Harnessing the power of clustered regularly interspaced short palindromic repeats (CRISPR) based microfluidics for next-generation molecular diagnostics.

CRISPR-based (Clustered regularly interspaced short palindromic repeats-based) technologies have revolutionized molecular biology and diagnostics, offering unprecedented precision and versatility. However, challenges remain, such as high costs, demanding technical expertise, and limited quantification capabilities. To overcome these limitations, innovative microfluidic platforms are emerging as powerful tools for enhancing CRISPR diagnostics. This review explores the exciting intersection of CRISPR and microfluidics, highlighting their potential to revolutionize healthcare diagnostics. By integrating CRISPR's specificity with microfluidics' miniaturization and automation, researchers are developing more sensitive and portable diagnostic tools for a range of diseases. These microfluidic devices streamline sample processing, improve diagnostic performance, and enable point-of-care applications, allowing for rapid and accurate detection of pathogens, genetic disorders, and other health conditions. The review discusses various CRISPR/Cas systems, including Cas9, Cas12, and Cas13, and their integration with microfluidic platforms. It also examines the advantages and limitations of these systems, highlighting their potential for detecting DNA and RNA biomarkers. The review also explores the key challenges in developing and implementing CRISPR-driven microfluidic diagnostics, such as ensuring robustness, minimizing cross-contamination, and achieving robust quantification. Finally, it highlights potential future directions for this rapidly evolving field, emphasizing the transformative potential of these technologies for personalized medicine and global health.

Endovascular Treatment of May-Thurner Syndrome in an Office-Based Laboratory.

May-Thurner syndrome (MTS) is a rare condition that increases the risk of left-sided iliofemoral venous thrombosis due to compression of the left common iliac vein by the right common iliac artery. Treatment for symptomatic MTS typically includes combined anticoagulation and endovascular therapy. This patient presented to the emergency department with acute left lower extremity pain and swelling. After imaging confirmed MTS, the patient was discharged from the ED and expeditiously treated in an office-based lab (OBL) setting with venous thrombectomy, angioplasty, and stenting. The setting where endovascular therapy is performed may significantly impact access to care for patients. Additionally, cost-effectiveness is a factor that should be considered when deciding the treatment site of service. We demonstrate the safety and cost-viability of performing venous thrombectomy, angioplasty, and stenting in an outpatient setting for the treatment of acute iliofemoral venous thrombosis.

Lab-in-a-Vial Rapid Test for Internet of Things-Embedded Point-of-Healthcare Protein Biomarker Detection in Bodily Fluids.

Amateurs often struggle with detecting and quantifying protein biomarkers in body fluids due to the high expertise required. This study introduces a Lab-in-a-Vial (LV) rapid diagnostic platform, featuring hydrangea-like platinum nanozymes (PtNH), for rapid, accurate detection and quantification of protein biomarkers on-site within 15 min. This method significantly enhances detection sensitivity for various biomarkers in body fluids, surpassing traditional methods such as enzyme-linked immunosorbent assays (ELISA) and lateral flow assays (LFA) by ≈250 to 1300 times. The LV platform uses a glass vial coated with specific bioreceptors such as antigens or antibodies, enabling rapid in vitro evaluation of disease risk from small fluid samples, similar to a personal ELISA-like point-of-care test (POCT). It overcomes challenges in on-site biomarker detection, allowing both detection and quantification through a portable wireless spectrometer for healthcare internet of things (H-IoT). The platform's effectiveness and adaptability are confirmed using IgG/IgM antibodies from SARS-CoV-2 infected patients and nuclear matrix protein (NMP22) from urothelial carcinoma (UC) patients as biomarkers. These tests demonstrated its accuracy and flexibility. This approach offers vast potential for diverse disease applications, provided that the relevant protein biomarkers in bodily fluids are identified.

Design and implementation of a lab-on-a-chip for assisted reproductive technologies.

Introduction: The microfluidic device is highly optimized to remove oocytes from the cumulus-corona cell mass surrounding them. Additionally, it effectively captures and immobilizes the oocytes, aiding in assessing their quality and facilitating the injection of sperm into the oocyte. In this study, a novel microfluidic chip was designed and manufactured using conventional soft lithography methods. Methods: This research proposes the utilization of a microfluidic chip as a substitute for the conventional manual procedures involved in oocyte denudation, trapping, and immobilization. The microfluidic chip was modeled and simulated using COMSOL Multiphysics® 5.2 software to optimize and enhance its design and performance. The microfluidic chip was fabricated using conventional injection molding techniques on a polydimethylsiloxane substrate by employing soft lithography methods. Results: A hydrostatic force was applied to guide the oocyte through predetermined pathways to eliminate the cumulus cells surrounding the oocyte. The oocyte was subsequently confined within the designated trap region by utilizing hydraulic resistance along the paths and immobilized by applying vacuum force. Conclusion: The application of this chip necessitates a lower level of operator expertise compared to enzymatic and mechanical techniques. Moreover, it is feasible to continuously monitor the oocyte's state throughout the procedure. There is a reduced need for cultural media compared to more standard approaches.

