Citations

Infectious diseases contribute significantly to the global disease burden. Sensitive and accurate screening methods are some of the most effective means of identifying sources of infection and controlling infectivity. Conventional detecting strategies such as quantitative polymerase chain reaction (qPCR), DNA sequencing, and mass spectrometry typically require bulky equipment and well-trained personnel. Therefore, mass screening of a large population using conventional strategies during pandemic periods often requires additional manpower, resources, and time, which cannot be guaranteed in resource-limited settings. Recently, emerging microfluidic technologies have shown the potential to replace conventional methods in performing point-of-care detection because they are automated, miniaturized, and integrated. By exploiting the spatial separation of detection sites, microfluidic platforms can enable the multiplex detection of infectious diseases to reduce the possibility of misdiagnosis and incomplete diagnosis of infectious diseases with similar symptoms. This review presents the recent advances in microfluidic platforms used for multiplex detection of infectious diseases, including microfluidic immunosensors and microfluidic nucleic acid sensors. As representative microfluidic platforms, lateral flow immunoassay (LFIA) platforms, polymer-based chips, paper-based devices, and droplet-based devices will be discussed in detail. In addition, the current challenges, commercialization, and prospects are proposed to promote the application of microfluidic platforms in infectious disease detection.

Abstract Aim Perianal Paget's disease (PAPD) is a rare disorder with a predisposition to anal and colorectal malignancies and an unclear prognosis. Our previous 25‐year series demonstrated a non‐aggressive nature. This study aims to describe our updated institutional experience. Methods This is a retrospective review of all patients diagnosed with primary PAPD from 1991 to 2021. A prospectively maintained institutional database was searched which included demographics, clinical and pathological manifestations, treatment methods, recurrence, oncological outcome and mortality. Results Thirty patients were diagnosed with PAPD. Fifteen were women (50%); the average age at diagnosis was 71 ± 10.7 years, and the average lesion size was 3.7 ± 2.6 cm. At diagnosis, 12 (40%) were harbouring invasive anal adenocarcinoma. Eight (27%) developed adenocarcinomas concurrent with PAPD recurrence at a mean interval of 9 ± 4.4 years (range 1.9–14.8). The Kaplan–Meier curve estimated overall survival of 93%, 86%, 82%, 65% and 56% at 1, 3, 5, 10 and 15 years, respectively. Median survival was 16 years. Six (20%) had disease‐related mortality. Initially, nine (30%) were treated with abdominoperineal resection (APR), 15 (50%) underwent local resection, three (10%) were treated with radiotherapy, two (7%) received only topical therapy and one (3%) chose observation. Fifteen (50%) experienced recurrence of PAPD, two after undergoing APR. Five (17%) had persistent disease until death. Only 10 (33%) did not experience PAPD recurrence, seven of whom underwent APR. The mean follow‐up time was 9.2 ± 6.2 years. Conclusions Perianal Paget's disease is an aggressive entity with high rates of synchronous anal adenocarcinoma at diagnosis and development of metachronous adenocarcinoma later in life.

Although the harmful effects of smoking after a cancer diagnosis have been clearly demonstrated, many patients continue to smoke cigarettes during treatment and beyond. The NCCN Guidelines for Smoking Cessation emphasize the importance of smoking cessation in all patients with cancer and seek to establish evidence-based recommendations tailored to the unique needs and concerns of patients with cancer. The recommendations contained herein describe interventions for cessation of all combustible tobacco products (eg, cigarettes, cigars, hookah), including smokeless tobacco products. However, recommendations are based on studies of cigarette smoking. The NCCN Smoking Cessation Panel recommends that treatment plans for all patients with cancer who smoke include the following 3 tenets that should be done concurrently: (1) evidence-based motivational strategies and behavior therapy (counseling), which can be brief; (2) evidence-based pharmacotherapy; and (3) close follow-up with retreatment as needed.

