Screening for At-Risk Nonalcoholic Fatty Liver Disease in the Primary Care Setting.

While nonalcoholic fatty liver disease is a leading cause of end-stage liver disease, most patients with nonalcoholic fatty liver disease do not develop cirrhosis and its complications. Therefore, risk stratification using inexpensive, noninvasive screening modalities is critical to avoid overdiagnosis and overtreatment of a large proportion of the population. In this review, we discuss the data supporting screening and current professional society recommendations on this topic. Screening for at-risk nonalcoholic fatty liver disease is recommended in patients with risk factors including diabetes, the metabolic syndrome, hepatic steatosis, and elevated aminotransferases. Screening typically consists of noninvasive testing using serum biomarkers followed by elastography using specialized imaging modalities. This sequential screening approach accurately identifies both high- and low-risk patients and is cost-effective when applied to at-risk populations. In conclusion, screening for advanced nonalcoholic fatty liver disease in the primary care setting is a crucial part of identifying high-risk patients who may benefit from aggressive intervention while avoiding overtreatment of patients at low risk of liver-related complications.

Racial Disparity in Esophageal Squamous Cell Carcinoma Treatment and Survival in the United States.

INTRODUCTION: Esophageal squamous cell carcinoma (ESCC) has a higher incidence and prevalence than esophageal adenocarcinoma among Black individuals in the United States. Black individuals have lower ESCC survival. These racial disparities have not been thoroughly investigated. We examined the disparity in treatment and survival stratified by ESCC stage at diagnosis. METHODS: The Surveillance, Epidemiology, and End Results database was queried to identify patients with ESCC between 2000 and 2019. The identified cohort was divided into subgroups by race. Patient and cancer characteristics, treatment received, and survival rates were compared across the racial subgroups. RESULTS: A total of 23,768 patients with ESCC were identified. Compared with White individuals, Black individuals were younger and had more distant disease during diagnosis (distant disease: 26.7% vs 23.8%, P < 0.001). Black individuals had lower age-standardized 5-year survival for localized (survival % [95% confidence interval]: 19.3% [16-22.8] vs 27.6% [25.1-30.2]), regional (14.3% [12-16.7] vs 21.1% [19.6-22.7]), and distant (2.9% [1.9-4.1] vs 6.5% [5.5-7.5]) disease. Black individuals were less likely to receive chemotherapy (54.7% vs 57.5%, P = 0.001), radiation (58.5% vs 60.4%, P = 0.03), and surgery (11.4% vs 16.3%, P < 0.0001). DISCUSSION: Black individuals with ESCC have a lower survival rate than White individuals. This could be related to presenting at a later stage but also disparities in which treatments they receive even among individuals with the same stage of disease. To what extent these disparities in receipt of treatment is due to structural racism, social determinants of health, implicit bias, or patient preferences deserves further study.

Pharmacological, Physiochemical, and Drug-Relevant Biological Properties of Short Chain Fatty Acid Hexosamine Analogues Used in Metabolic Glycoengineering.

In this study, we catalog structure activity relationships (SAR) of several short chain fatty acid (SCFA)-modified hexosamine analogues used in metabolic glycoengineering (MGE) by comparing in silico and experimental measurements of physiochemical properties important in drug design. We then describe the impact of these compounds on selected biological parameters that influence the pharmacological properties and safety of drug candidates by monitoring P-glycoprotein (Pgp) efflux, inhibition of cytochrome P450 3A4 (CYP3A4), hERG channel inhibition, and cardiomyocyte cytotoxicity. These parameters are influenced by length of the SCFAs (e.g., acetate vs n-butyrate), which are added to MGE analogues to increase the efficiency of cellular uptake, the regioisomeric arrangement of the SCFAs on the core sugar, the structure of the core sugar itself, and by the type of N-acyl modification (e.g., N-acetyl vs N-azido). By cataloging the influence of these SAR on pharmacological properties of MGE analogues, this study outlines design considerations for tuning the pharmacological, physiochemical, and the toxicological parameters of this emerging class of small molecule drug candidates.

Metabolic glycoengineering bacteria for therapeutic, recombinant protein, and metabolite production applications.

Metabolic glycoengineering is a specialization of metabolic engineering that focuses on using small molecule metabolites to manipulate biosynthetic pathways responsible for oligosaccharide and glycoconjugate production. As outlined in this article, this technique has blossomed in mammalian systems over the past three decades but has made only modest progress in prokaryotes. Nevertheless, a sufficient foundation now exists to support several important applications of metabolic glycoengineering in bacteria based on methods to preferentially direct metabolic intermediates into pathways involved in lipopolysaccharide, peptidoglycan, teichoic acid, or capsule polysaccharide production. An overview of current applications and future prospects for this technology are provided in this report.

PNPLA3 risk allele is associated with risk of hepatocellular carcinoma but not decompensation in compensated cirrhosis.

