referable Glaucoma (Suspects and No glaucoma) when compared to a final diagnosis (comprehensive glaucoma workup) and majority grading (image grading) by Glaucoma specialists (pre-defined criteria) were evaluated. RESULTS: The AI system demonstrated a sensitivity and specificity of 93.7% (95% CI: 87.6-96.9%) and 85.6% (95% CI:78.6-90.6%), respectively, in the detection of referable glaucoma when compared against final diagnosis following full glaucoma workup. True negative rate in definite non-glaucoma cases was 94.7% (95% CI: 87.2-97.9%). Amongst the false negatives were 4 early and 3 moderate glaucoma. When the same set of images provided to the AI was also provided to the specialists for image grading, specialists detected 60% (67/111) of true glaucoma cases versus a detection rate of 94% (104/111) by the AI. CONCLUSION: The AI tool showed robust performance when compared against a stringent benchmark. It had modest over-referral of normal subjects despite being challenged with fundus images alone. The next step involves a population-level assessment.

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population.

Purpose: The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP). Participants: Images were collected from infants enrolled in the KIDROP tele-ROP screening program. Methods: We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1-3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist. Results: Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%-92.59%) and 91.22% (95% CI: 90.42%-91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%-83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%-96.61%) and the AUROC was 0.970. Conclusion: The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.

Bioinspired synthesis of bioactive glass nanocomposites for hyaluronic acid delivery to bone and skin.

In this study, we present nanocomposites of bioactive glass (BG) and hyaluronic acid (HA) (nano-BGHA) for effective delivery of HA to skin and bone. The synthesis of the nanocomposites has been carried out through the bio-inspired method, which is a modification of the traditional Stober's synthesis as it avoids using ethanol, ammonia, synthetic surfactants, or high-temperature calcination. This environmentally friendly, bio-inspired route allowed the synthesis of mesoporous nanocomposites with an average hydrodynamic radius of ∼190 nm and an average net surface charge of ∼-21 mV. Most nanocomposites are amorphous and bioactive in nature with over 70 % cellular viability for skin and bone cell lines even at high concentrations, along with high cellular uptake (90-100 %). Furthermore, the nanocomposites could penetrate skin cells in a transwell set-up and artificial human skin membrane (StratM®), thus depicting an attractive strategy for the delivery of HA to the skin. The purpose of the study is to develop nanocomposites of HA and BG that can have potential applications in non-invasive treatments that require the delivery of high molecular weight HA such as in the case of osteoarthritis, sports injury treatments, eye drops, wound healing, and some anticancer treatments, if further investigated. The presence of BG further enhances the range to bone-related applications. Additionally, the nanocomposites can have potential cosmeceutical applications where HA is abundantly used, for instance in moisturizers, dermal fillers, shampoos, anti-wrinkle creams, etc.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.

INTRODUCTION: Numerous studies have demonstrated the use of artificial intelligence (AI) for early detection of referable diabetic retinopathy (RDR). A direct comparison of these multiple automated diabetic retinopathy (DR) image assessment softwares (ARIAs) is, however, challenging. We retrospectively compared the performance of two modern ARIAs, IDx-DR and Medios AI. METHODS: In this retrospective-comparative study, retinal images with sufficient image quality were run on both ARIAs. They were captured in 811 consecutive patients with diabetes visiting diabetic clinics in Poland. For each patient, four non-mydriatic images, 45° field of view, i.e., two sets of one optic disc and one macula-centered image using Topcon NW400 were captured. Images were manually graded for severity of DR as no DR, any DR (mild non-proliferative diabetic retinopathy [NPDR] or more severe disease), RDR (moderate NPDR or more severe disease and/or clinically significant diabetic macular edema [CSDME]), or sight-threatening DR (severe NPDR or more severe disease and/or CSDME) by certified graders. The ARIA output was compared to manual consensus image grading (reference standard). RESULTS: On 807 patients, based on consensus grading, there was no evidence of DR in 543 patients (67%). Any DR was seen in 264 (33%) patients, of which 174 (22%) were RDR and 41 (5%) were sight-threatening DR. The sensitivity of detecting RDR against reference standard grading was 95% (95% CI: 91, 98%) and the specificity was 80% (95% CI: 77, 83%) for Medios AI. They were 99% (95% CI: 96, 100%) and 68% (95% CI: 64, 72%) for IDx-DR, respectively. CONCLUSION: Both the ARIAs achieved satisfactory accuracy, with few false negatives. Although false-positive results generate additional costs and workload, missed cases raise the most concern whenever automated screening is debated.

