Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

The preoperative distinction between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) can be difficult, even for experts, but is highly relevant. We aimed to develop an easy-to-use algorithm, based on a convolutional neural network (CNN) to preoperatively discern PCNSL from GBM and systematically compare its performance to experienced neurosurgeons and radiologists. To this end, a CNN-based on DenseNet169 was trained with the magnetic resonance (MR)-imaging data of 68 PCNSL and 69 GBM patients and its performance compared to six trained experts on an external test set of 10 PCNSL and 10 GBM. Our neural network predicted PCNSL with an accuracy of 80% and a negative predictive value (NPV) of 0.8, exceeding the accuracy achieved by clinicians (73%, NPV 0.77). Combining expert rating with automated diagnosis in those cases where experts dissented yielded an accuracy of 95%. Our approach has the potential to significantly augment the preoperative radiological diagnosis of PCNSL.

Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer

Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.

An automatic immuno-microfluidic system integrating electrospun polystyrene microfibrous reactors for rapid detection of salivary cortisol

Conventional competitive enzyme-linked immunosorbent assay (ELISA) to measure the cortisol level in body fluid consumes a large amount of time, owing to complicated operations involved and requirement for precise control of reagent addition. We developed an automatic microfluidic system to detect salivary cortisol rapidly, and an electrospun polystyrene (PS) microfiber-based reactor providing considerable binding sites for antibody immobilization, thus resolving the time limitations of competitive ELISA. Cortisol sample, horseradish peroxidase (HRP)-conjugated cortisol, and 3,3',5,5'-tetramethylbenzidine (TMB) substrate were delivered to the PS reactor from containers in sequence by pumps automatically. The color variation due to oxidized TMB complex reflects the cortisol concentration level measured using an RGB phototransistor. In addition, the entire procedure from sample introduction to obtaining the photocurrent took only 15 min. This system can be implemented to quantify cortisol from 0.37 ng/mL to 30 ng/mL, and the limit of detection was estimated at 0.37 ng/mL.

Profiling the inflammatory bowel diseases using genetics, serum biomarkers, and smoking information

Crohn's disease (CD) and ulcerative colitis (UC) are two etiologically related yet distinctive subtypes of the inflammatory bowel diseases (IBD). Differentiating CD from UC can be challenging using conventional clinical approaches in a subset of patients. We designed and evaluated a novel molecular-based prediction model aggregating genetics, serum biomarkers, and tobacco smoking information to assist the diagnosis of CD and UC in over 30,000 samples. A joint model combining genetics, serum biomarkers and smoking explains 46% (42-50%, 95% CI) of phenotypic variation. Despite modest overlaps with serum biomarkers, genetics makes unique contributions to distinguishing IBD subtypes. Smoking status only explains 1% (0-6%, 95% CI) of the phenotypic variance suggesting it may not be an effective biomarker. This study reveals that molecular-based models combining genetics, serum biomarkers, and smoking information could complement current diagnostic strategies and help classify patients based on biologic state rather than imperfect clinical parameters.

Non-invasive assessment of liver fibrosis by serum metabolites in non-human primates and human patients

Liver fibrosis, a rising cause of chronic liver diseases, could eventually develop into cirrhosis and liver failure. Current diagnosis of liver fibrosis relies on pathological examination of hepatic tissues acquired from percutaneous biopsy, which may produce invasive injuries. Here, for non-invasive assessment of liver fibrosis, we applied comparative multi-omics in non-human primates (rhesus macaques) and subsequent serum biopsy in human patients. Global transcriptomics showed significant gene enrichment of metabolism process, in parallel with oxidative stress and immune responses in fibrotic primates. Targeted metabolomics were concordant with transcriptomic patterns, identifying elevated lipids and porphyrin metabolites during hepatic fibrosis. Importantly, liquid biopsy results validated that specific metabolites in the serum (e.g., biliverdin) were highly diagnostic to distinguish human patients from healthy controls. Findings describe the interconnected transcriptional and metabolic network in primate liver fibrosis and provide potential indices for non-invasive detection of liver fibrosis in humans.

Development of a loop-mediated isothermal amplification (LAMP)-based electrochemical test for rapid detection of SARS-CoV-2

Rapid, reliable, sensitive, portable, and accurate diagnostics are required to control disease outbreaks such as COVID-19 that pose an immense burden on human health and the global economy. Here we developed a loop-mediated isothermal amplification (LAMP)-based electrochemical test for the detection of SARS-CoV-2 that causes COVID-19. The test is based on the oxidation-reduction reaction between pyrophosphates (generated from positive LAMP reaction) and molybdate that is detected by cyclic voltammetry using inexpensive and disposable carbon screen printed electrodes. Our test showed higher sensitivity (detecting as low as 5.29 RNA copies/μL) compared to the conventional fluorescent reverse transcriptase (RT)-LAMP. We validated our tests using human serum and saliva spiked with SARS-CoV-2 RNA and clinical (saliva and nasal-pharyngeal) swab samples demonstrating 100% specificity and 93.33% sensitivity. Our assay provides a rapid, specific, and sensitive test with an electrochemical readout in less than 45 min that could be adapted for point-of-care settings.

Using machine learning to model older adult inpatient trajectories from electronic health records data

Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients' hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients progressing toward stable 'discharge-like' states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM; 12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUC-ROC: MVTS data = 0.85; HMM states = 0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation.

Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis

Diagnostic protocol for prostate cancer (KP) is affected by poor accuracy and high false-positive rate. The most promising innovative approach is based on urine analysis by electronic noses (ENs), highlighting a specific correlation between urine alteration and KP presence. Although EN could be exploited to develop non-invasive KP diagnostic tools, no study has already introduced EN into clinical practice, most probably because of drift issues that hinder EN scaling up from research objects to large-scale diagnostic devices. This study, proposing an EN for non-invasive KP detection, describes the data processing protocol applied to a urine headspace dataset acquired over 9 months, comprising 81 patients with KP and 41 controls, for compensating the drift. It proved effective in mitigating drift on 1-year-old sensors by restoring accuracy from 55% up to 80%, achieved by new sensors not subjected to drift. The model achieved, on double-blind validation, a balanced accuracy of 76.2% (CI_95% 51.9–92.3).

•

Urine odor alteration due to prostate cancer can be detected by electronic noses

•

Sensors drift hinders electronic nose scaling up to large-scale diagnostic devices

•

OSC mitigates drift on 1-year-old sensors, restoring accuracy from 55% up to 80%

Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test

The sudden loss of smell is among the earliest and most prevalent symptoms of COVID-19 when measured with a clinical psychophysical test. Research has shown the potential impact of frequent screening for olfactory dysfunction, but existing tests are expensive and time consuming. We developed a low-cost ($0.50/test) rapid psychophysical olfactory test (KOR) for frequent testing and a model-based COVID-19 screening framework using a Bayes Network symptoms model. We trained and validated the model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 33% prevalence, 309 RT-PCR confirmed) and healthy miners (n = 1,365; 1.1% prevalence, 15 RT-PCR confirmed). The model predicted COVID-19 status with 76% and 96% accuracy in the healthcare and miners samples, respectively (healthcare: AUC = 0.79 [0.75-0.82], sensitivity: 59%, specificity: 87%; miners: AUC = 0.71 [0.63-0.79], sensitivity: 40%, specificity: 97%, at 0.50 infection probability threshold). Our results highlight the potential for low-cost, frequent, accessible, routine COVID-19 testing to support society's reopening.