BACKGROUND: The PNPLA3-rs738409-G, TM6SF2-rs58542926-T, and HSD17B13-rs6834314-A polymorphisms have been associated with cirrhosis, hepatic decompensation, and HCC. However, whether they remain associated with HCC and decompensation in people who already have cirrhosis remains unclear, which limits the clinical utility of genetics in risk stratification as HCC is uncommon in the absence of cirrhosis. We aimed to characterize the effects of PNPLA3, TM6SF2, and HSD17B13 genotype on hepatic decompensation, HCC, and liver-related mortality or liver transplant in patients with baseline compensated cirrhosis. METHODS: We conducted a single-center retrospective study of patients in the Michigan Genomics Initiative who underwent genotyping. The primary predictors were PNPLA3, TM6SF2, and HSD17B13 genotypes. Primary outcomes were either hepatic decompensation, HCC, or liver-related mortality/transplant. We conducted competing risk Fine-Gray analyses on our cohort. RESULTS: We identified 732 patients with baseline compensated cirrhosis. During follow-up, 50% of patients developed decompensation, 13% developed HCC, 24% underwent liver transplant, and 27% died. PNPLA3-rs738409-G genotype was associated with risk of incident HCC: adjusted subhazard hazard ratio 2.42 (1.40-4.17), p=0.0015 for PNPLA3-rs738409-GG vs. PNPLA3-rs738409-CC genotype. The 5-year cumulative incidence of HCC was higher in PNPLA3-rs738409-GG carriers than PNPLA3-rs738409-CC/-CG carriers: 15.6% (9.0%-24.0%) vs. 7.4% (5.2%-10.0%), p<0.001. PNPLA3 genotype was not associated with decompensation or the combined outcome of liver-related mortality or liver transplant. TM6SF2 and HSD17B13 genotypes were not associated with decompensation or HCC. CONCLUSIONS: The PNPLA3-rs738409-G allele is associated with an increased risk of HCC among patients with baseline compensated cirrhosis. People with cirrhosis and PNPLA3-rs738409-GG genotype may warrant more intensive HCC surveillance.

Combination of Fibrosis-4, liver-stiffness measurement, and Fibroscan-AST score to predict liver-related outcomes in nonalcoholic fatty liver disease.

INTRODUCTION: Noninvasive tests, such as Fibrosis-4 (FIB-4), liver-stiffness measurement (LSM) by vibration-controlled transient elastography, and Fibroscan-AST (FAST), are frequently used for risk stratification in NAFLD. The comparative performance of FIB-4 and LSM and FAST to predict clinical outcomes of patients with NAFLD remained unclear. We aim to evaluate the performance of FIB-4, LSM, and FAST scores to predict clinical outcomes in patients with NAFLD. METHODS: We included consecutive adult patients with NAFLD with transient elastography performed between 2015 and 2022 from the United States and Singapore. Patients with NAFLD stratified based on baseline FIB-4, LSM, and FAST score were followed up until clinical outcomes notably liver-related events (LREs), LREs or death, death, and major adverse cardiac events. RESULTS: A total of 1262 patients with NAFLD (63% with obesity and 37% with diabetes) with vibration-controlled transient elastography were followed up for median 3.5 years. FIB-4 stratified patients with NAFLD into low-risk (<1.3), intermediate-risk (1.3-2.67), and high-risk (>2.67) in 59.4%, 31.5%, and 9.1%, respectively. No LRE occurred with baseline FIB-4 <1.3, regardless of LSM and FAST score. Higher FIB-4 was associated with a higher risk of LREs within each LSM category. FIB-4 had a higher area under the received operating characteristic curve than LSM or FAST score to predict LRE. CONCLUSIONS: In this multicenter international study, FIB-4 and LSM synergistically predicted the risk of LRE. In patients with FIB-4 <1.3, vibration-controlled transient elastography may incorrectly classify up to 10% of the patients as high risk. FIB-4 should be incorporated into risk stratification in NAFLD even among patients who underwent VCTE.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence.

BACKGROUND: Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources. OBJECTIVE: To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence. METHODS: We used a fiber laser-based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set. RESULTS: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images. CONCLUSION: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.

Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks.

BACKGROUND: Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence. METHODS: We used fiber laser-based SRH, a label-free, nonconsumptive, high-resolution microscopy method (<60 sec per 1 × 1 mm2) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48). RESULTS: Using patch-level CNN predictions, the inference algorithm returns a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%. CONCLUSION: SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.

Automated histologic diagnosis of CNS tumors with machine learning.

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

Cell Surface and Membrane Engineering: Emerging Technologies and Applications.

Membranes constitute the interface between the basic unit of life-a single cell-and the outside environment and thus in many ways comprise the ultimate "functional biomaterial". To perform the many and often conflicting functions required in this role, for example to partition intracellular contents from the outside environment while maintaining rapid intake of nutrients and efflux of waste products, biological membranes have evolved tremendous complexity and versatility. This article describes how membranes, mainly in the context of living cells, are increasingly being manipulated for practical purposes with drug discovery, biofuels, and biosensors providing specific, illustrative examples. Attention is also given to biology-inspired, but completely synthetic, membrane-based technologies that are being enabled by emerging methods such as bio-3D printers. The diverse set of applications covered in this article are intended to illustrate how these versatile technologies-as they rapidly mature-hold tremendous promise to benefit human health in numerous ways ranging from the development of new medicines to sensitive and cost-effective environmental monitoring for pathogens and pollutants to replacing hydrocarbon-based fossil fuels.