Associations between metabolic dysfunction-associated fatty liver disease and atherosclerotic cardiovascular disease.

Non-alcoholic fatty liver disease (NAFLD) is closely related to metabolic syndrome and remains a major global health burden. The increased prevalence of obesity and type 2 diabetes mellitus (T2DM) worldwide has contributed to the rising incidence of NAFLD. It is widely believed that atherosclerotic cardiovascular disease (ASCVD) is associated with NAFLD. In the past decade, the clinical implications of NAFLD have gone beyond liver-related morbidity and mortality, with a majority of patient deaths attributed to malignancy, coronary heart disease (CHD), and other cardiovascular (CVD) complications. To better define fatty liver disease associated with metabolic disorders, experts proposed a new term in 2020 - metabolic dysfunction associated with fatty liver disease (MAFLD). Along with this new designation, updated diagnostic criteria were introduced, resulting in some differentiation between NAFLD and MAFLD patient populations, although there is overlap. The aim of this review is to explore the relationship between MAFLD and ASCVD based on the new definitions and diagnostic criteria, while briefly discussing potential mechanisms underlying cardiovascular disease in patients with MAFLD.

Investigating the impact of empagliflozin on the retina of diabetic mice.

BACKGROUND: Diabetic retinopathy (DR) frequently results in compromised visual function, with hyperglycemia-induced disruption of the blood-retinal barrier (BRB) through various pathways as a critical mechanism. Existing DR treatments fail to address early and potentially reversible microvascular alterations. This study examined the effects of empagliflozin (EMPA), a selective Sodium-glucose transporter 2 (SGLT2) inhibitor, on the retina of db/db mice. The objective of this study is to investigate the potential role of EMPA in the prevention and delay of DR. METHODS: db/db mice were randomly assigned to either the EMPA treatment group (db/db + Emp) or the model group (db/db), while C57 mice served as the normal control group (C57). Mice in the db/db + Emp group received EMPA for eight weeks. Body weight, fasting blood glucose (FBG), and blood VEGF were subsequently measured in all mice, along with the detection of specific inflammatory factors and BRB proteins in the retina. Retinal SGLT2 protein expression was compared using immunohistochemical analysis, and BRB structural changes were observed via electron microscopy. RESULTS: EMPA reduced FBG, blood VEGF, and retinal inflammatory factors TNF-α, IL-6, and VEGF levels in the eye tissues of db/db mice. EMPA also increased Claudin-1, Occludin-1, and ZO-1 levels while decreasing ICAM-1 and Fibronectin, thereby preserving BRB function in db/db mice. Immunohistochemistry revealed that EMPA reduced SGLT2 expression in the retina of diabetic mice, and electron microscopy demonstrated that EMPA diminished tight junction damage between retinal vascular endothelial cells and prevented retinal vascular basement membrane thickening in diabetic mice. CONCLUSION: EMPA mitigates inflammation and preserves BRB structure and function, suggesting that it may prevent DR or serve as an effective early treatment for DR.

Association of LDL-C/HDL-C ratio with coronary heart disease: A meta-analysis.

BACKGROUND: Coronary heart disease (CHD) is a common heart disease and a leading cause of death in developed countries and some developing countries such as China. It is recognized as a multifactorial disease, with dyslipidemia being closely associated with the progression of coronary atherosclerosis. Numerous studies have confirmed the relationship between a single indicator of low-density lipoprotein cholesterol (LDL-C) or high-density lipoprotein cholesterol (HDL-C) and CHD. However, the association between LDL-C to HDL-C ratio (LHR) and CHD remains unclear. This study aimed to comprehensively explore the association between LHR and CHD. METHODS: This meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses. PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases were comprehensively searched up to June 15, 2023, to find the studies that indicated the connection between LHR and CHD. A total of 12 published studies were selected. The random-effects model was used to pool the data and mean difference (MD), and the 95% confidence intervals (CI) were taken as the overall outcome. No language restrictions existed in the study selection. The Review Manager 5.4 and Stata 12 were used to analyze the data. RESULTS: Twelve high-quality clinical studies involving 5544 participants, including 3009 patients with CHD, were enrolled in the meta-analysis. The findings revealed that the LHR was higher by 0.65 in patients with CHD than in those without CHD (MD, 0.65; 95% CI, 0.50-0.80). CONCLUSION: The LHR was found to be positively correlated with CHD, suggesting that it may serve as a potential indicator of CHD.