Ergonomic design of computer laboratory furniture: Mismatch analysis utilizing anthropometric data of university students.

Many studies have shown that ergonomically designed furniture improves productivity and well-being. As computers have become a part of students' academic lives, they will continue to grow in the future. We propose anthropometric-based furniture dimensions that are suitable for university students to improve computer laboratory ergonomics. We collected data from 380 participants and analyzed 11 anthropometric measurements, correlating them with 11 furniture dimensions. Two types of furniture were found and studied in different university computer laboratories: (1) a non-adjustable chair with a non-adjustable table and (2) an adjustable chair with a non-adjustable table. The mismatch calculation showed a significant difference between existing furniture dimensions and anthropometric measurements, indicating that 7 of the 11 existing furniture dimensions need improvement. The one-way ANOVA test with a significance level of 5% also showed a significant difference between the anthropometric data and existing furniture dimensions. All 11 dimensions were determined to match students' anthropometric data. The proposed dimensions were found to be more compatible and showed reduced mismatch percentages for nine furniture dimensions and nearly zero mismatches for seat width, backrest height, and under the hood for both males and females compared to the existing furniture dimensions. The proposed dimensions of the furniture set with adjustable seat height showed slightly improved match results for seat height and seat-to-table clearance, which showed zero mismatches compared with the non-adjustable furniture set. The table width and table depth dimensions were suggested according to Barnes and Squires' ergonomic work envelope model, considering hand reach. The positions of the keyboard and mouse are also suggested according to the work envelope. The monitor position and viewing angle were proposed according to OSHA guidelines. This study suggests that the proposed dimensions can improve comfort levels, reducing the risk of musculoskeletal disorders among students. Further studies on the implementation and long-term effects of the proposed dimensions in real-world computer laboratory settings are recommended.

Integrating machine learning and biosensors in microfluidic devices: A review.

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces the necessity of locally controlling the variables involved in the phenomena under investigation. For this reason, the literature has deeply explored the possibility of introducing sensing elements to investigate the physical quantities and the biochemical concentration inside microfluidic devices. Biosensors, particularly, are well known for their high accuracy, selectivity, and responsiveness. However, their signals could be challenging to interpret and must be carefully analysed to carry out the correct information. In addition, proper data analysis has been demonstrated even to increase biosensors' mentioned qualities. To this regard, machine learning algorithms are undoubtedly among the most suitable approaches to undertake this job, automatically learning from data and highlighting biosensor signals' characteristics at best. Interestingly, it was also demonstrated to benefit microfluidic devices themselves, in a new paradigm that the literature is starting to name "intelligent microfluidics", ideally closing this benefic interaction among these disciplines. This review aims to demonstrate the advantages of the triad paradigm microfluidics-biosensors-machine learning, which is still little used but has a great perspective. After briefly describing the single entities, the different sections will demonstrate the benefits of the dual interactions, highlighting the applications where the reviewed triad paradigm was employed.

Interprofessional Modules for Teaching Skills in Skill Labs.

Clinical skills laboratories are educational facilities that have the potential benefit for undergraduate and postgraduate medical students and medical staff. They provide a safe and protected environment in which the learner can practice clinical skills before using them in real clinical settings. These skills laboratories help to ensure that all students acquire the necessary techniques and are properly assessed before practicing on real patients. In addition, they support the acquisition, maintenance and enhancement of the clinical skills of students in the healthcare profession. Teaching skills in skill lab require a standardized module for each skill that can be applied to all the students universally. The study was started with the aim to solve a problem that has arisen as National Medical Council (NMC) has announced implementation of Competency based medical education (CBME) a new syllabus for medical education from 2019 MBBS batch that includes skill teaching. Faculty in various Medical colleges is not trained in developing modules and teaching skills to students in skill lab. There are no standardized teaching modules for skills training in skill laboratories. As part of the project we decided to develop modules for the two competencies to be taught in the skill lab for the subject Otorhinolaryngology i.e. Otoscopy and Anterior Nasal packing; NMC guidelines being the benchmark in developing these modules. Later modules were also to be implemented on sample students to get a feedback on the structure of the module for further improvement. These modules developed as part of the project will help as a baseline tool for developing other modules in different subjects by various Institutes. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-024-04568-0.

Neuro-XAI: Explainable Deep Learning Framework based on DeeplabV3+ and Bayesian Optimization for Segmentation and Classification of Brain Tumor in MRI Scans.

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97% classification accuracy and a 0.98 global accuracy.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry.