Bioreducible Gene Delivery Platform that Promotes Intracellular Payload Release and Widespread Brain Dispersion.

We here introduce a novel bioreducible polymer-based gene delivery platform enabling widespread transgene expression in multiple brain regions with therapeutic relevance following intracranial convection-enhanced delivery. Our bioreducible nanoparticles provide markedly enhanced gene delivery efficacy in vitro and in vivo compared to nonbiodegradable nanoparticles primarily due to the ability to release gene payloads preferentially inside cells. Remarkably, our platform exhibits competitive gene delivery efficacy in a neuron-rich brain region compared to a viral vector under previous and current clinical investigations with demonstrated positive outcomes. Thus, our platform may serve as an attractive alternative for the intracranial gene therapy of neurological disorders.

Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center.

Accurate classification of laryngeal cancer is a critical step for diagnosis and appropriate treatment. Radiomics is a rapidly advancing field in medical image processing that uses various algorithms to extract many quantitative features from radiological images. The high dimensional features extracted tend to cause overfitting and increase the complexity of the classification model. Thereby, feature selection plays an integral part in selecting relevant features for the classification problem. In this study, we explore the predictive capabilities of radiomics on Computed Tomography (CT) images with the incidence of laryngeal cancer to predict the histopathological grade and T stage of the tumour. Working with a pilot dataset of 20 images, an experienced radiologist carefully annotated the supraglottic lesions in the three-dimensional plane. Over 280 radiomic features that quantify the shape, intensity and texture were extracted from each image. Machine learning classifiers were built and tested to predict the stage and grade of the malignant tumour based on the calculated radiomic features. To investigate if radiomic features extracted from CT images can be used for the classification of laryngeal tumours. Out of 280 features extracted from every image in the dataset, it was found that 24 features are potential classifiers of laryngeal tumour stage and 12 radiomic features are good classifiers of histopathological grade of the laryngeal tumor. The novelty of this paper lies in the ability to create these classifiers before the surgical biopsy procedure, giving the clinician valuable, timely information.

The Urgent Need for Healthcare Workforce Upskilling and Ethical Considerations in the Era of AI-Assisted Medicine.

This letter is in response to the article "Enhancing India's Health Care during COVID Era: Role of Artificial Intelligence and Algorithms". While the integration of AI has the potential to improve patient outcomes and reduce the workload of healthcare professionals, there is a need for significant training and upskilling of healthcare providers. There are ethical and privacy concerns related to the use of AI in healthcare, which must be accompanied by rigorous guidelines. One solution to the overburdened healthcare systems in India is the use of new language generation models like ChatGPT to assist healthcare workers in writing discharge summaries. By using these technologies responsibly, we can improve healthcare outcomes and alleviate the burden on overworked healthcare professionals.

Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning.

This study aims to investigate public sentiment on laryngeal cancer via tweets in 2022 using machine learning. We aimed to analyze the public sentiment about laryngeal cancer on Twitter last year. A novel dataset was created for the purpose of this study by scraping all tweets from 1st Jan 2022 that included the hashtags #throatcancer, #laryngealcancer, #supraglotticcancer, #glotticcancer, and #subglotticcancer in their text. After all tweets underwent a fourfold data cleaning process, they were analyzed using natural language processing and sentiment analysis techniques to classify tweets into positive, negative, or neutral categories and to identify common themes and topics related to laryngeal cancer. The study analyzed a corpus of 733 tweets related to laryngeal cancer. The sentiment analysis revealed that 53% of the tweets were neutral, 34% were positive, and 13% were negative. The most common themes identified in the tweets were treatment and therapy, risk factors, symptoms and diagnosis, prevention and awareness, and emotional impact. This study highlights the potential of social media platforms like Twitter as a valuable source of real-time, patient-generated data that can inform healthcare research and practice. Our findings suggest that while Twitter is a popular platform, the limited number of tweets related to laryngeal cancer indicates that a better strategy could be developed for online communication among netizens regarding the awareness of laryngeal cancer.

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.

PRCIS: The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts. PURPOSE: The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera. METHODS: In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists. RESULTS: We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08). CONCLUSIONS: The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.

Validation of a simple-to-use, affordable, portable, wavefront aberrometry-based auto refractometer in the adult population: A prospective study.

