Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost-effective and non-invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model.

Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review.

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burden on patients and society. This review looks at recent research, both domestically and abroad, on the application of machine learning (ML) for early COPD screening. The review discusses the practical application, key optimization points, and prospects of ML techniques in early COPD screening. The aim is to establish a scientific foundation and reference framework for future research and the development of screening strategies.

Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.

Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by analyzing nutritional biomarkers, key leukemia indicators, and granulocytes in their blood. Using a machine learning algorithm and ten indices, the blood samples of 826 children with ALL and 255 children with AML were compared to a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an area under the curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.

Retinoblastoma patients treated in Sri Lanka from 2014 to 2020: epidemiology, clinical status and correlates of lag time in seeking tertiary care services.

BACKGROUND: Retinoblastoma (RB) is a tumour of children < 5 years with a incidence of 1 in 20,000. Around 20 RB cases are diagnosed yearly in Sri Lanka, a lower middle-income country with high literacy levels and healthcare free at point of delivery. Incidence, local and systemic severity and mortality related to RB are reportedly high in low- and middle- income countries in comparison to higher income countries. Aims of this study were to describe demographic, socioeconomic, and clinical characteristics of Sri Lankan RB patients attending the designated RB unit at the Lady Ridgeway Hospital (LRH), Colombo between January 2014 to December 2020, and determine correlates of lag time (LT) for first tertiary care visit after detecting the first symptom/sign. METHODS: Two descriptive cross-sectional studies (DCSS) were conducted, one on 171 RB patients with demographic and clinical data collected between 2017 and 2020. In 2021, the second DCSS took place where socioeconomic and further demographic data were collected using telephone interviews, recruiting a subgroup of 90 (53%), consenting and contactable RB patient/ parent pairs. Bivariate and multivariable analyses were applied to determine correlates of LT of > 4 weeks for first tertiary care visit. Results were expressed as odds ratios and 95% confidence intervals. RESULTS: LRH survey (N = 171): Median age at diagnosis was 15 months (range 1-94 months; IQR: 8-27); 89 (52%) were females. Groups D and E tumours were 25.7% (n = 44) and 62.6% (n = 107) respectively with 121 (71%) enucleations. The number of deaths were 2 (1.2%). Telephone survey (N = 90): Proportion with LT of > 4 weeks for first tertiary care visit was 58% (n = 52). None of the putative risk factors (ethnicity, parental educational level, socioeconomic status, distance from residence to tertiary care unit and receiving financial assistance) were associated with LT in both analyses. CONCLUSION: Despite a high proportion with groups D and E tumours and enucleations, mortality rate was low, most likely due to availability of designated tertiary care. No correlates for LT of > 4 weeks for tertiary care presentation were identified. Early RB detection needs rigorous implementation of screening strategies and increased awareness among primary care health workers and parents.

Screening preterm-born infants for autistic traits may help to identify social communication difficulties at five years of age.

AIM: This study compared neurodevelopmental screening questionnaires completed when preterm-born children reached 2 years of corrected age with social communication skills at 5.5 years of age. METHODS: Eligible subjects were born in 2011 at 24-34 weeks of gestation, participated in a French population-based epidemiological study and were free of motor and sensory impairment at 2 years of corrected age. The Ages and Stages Questionnaire (ASQ) and the Modified Checklist for Autism in Toddlers (M-CHAT) were used at 2 years and the Social Communication Questionnaire (SCQ) at 5.5 years of age. RESULTS: We focused on 2119 children. At 2 years of corrected age, the M-CHAT showed autistic traits in 20.7%, 18.5% and 18.2% of the children born at 24-26, 27-31 and 32-34 weeks of gestation, respectively (p = 0.7). At 5.5 years of age, 12.6%, 12.7% and 9.6% risked social communication difficulties, with an SCQ score ≥90th percentile (p = 0.2). A positive M-CHAT score at 2 years was associated with higher risks of social communication difficulties at 5.5 years of age (odds ratio 3.46, 95% confidence interval 2.04-5.86, p < 0.001). Stratifying ASQ scores produced similar results. CONCLUSION: Using parental neurodevelopmental screening questionnaires for preterm-born children helped to identify the risk of later social communication difficulties.

Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study.

BACKGROUND: Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible. OBJECTIVE: This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB. METHODS: In this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia. RESULTS: The study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB (P<.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P=.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (ß=-0.65, 95% CI -1.15 to -0.15; P=.01) and simple motor (ß=-0.86, 95% CI -1.43 to -0.30; P=.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model. CONCLUSIONS: The study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral.

A Decision-Making Algorithm for Remote Digital Assessments of Alzheimer's Disease.

INTRODUCTION: Remote digital assessments (RDAs) such as voice recording, video and motor sensors, olfactory, hearing, and vision screenings are now starting to be employed to complement classical biomarker and clinical evidence to identify patients in the early AD stages. Choosing which RDA can be proposed to individual patients is not trivial and often time-consuming. This position paper presents a decision-making algorithm for using RDA during teleconsultations in memory clinic settings. METHOD: The algorithm was developed by an expert panel following the Delphi methodology. RESULTS: The decision-making algorithm is structured as a series of yes-no questions. The resulting questionnaire is freely available online. DISCUSSION: We suggest that the use of screening questionnaires in the context of memory clinics may help accelerating the adoption of RDA in everyday clinical practice.

[Study on Circulating Tumor Cell Detection System Based on Microfluidic Chip].

Objective: To achieve high throughput and high detection rate of circulating tumor cells (CTCs) in human peripheral blood, and to provide efficient and accurate early screening for cancer patients. Methods: A microfluidic chip with the integration of sorting, enrichment and detection was designed, and CTCs at the single cell level were detected by fluorescence detection system to obtain the number of CTCs in samples. Results: The peripheral blood samples after lysed red blood cells were used for 6 experiments. When the injection rate reached 0.2 mL/h, CTCs could reach the best detection rate of 78.6%, and the correlation coefficient within the group was above 0.8. Conclusion: CTCs detection system can achieve high detection rate and has good reliability, which can provide a reliable reference for clinical research in related fields.

A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity.

BACKGROUND: Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death. METHODS: The authors developed an innovative framework consisting of ResNet-18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non-APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five-fold cross-validation. RESULTS: Both the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p < .001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non-APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework. CONCLUSIONS: To the authors' knowledge, their convolutional neural network-based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL. PLAIN LANGUAGE SUMMARY: The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurate way.

Liquid biopsy using cell-free DNA in the early diagnosis of hepatocellular carcinoma.

Hepatocellular carcinoma ranks fourth in cancer-related causes of death worldwide and second in China. Patients with hepatocellular carcinoma (HCC) at the early stage have a better prognosis compared to HCC patients at the late stage. Therefore, early screening for HCC is critical for clinical treatment decisions and improving the prognosis of patients. Ultrasound (US), computed tomography (CT), and serum alpha fetoprotein (AFP) have been used to screen HCC, but HCC is still difficult to be diagnosed in the early stage due to the low sensitivity of the above methods. It is urgent to find a method with high sensitivity and specificity for the early diagnosis of HCC. Liquid biopsy is a noninvasive detection method using blood or other bodily fluids. Cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) are important biomarkers for liquid biopsy. Recently, HCC screening methods using the application of cfDNA and ctDNA have become the hot spot of early HCC diagnostics. In this mini review, we summarize the latest research progress of liquid biopsy based on blood cfDNA in early screening of HCC.

Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening.

BACKGROUND: To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS: A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS: In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.

The role of routine first-trimester ultrasound screening for central nervous system abnormalities: a longitudinal single-center study using an unselected cohort with 3-year experience.