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 µm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the 1.5-10 µm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.

The harms of promoting the lab leak hypothesis for SARS-CoV-2 origins without evidence.

Science is humanity's best insurance against threats from nature, but it is a fragile enterprise that must be nourished and protected. The preponderance of scientific evidence indicates a natural origin for SARS-CoV-2. Yet, the theory that SARS-CoV-2 was engineered in and escaped from a lab dominates media attention, even in the absence of strong evidence. We discuss how the resulting anti-science movement puts the research community, scientific research, and pandemic preparedness at risk.

Expansion of WallStents® After Initial Deployment in Nonthrombotic Iliac Vein Lesions.

OBJECTIVE: To determine the structural changes of Wallstents® (Boston Scientific, Natick, MA) in vivo following deployment in iliac veins. METHODS: This retrospective single-center study was performed from September 2012 to April 2013 and included 100 office-based patients who underwent initial stent placement for nonthrombotic iliac vein lesions with Wallstent® as well as a second procedure for stenting of the contralateral iliac vein. Measurements were obtained with marker balloons and the diameters of the stents were compared at the time of the index procedure to the secondary procedure. RESULTS: The average time between the two procedures was 28 days (range 3-237, SD ± 39.89). The overall average stent diameter after the index procedure was 16.38 mm (range 10.95-21.45, SD ± 2.24). The overall average stent diameter of the index stent when remeasured during the second intervention was 17.58 mm (range 12.84-24.11, SD ± 2.38, p=0.0003) which was significantly different from the initial measurements. There was no difference when comparing changes in stent diameter by gender or laterality of procedure. However, there was a significant difference in expansion of stents when placed in the common iliac vein vs the external iliac or common femoral veins. CONCLUSIONS: This study shows that self-expanding Wallstents® can continue to expand days to weeks in vivo following initial deployment. Additionally, we found that the change in diameter from initial placement to follow up was more significant in stents placed in the proximal and middle segments of the common iliac vein. CLINICAL RELEVANCE: Wallstents® are durable implants designed to last within a patient for the rest of their life, it is important to understand the structural changes occurring after their placement. This study allows for a better understanding of Wallstent® dynamics in vivo.

MANGOU (Miyazaki Advanced New General Surgery of University) Wet Lab Training Relieves Anxiety About Surgical Skills in Surgical Education: A Cross-Sectional Study.

PURPOSE: To increase the number of medical students or residents who want to become surgeons, we must evaluate our program that recruits new young surgeons. METHODS: We planned surgical training programs for medical students and residents that we named the MANGOU (Miyazaki Advanced New General surgery Of University) training project in the Department of Surgery, Miyazaki University, Japan. From January 2016 through December 2022, we asked trainees who attended this training to complete questionnaires to evaluate their interest in surgery, confidence in surgical skills, and training. Scoring of the questionnaire responses was based on a 5-point Likert scale, and we evaluated this training prospectively. RESULTS: Among the 109 trainees participating in this training, 61 answered the questionnaires. Two participants found the training boring, but 59 (96.7%) enjoyed it. All of them answered "Yes" to wanting to participate in the next training. Respective pre- and post-training scores were as follows: confidence in surgical skills, 2.2 ± 1.0 and 3.0 ± 1.0 (p < 0.0001); interest in surgery, 4.2 ± 0.8 and 4.4 ± 0.5 (p = 0.0011); and willingness to become surgeons, 3.9 ± 0.7 and 4.1 ± 0.6 (p = 0.0011). All scores rose after MANGOU training. CONCLUSION: We planned MANGOU surgical wet lab training for medical students and residents that aimed to educate and recruit new surgeons. After joining the MANGOU training, the trainees' anxiety about surgery was reduced, their confidence in performing surgical procedures improved, they showed more interest in surgery, and they increased their motivation to become surgeons.

Treating Chronic Iliac Vein Stent Occlusion in an Office-Based Lab Setting.

Iliac vein stenting is performed when sufficient venous patency is not achieved via angioplasty or lysis. Iliac vein stenting is known to be effective; however, occlusion of the stent occurs occasionally. There is a lack of effective treatment options for those with failed prior venous stents, and traditional methods may involve the removal of the stent and surgical reconstruction. We present a patient with a right leg post-thrombotic syndrome and narcotic abuse after occlusion of a previously placed right common iliac/external iliac vein stent 25 years prior. After transfer to an office-based lab (OBL), femoral vein access was achieved. Then, a second stent was deployed adjacent to the previously chronically thrombosed stent. Imaging confirmed adequate deployment of the new stent and venous flow. Treatment resulted in a significant decrease in patient pain and cessation of narcotics. We demonstrate successful recanalization of a right iliac vein thrombosis via parallel deployment of a stent adjacent to a chronically thrombosed stent.