Objectives: The development and evaluation of percutaneous thrombectomy devices for pulmonary embolism (PE) pose a need for standardized large in vivo models with representative anatomical and physiological conditions and clots analogs. In this study, we present a swine model of PE model employing human-derived clot analogs. Material and Methods: Baseline angiographic and physiological pressure measurements were obtained in six adult Yorkshire pigs (45–65 kg) and results were benchmarked for interspecies comparison with published human data using fluoroscopic examinations, intra-arterial pressure measurements, and histologic studies. Then, clot analogs were created ex vivo employing banked human blood and a subset incubated in iodinated contrast for fluoroscopic visualization. Clot analogs were then embolized via a femoral venous access and angiographic/physiological consequences were evaluated. Results: The main, right, and left pulmonary artery diameters were 24 ± 1.1 mm, 16.5 ± 0.8 mm, and 12.6 ± 1.2 mm, respectively. The angle between the main pulmonary artery at the bifurcation point was approximately 90–95°. The clot analogs were heterogeneous and had increased fibrin content along the clot length. The overall composition was 96.63% red blood cell (RBC)/3.37% fibrin in the initial section, 48.85% RBC/51.15% fibrin in the intermediate section, and 3.44% RBC/96.56% fibrin in the final section. Embolization of the clot analogs resulted in distal occlusion of the right and left pulmonary arteries. Conclusions: This swine model coupled with clot analog is able to accurately mimic human anatomical and physiological conditions in PE making it feasible for the evaluation of pulmonary thrombectomy devices.

Abstract Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Abstract Purpose Assessing expertise using psychometric models usually yields a measure of ability that is difficult to generalize to the complexity of diagnoses in clinical practice. However, using an item response modeling framework, it is possible to create a decision-aligned response model that captures a clinician’s decision-making behavior on a continuous scale that fully represents competing diagnostic possibilities. In this proof-of-concept study, the authors demonstrate the necessary statistical conceptualization of this model using a specific electrocardiogram (ECG) example. Method The authors collected a range of ECGs with elevated ST segments due to either ST-elevation myocardial infarction (STEMI) or pericarditis. Based on pilot data, 20 ECGs were chosen to represent a continuum from “definitely STEMI” to “definitely pericarditis,” including intermediate cases in which the diagnosis was intentionally unclear. Emergency medicine and cardiology physicians rated these ECGs on a 5-point scale (“definitely STEMI” to “definitely pericarditis”). The authors analyzed these ratings using a graded response model showing the degree to which each participant could separate the ECGs along the diagnostic continuum. The authors compared these metrics with the discharge diagnoses noted on chart review. Results Thirty-seven participants rated the ECGs. As desired, the ECGs represented a range of phenotypes, including cases where participants were uncertain in their diagnosis. The response model showed that participants varied both in their propensity to diagnose one condition over another and in where they placed the thresholds between the 5 diagnostic categories. The most capable participants were able to meaningfully use all categories, with precise thresholds between categories. Conclusions The authors present a decision-aligned response model that demonstrates the confusability of a particular ECG and the skill with which a clinician can distinguish 2 diagnoses along a continuum of confusability. These results have broad implications for testing and for learning to manage uncertainty in diagnosis.

PurposeTo improve calibrationless parallel imaging using pre‐learned subspaces of coil sensitivity functions.Theory and MethodsA subspace‐based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.ResultsWith no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state‐of‐the‐art methods including JSENSE, DeepSENSE, P‐LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.ConclusionA subspace‐based method named JSENSE‐Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

A hexanucleotide repeat expansion in intron 1 of the C9orf72 gene is the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia, or c9ALS/FTD. The RNA transcribed from the expansion, r(G 4 C 2 ) exp , causes various pathologies, including intron retention, aberrant translation that produces toxic dipeptide repeat proteins (DPRs), and sequestration of RNA-binding proteins (RBPs) in RNA foci. Here, we describe a small molecule that potently and selectively interacts with r(G 4 C 2 ) exp and mitigates disease pathologies in spinal neurons differentiated from c9ALS patient-derived induced pluripotent stem cells (iPSCs) and in two c9ALS/FTD mouse models. These studies reveal a mode of action whereby a small molecule diminishes intron retention caused by the r(G 4 C 2 ) exp and allows the liberated intron to be eliminated by the nuclear RNA exosome, a multi-subunit degradation complex. Our findings highlight the complexity of mechanisms available to RNA-binding small molecules to alleviate disease pathologies and establishes a pipeline for the design of brain penetrant small molecules targeting RNA with novel modes of action in vivo.