BACKGROUND: To evaluate the role of a standardized first-trimester scan in screening different kinds of central nervous system malformations and to report a 3-year experience from a tertiary center using an unselected cohort. METHODS: This was a retrospective analysis of prospectively collected data from a single center evaluating first-trimester scans with predesigned standardized protocols performed between 1 May 2017 and 1 May 2020, involving 39,526 pregnancies. All pregnant women underwent a series of prenatal ultrasound scans at 11-14, 20-24, 28-34 and 34-38 weeks of gestation. Abnormalities were confirmed by magnetic resonance imaging, postmortem examination or trained ultrasound professionals. Pregnancy outcomes and some postnatal follow-up were obtained from maternity medical records and telephone calls. RESULTS: A total of 38,586 pregnancies included in the study. The detection rates of CNS anomalies by ultrasound in the first, second, third and late third trimester were 32%, 22%, 25%, and 16%, respectively. And there were 5% of CNS anomalies missed by prenatal ultrasound. In the first-trimester scan, we diagnosed all cases of exencephaly, anencephaly, alobar holoprosencephaly and meningoencephalocele, and some cases of posterior cranial fossa anomalies (20%), open spina bifida (67%), semilobar holoprosencephaly (75%) and severe ventriculomegaly (8%). Vein of Galen aneurysmal malformation, closed spina bifida, lobar holoprosencephaly, intracranial infection, arachnoid cyst, agenesis of the corpus callosum, cysts of the septum pellucidum and isolated absence of the septum pellucidum were never detected during the first trimester. The abortion rates of fetal CNS anomalies detected by first-trimester scan, second-trimester scan, and third- trimester scan were 96%, 84% and 14%, respectively. CONCLUSIONS: The study showed that almost 1/3 of central nervous system anomalies were detected by the standard first-trimester scan and these cases were associated with a high rate of abortion. Early screening for fetal abnormalities gives parents more time for medical advice and safer abortion if needed. It is therefore recommended that some major CNS anomalies should be screened in the first trimester. The standardized anatomical protocol, consisting of four fetal brain planes, were recommended for routine first trimester ultrasound screening.

A barrier to participation in cervical cancer screenings: fatalism.

Cervical cancer is a significant disease affecting women's health in terms of its incidence and is one of the most preventable cancers. However, participation in early cervical cancer-screening programs has been unsatisfactory for various reasons. In this descriptive, relationship-seeking study, we examined the relationship between fatalism tendency, an individual barrier to participation in early cancer screening programs, and women's attitudes toward the early diagnosis of cervical cancer and undergoing the Pap smear test. Research data were collected between August 1, 2019 and December 1, 2019, in a city in northern Turkey from 602 women using a participant information form, the Attitudes Toward Early Diagnosis in Cervical Cancer Scale, and the Fatalism Tendency Scale. We found that fatalistic tendencies in women were a predictor of their attitudes toward the early diagnosis of cervical cancer (odds ratio [OR] = -0.64, ß = .47, p < .001) and undergoing the Pap smear test (OR = 1.01, ß = -.15, p < .001). Women with high fatalism tendencies had a more negative attitude toward the early diagnosis of cervical cancer and their participation rate in Pap smear screening programs was low. Therefore, nurses must consider women's fatalistic tendencies and attitudes toward cancer when organizing educational and informational programs that encourage participation in cervical cancer screening.

Epidemiology and early screening strategies for colorectal cancer in China.

China ranks the first worldwide in the number of new colorectal cancer (CRC) cases and CRC-related deaths. The increasing incidence of early-onset CRC in recent years highlights the challenges related to CRC screening and prevention. High-quality colonoscopy is the universally used gold standard for CRC screening. Risk assessment combined with a two-step screening strategy based on colonoscopy and non-invasive examinations was proven to be highly effective. However, systematic use of well-established risk factors associated with CRC, beyond age, could better identify those who might harbor advanced colorectal neoplasia, improve the diagnostic yield of current screening modalities, and optimize the selection of individuals who might benefit most from preventive strategies. "Personalization" and "Standardization" are the future development directions of CRC screening, from the initiation of screening in those at high risk for CRC to follow-up after treatment, which are the key to ensure the screening efficiency.

[Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage].

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.

Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity.

Neuroimaging-driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model-agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder-specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.

Serum untargeted lipidomics by UHPLC-ESI-HRMS aids the biomarker discovery of colorectal adenoma.

BACKGROUND: Colorectal adenoma (CA) is an important precancerous lesion and early screening target of colorectal cancer (CRC). Lipids with numerous physiological functions are proved to be involved in the development of CRC. However, there is no lipidomic study with large-scale serum samples on diagnostic biomarkers for CA. METHODS: The serum lipidomics of CA patients (n = 50) and normal control (NR) (n = 50) was performed by ultra high performance liquid chromatography-high resolution mass spectrometry with electrospray ionization (UHPLC-ESI-HRMS). Univariate and multivariate statistical analyses were utilized to screen the differential lipids between groups, and combining the constituent ratio analysis and diagnostic efficiency evaluation by receiver operating characteristic (ROC) curve disclosed the potential mechanism and biomarkers for CA. RESULTS: There were obvious differences in serum lipid profiles between CA and NR groups. Totally, 79 differential lipids were selected by criterion of P < 0.05 and fold change > 1.5 or < 0.67. Triacylglycerols (TAGs) and phosphatidylcholines (PCs) were the major differential lipids with ratio > 60%, indicating these two lipid metabolic pathways showed evident disequilibrium, which could contribute to CA formation. Of them, 12 differential lipids had good diagnostic ability as candidate biomarkers for CA (AUC ≥ 0.900) by ROC analysis. CONCLUSIONS: To our knowledge, this is the first attempt to profile serum lipidomics and explore lipid biomarkers of CA to help early screening of CRC. 12 differential lipids are obtained to act as potential diagnostic markers of CA. PCs and fatty acids were the main dysregulated biomarkers for CA in serum.

Late hepatitis C virus diagnosis among patients with newly diagnosed hepatocellular carcinoma: a case-control study.

BACKGROUND: New direct-acting antiviral therapies have revolutionized hepatitis C virus (HCV) infection therapy. Nonetheless, once liver cirrhosis is established, the risk of hepatocellular carcinoma (HCC) still exists despite virus eradication. Late HCV diagnosis hinders timely access to HCV treatment. Thus, we determined trends and risk factors associated with late HCV among patients with a diagnosis of HCC in Taiwan. METHODS: We conducted a population-based unmatched case-control study. 2008-2018 Claims data were derived from the Taiwan National Health Insurance Research Database. Individuals with an initial occurrence of liver cancer between 2012 and 2018 were included. The late HCV group were referred as individuals who were diagnosed with HCC within 3 years after HCV diagnosis. The control group were referred as individuals who were diagnosed more than 3 years after the index date. We used multivariable logistic models to explore individual- and provider-level risk factors associated with a late HCV diagnosis. RESULTS: A decreasing trend was observed in the prevalence of late HCV-related HCC diagnosis between 2012 and 2018 in Taiwan. On an individual level, male, elderly patients, patients with diabetes mellitus (DM), and patients with alcohol-related disease had significantly higher risks of late HCV-related HCC diagnosis. On a provider level, patients who were mainly cared for by male physicians, internists and family medicine physicians had a significantly lower risk of late diagnosis. CONCLUSIONS: Elderly and patients who have DM and alcohol related disease should receive early HCV screening. In addition to comorbidities, physician factors also matter. HCV screening strategies shall take these higher risk patients and physician factors into consideration to avoid missing opportunities for early intervention.

Reducing health inequality in Black, Asian and other minority ethnic pregnant women: impact of first trimester combined screening for placental dysfunction on perinatal mortality.