BACKGROUND: Refraction is one of the key components of a comprehensive eye examination. Auto refractometers that are reliable and affordable can be beneficial, especially in a low-resource community setting. The study aimed to validate the accuracy of a novel wave-front aberrometry-based auto refractometer, Instaref R20 against the open-field system and subjective refraction in an adult population. METHODS: All the participants underwent a comprehensive eye examination including objective refraction, subjective acceptance, anterior and posterior segment evaluation. Refraction was performed without cycloplegia using WAM5500 open-field auto refractometer (OFAR) and Instaref R20, the study device. Agreement between both methods was evaluated using Bland-Altman analysis. The repeatability of the device based on three measurements in a subgroup of 40 adults was assessed. RESULTS: The refractive error was measured in 132 participants (mean age,30.53 ± 9.36 years, 58.3% female). The paired mean difference of the refraction values of the study device against OFAR was - 0.13D for M, - 0.0002D (J0) and - 0.13D (J45) and against subjective refraction (SR) was - 0.09D (M), 0.06 (J0) and 0.03D (J45). The device agreed within +/- 0.50D of OFAR in 78% of eyes for M, 79% for J0 and 78% for J45. The device agreed within +/- 0.5D of SR values for M (84%), J0 (86%) and J45 (89%). CONCLUSION: This study found a good agreement between the measurements obtained with the portable autorefractor against open-field refractometer and SR values. It has a potential application in population-based community vision screening programs for refractive error correction without the need for highly trained personnel.

Validation of a Simple-to-Use, Affordable, Portable Wavefront Aberrometry-Based Auto Refractometer in a Paediatric Population.

Purpose: InstaRef R20 is a handheld, affordable auto refractometer based on Shack Hartmann aberrometry technology. The study's objective was to compare InstaRef R20's performance for identifying refractive error in a paediatric population to that of standard subjective and objective refraction under both pre- and post-cycloplegic conditions. Methods: Refraction was performed using 1) standard clinical procedure consisting of retinoscopy followed by subjective refraction (SR) under pre- and post-cycloplegic conditions and 2) InstaRef R20. Agreement between both methods was evaluated using Bland-Altman analysis. The repeatability of the device based on three measurements in a subgroup of 20 children was assessed. Results: The refractive error was measured in 132 children (mean age 12.31 ± 3 years). The spherical equivalent (M) and cylindrical components (J0 and J45) of the device had clinically acceptable differences (within ±0.50D) and acceptable agreement compared to standard pre- and post-cycloplegic manual retinoscopy and subjective refraction (SR). The device agreed within ± 0.50D of retinoscopy in 67% of eyes for M, 78% for J0 and 80% for J45 and within ± 0.50D of SR in 70% for M and 77% for cylindrical components. Conclusion: InstaRef R20 has an acceptable agreement compared to standard retinoscopy in paediatric population. The measurements from this device can be used as a starting point for subjective acceptance. The device being simple to use, portable, reliable and affordable has the potential for large-scale community-based refractive error detection.

Towards a Device Agnostic AI for Diabetic Retinopathy Screening: An External Validation Study.

Purpose: To evaluate the performance of a validated Artificial Intelligence (AI) algorithm developed for a smartphone-based camera on images captured using a standard desktop fundus camera to screen for diabetic retinopathy (DR). Participants: Subjects with established diabetes mellitus. Methods: Images captured on a desktop fundus camera (Topcon TRC-50DX, Japan) for a previous study with 135 consecutive patients (233 eyes) with established diabetes mellitus, with or without DR were analysed by the AI algorithm. The performance of the AI algorithm to detect any DR, referable DR (RDR Ie, worse than mild non proliferative diabetic retinopathy (NPDR) and/or diabetic macular edema (DME)) and sight-threatening DR (STDR Ie, severe NPDR or worse and/or DME) were assessed based on comparisons against both image-based consensus grades by two fellowship trained vitreo-retina specialists and clinical examination. Results: The sensitivity was 98.3% (95% CI 96%, 100%) and the specificity 83.7% (95% CI 73%, 94%) for RDR against image grading. The specificity for RDR decreased to 65.2% (95% CI 53.7%, 76.6%) and the sensitivity marginally increased to 100% (95% CI 100%, 100%) when compared against clinical examination. The sensitivity for detection of any DR when compared against image-based consensus grading and clinical exam were both 97.6% (95% CI 95%, 100%). The specificity for any DR detection was 90.9% (95% CI 82.3%, 99.4%) as compared against image grading and 88.9% (95% CI 79.7%, 98.1%) on clinical exam. The sensitivity for STDR was 99.0% (95% CI 96%, 100%) against image grading and 100% (95% CI 100%, 100%) as compared against clinical exam. Conclusion: The AI algorithm could screen for RDR and any DR with robust performance on images captured on a desktop fundus camera when compared to image grading, despite being previously optimized for a smartphone-based camera.

Automated segmentation of the larynx on computed tomography images: a review.

The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement. This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work. We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-022-00221-3.

A Novel, Smartphone-Based, Teleophthalmology-Enabled, Widefield Fundus Imaging Device With an Autocapture Algorithm.

Purpose: Widefield imaging can detect signs of retinal pathology extending beyond the posterior pole and is currently moving to the forefront of posterior segment imaging. We report a novel, smartphone-based, telemedicine-enabled, mydriatic, widefield retinal imaging device with autofocus and autocapture capabilities to be used by non-specialist operators. Methods: The Remidio Vistaro uses an annular illumination design without cross-polarizers to eliminate Purkinje reflexes. The measured resolution using the US Air Force target test was 64 line pairs (lp)/mm in the center, 57 lp/mm in the middle, and 45 lp/mm in the periphery of a single-shot retinal image. An autocapture algorithm was developed to capture images automatically upon reaching the correct working distance. The field of view (FOV) was validated using both model and real eyes. A pilot study was conducted to objectively assess image quality. The FOVs of montaged images from the Vistaro were compared with regulatory-approved widefield and ultra-widefield devices. Results: The FOV of the Vistaro was found to be approximately 65° in one shot. Automatic image capture was achieved in 80% of patient examinations within an average of 10 to 15 seconds. Consensus grading of image quality among three graders showed that 91.6% of the images were clinically useful. A two-field montage on the Vistaro was shown to exceed the cumulative FOV of a seven-field Early Treatment Diabetic Retinopathy Study image. Conclusions: A novel, smartphone-based, portable, mydriatic, widefield imaging device can view the retina beyond the posterior pole with a FOV of 65° in one shot. Translational Relevance: Smartphone-based widefield imaging can be widely used to screen for retinal pathologies beyond the posterior pole.

Design and Performance Characterization of a Novel, Smartphone-Based, Portable Digital Slit Lamp for Anterior Segment Screening Using Telemedicine.

Purpose: Telemedicine-enabled, portable digital slit lamps can help to decentralize screening to close-to-patient contexts. We report a novel design for a portable, digital slit lamp using a smartphone. It works on an advanced optical design and has the capability of instantaneous, objective photodocumentation to capture anterior segment images and is telemedicine-enabled. Methods: The device is constructed keeping its usability and the importance of design ergonomics for nonspecialized field personnel in mind. The optical design is described, and the resolution and magnification are compared with traditional desktop-based slit lamps. A Health Insurance Portability and Accountability Act (HIPAA)-compliant, patient management software is integrated to synchronize the captured images with a secure cloud server along with a sharpness algorithm to extract the best focused frames of the cornea, iris, and lens, from videos. We demonstrate its photodocumentation ability and teleophthalmology feasibility by capturing images in a pilot study from nine subjects. Results: Images were obtained in various illumination, magnification, and filter settings. Synchronous and asynchronous teleophthalmology consults were conducted. The performance of the device was shown to be limited by the smartphone sensor resolution and not the optical design, because the Air Force target resolution was found to be the same on smartphone-mounted traditional slit lamps despite a lower magnification. Conclusions: The novel, portable, digital slit lamp with advanced optical design using smartphones has the ability to screen for anterior segment pathologies using telemedicine. Translational Relevance: A portable, telemedicine-friendly, ergonomically designed, slit lamp used by nonspecialist personnel allows for both synchronous and asynchronous modes of consultation at remote locations, facilitating mass screening programs.

Identification of kinases and kinase inhibitors for the differential targeting of Wnt/ß-catenin signaling in gastric cancer subtypes.

The oncogenic signaling pathway Wnt is often activated in many cancers including gastric cancer. Wnt signaling pathway is considered as a potential target for developing new targeted therapeutics. Kinase inhibitors are the promising class of drugs for many diseases including cancers. Toward identifying the potent inhibitors targeting Wnt signaling pathway, a kinase inhibitor library with 82 inhibitors were screened using Wnt pathway reporter assay in gastric cancer cells. Notably, 34 kinase inhibitors were identified to inhibit Wnt mediated reporter activity to the extent of more than 50%. The corresponding kinase genes, which are known targets of these kinase inhibitors, were investigated for their expression in the available mRNA profiles of gastric tumors. A major group of the kinase genes showed higher expression in intestinal subtype gastric tumors. Another group of kinase genes were found expressed in diffuse type gastric tumors. The kinase genes expressed in intestinal type gastric tumors were found associated with varying survival of gastric cancer patients whereas those expressed in diffuse type tumors were found associated with the poor survival. Thus, the kinase genes specifically expressed in intestinal and diffuse type gastric tumors and the kinase inhibitors to target Wnt signaling pathway in gastric cancer subtypes have been identified.