OBJECTIVE: To assess the impact of the Fetal Medicine Foundation (FMF) first trimester screening algorithm for pre-eclampsia on health disparities in perinatal death among minority ethnic groups. DESIGN: A retrospective cohort study from July 2016 to December 2020. SETTING: A large London teaching hospital. PATIENTS AND METHODS: All women who underwent first trimester pre-eclampsia risk assessment using either the NICE screening checklist or the FMF multimodal approach. Women considered at high-risk in the FMF cohort were offered 150 mg aspirin before 16 weeks' gestation, serial growth scans and elective birth at 40 weeks. MAIN OUTCOME MEASURES: Stillbirth, neonatal death and perinatal death rates stratified by screening method and maternal ethnicity. RESULTS: In the NICE cohort, the perinatal death rate was significantly higher in non-white than white women (7.95 versus 2.63/1000 births, OR 3.035, 95% CI 1.551-5.941). Following the introduction of FMF screening, the perinatal death rate in non-white women fell from 7.95 to 3.22/1000 births (OR 0.403, 95% CI 0.206-0.789), such that it was no longer significantly different from the perinatal mortality rate in white women (3.22 versus 2.55/1000 births, OR 1.261, 95% CI 0.641-2.483). CONCLUSIONS: First trimester combined screening for placental dysfunction is associated with a significant reduction in perinatal death in minority ethnic women. Health disparities in perinatal death among ethnic minority women demand urgent attention from both clinicians and health policy makers. The data of this study suggest that this ethnic health inequality may be avoidable. TWEETABLE ABSTRACT: Multimodal early pregnancy placental dysfunction screening can lead to a significant reduction in perinatal deaths in non-white women.

Carbon monoxide (CO) correlates with symptom severity, autoimmunity, and responses to probiotics treatment in a cohort of children with autism spectrum disorder (ASD): a post-hoc analysis of a randomized controlled trial.

BACKGROUND: Inflammation, autoimmunity, and gut-brain axis have been implicated in the pathogenesis of autism spectrum disorder (ASD). Carboxyhemoglobin (SpCO) as a non-invasive measurement of inflammation has not been studied in individuals with ASD. We conducted this post-hoc study based on our published clinical trial to explore SpCO and its association with ASD severity, autoimmunity, and response to daily Lactobacillus plantarum probiotic supplementation. METHODS: In this study, we included 35 individuals with ASD aged 3-20 years from a previously published clinical trial of the probiotic Lactobacillus plantarum. Subjects were randomly assigned to receive daily Lactobacillus plantarum probiotic (6 × 1010 CFUs) or a placebo for 16 weeks. The outcomes in this analysis include Social Responsiveness Scale (SRS), Aberrant Behavior Checklist second edition (ABC-2), Clinical Global Impression (CGI) scale, SpCO measured by CO-oximetry, fecal microbiome by 16 s rRNA sequencing, blood serum inflammatory markers, autoantibodies, and oxytocin (OT) by ELISA. We performed Kendall's correlation to examine their interrelationships and used Wilcoxon rank-sum test to compare the means of all outcomes between the two groups at baseline and 16 weeks. RESULTS: Elevated levels of serum anti-tubulin, CaM kinase II, anti-dopamine receptor D1 (anti-D1), and SpCO were found in the majority of ASD subjects. ASD severity is correlated with SpCO (baseline, R = 0.38, p = 0.029), anti-lysoganglioside GM1 (R = 0.83, p = 0.022), anti-tubulin (R = 0.69, p = 0.042), and anti-D1 (R = 0.71, p = 0.045) in treatment group. CONCLUSIONS: The findings of the present study suggests that the easily administered and non-invasive SpCO test offers a potentially promising autoimmunity and inflammatory biomarker to screen/subgroup ASD and monitor the treatment response to probiotics. Furthermore, we propose that the associations between autoantibodies, gut microbiome profile, serum OT level, GI symptom severity, and ASD core symptom severity scores are specific to the usage of probiotic treatment in our subject cohort. Taken together, these results warrant further studies to improve ASD early diagnosis and treatment outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03337035 , registered November 8, 2017.