A novel approach to anterior segment imaging with smartphones in the COVID-19 era.

PURPOSE: To report a novel, telemedicine-friendly, smartphone-based, wireless anterior segment device with instant photo-documentation ability in the COVID-19 era. METHODS: Anterior Imaging Module (AIM) was constructed based on a 50/50 beam splitter design, to match the magnification drum optics of slit-lamps with a three-step or higher level of magnification. The design fills the smartphone sensor fully at the lowest magnification and matches the fixed focus of the slit-lamp. It comes with a smartphone for instant photo-documentation, an in-built software application for data-management and secure HIPAA compliant cloud storage, and a Bluetooth trigger for a one-tap image capture. The construction of the device is explained, and the optical resolution measured using U.S. air-force resolution test. AIM's performance was characterized with traceability to internationally relevant performance standards for digital slit-lamps after image quality assessment through a pilot study. RESULTS: Clinically useful anterior segment images were obtained with both diffuse and slit illumination at different magnification settings with the highest magnification (40X) resolution of 359 lines per cm and the lowest magnification (16X) resolution of 113 lines per cm. CONCLUSION: AIM is a novel, wireless, telemedicine-enabled design that digitizes existing, analog slit lamps with at least three-step magnification. The settings ensure the focus is determined purely by the position of the slit-lamp. Hence, the image viewed and captured on the smartphone is exactly what the clinician sees through the eyepiece. This helps in maintaining distance from the patient in the ongoing COVID-19 pandemic, as well.

A Two-Pronged Pulmonary Gene Delivery Strategy: A Surface-Modified Fullerene Nanoparticle and a Hypotonic Vehicle.

Inhaled gene therapy poses a unique potential of curing chronic lung diseases, which are currently managed primarily by symptomatic treatments. However, it has been challenging to achieve therapeutically relevant gene transfer efficacy in the lung due to the presence of numerous biological delivery barriers. Here, we introduce a simple approach that overcomes both extracellular and cellular barriers to enhance gene transfer efficacy in the lung in vivo. We endowed tetra(piperazino)fullerene epoxide (TPFE)-based nanoparticles with non-adhesive surface polyethylene glycol (PEG) coatings, thereby enabling the nanoparticles to cross the airway mucus gel layer and avoid phagocytic uptake by alveolar macrophages. In parallel, we utilized a hypotonic vehicle to facilitate endocytic uptake of the PEGylated nanoparticles by lung parenchymal cells via the osmotically driven regulatory volume decrease (RVD) mechanism. We demonstrate that this two-pronged delivery strategy provides safe, wide-spread and high-level transgene expression in the lungs of both healthy mice and mice with chronic lung diseases characterized by reinforced delivery barriers.

Calcium channel blockers lercanidipine and amlodipine inhibit YY1/ERK/TGF-ß mediated transcription and sensitize the gastric cancer cells to doxorubicin.

Elevated expression of YY1 is known to confer anti-apoptotic phenotype and hence is an attractive target for cancer therapeutics. In a repurpose screening, towards the identification of the inhibitors of YY1 regulated transcription in gastric cancer cells, the calcium channel blockers lercanidipine and amlodipine have been identified to inhibit YY1 more efficiently. We further probed these calcium channel blockers for their potential feature of alleviating the drug resistance in gastric cancer cells. Lercanidipine and amlodipine were found to show an enhanced effect with doxorubicin in inhibiting the growth of gastric cancer cells. While doxorubicin was identified to activate the pathways TGF-ß and ERK/MAPK, lercanidipine was found to inhibit these pathways. This being the molecular mechanism behind the identified advantage of lercanidipine and amlodipine in sensitizing gastric cancer cells to doxorubicin. In multiple cellular models from different lineages, the cells with less sensitivity to doxorubicin were found to have the inherent activation of ERK/MAPK and TGF-ß pathways. Also, we have identified that doxorubicin, in combination with any of the calcium channel blockers, could inhibit the potential of cellular proliferation and spheroid formation in gastric cancer cells. The current study shows the usefulness of lercanidipine and amlodipine for the targeted and combinatorial therapeutics of gastric cancer and specifically to improve the efficiency of doxorubicin.