Progress in the treatment of diabetic cardiomyopathy, a systematic review.

Diabetic cardiomyopathy (DCM) is a condition characterized by myocardial dysfunction that occurs in individuals with diabetes, in the absence of coronary artery disease, valve disease, and other conventional cardiovascular risk factors such as hypertension and dyslipidemia. It is considered a significant and consequential complication of diabetes in the field of cardiovascular medicine. The primary pathological manifestations include myocardial hypertrophy, myocardial fibrosis, and impaired ventricular function, which can lead to widespread myocardial necrosis. Ultimately, this can progress to the development of heart failure, arrhythmias, and cardiogenic shock, with severe cases even resulting in sudden cardiac death. Despite several decades of both fundamental and clinical research conducted globally, there are currently no specific targeted therapies available for DCM in clinical practice, and the incidence and mortality rates of heart failure remain persistently high. Thus, this article provides an overview of the current treatment modalities and novel techniques pertaining to DCM, aiming to offer valuable insights and support to researchers dedicated to investigating this complex condition.

ß-elemene alleviates hyperglycemia-induced cardiac inflammation and remodeling by inhibiting the JAK/STAT3-NF-κB pathway.

BACKGROUND: Hyperglycemic induced cardiac hypertrophy and cardiac inflammation are important pathological processes in diabetic cardiomyopathy. ß-elemene (Ele) is a natural compound extracted from Curcuma Rhizoma and has anti-tumor effects. It also has therapeutic effects in some inflammatory diseases. However, the therapeutic effect of Ele on diabetic cardiomyopathy is not clear. The purpose of this study was to evaluate the effect of Ele on hyperglycemia-caused cardiac remodeling and heart failure. METHODS: C57BL/6 mice were intraperitoneally injected with streptozotocin to induce DCM, and Ele was administered intragastric after 8 weeks to investigate the effect of Ele. RNA sequencing of cardiac tissue was performed to investigate the mechanism. RESULTS: Ele markedly inhibited cardiac inflammation, fibrosis and hypertrophy in diabetic mice, as well as in high glucose-induced cardiomyocytes. RNA sequencing showed that cardioprotective effect of Ele involved the JAK/STAT3-NF-κB signaling pathway. Ele alleviated heart and cardiomyocyte inflammation in mice by blocking diabetes-induced JAK2 and STAT3 phosphorylation and NF-κB activation. CONCLUSIONS: The study found that Ele preserved the hearts of diabetic mice by inhibiting JAK/STAT3 and NF-κB mediated inflammatory responses, suggesting that Ele is an effective therapy for DCM.

Miniature mass spectrometers and their potential for clinical point-of-care analysis.

Mass spectrometry (MS) has become a powerful technique for clinical applications with high sensitivity and specificity. Different from conventional MS diagnosis in laboratory, point-of-care (POC) analyses in clinics require mass spectrometers and analytical procedures to be friendly for novice users and applicable for on-site clinical diagnosis. The recent decades have seen the progress in the development of miniature mass spectrometers, providing a promising solution for clinical POC applications. In this review, we report recent advances of miniature mass spectrometers and their exploration in clinical applications, mainly including the rapid analysis of illegal drugs, on-site monitoring of therapeutic drugs, and detection of biomarkers. With improved analytical performance, miniature mass spectrometers are also expected to apply to more and more clinical applications. Some promising POC analyses that can be performed by miniature mass spectrometers in the future are discussed. Lastly, we also provide our perspectives on the challenges in technical development of miniature mass spectrometers for clinical POC analysis.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap.

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

Conjugated linoleic acid alleviates glycolipid metabolic disorders by modulating intestinal microbiota and short-chain fatty acids in obese rats.

Although conjugated linoleic acid (CLA) has been shown to have anti-obesity properties, the effect and mechanism of CLA in alleviating glycolipid metabolism disorders remains unclear. In this work, it was observed that rats fed a high-fat diet (HFD) had lower body weight and body fat levels after 9 weeks of low-dose and high-dose CLA interventions. The results of blood biochemical indices showed that CLA significantly reduced the levels of total cholesterol, triglycerides, fasting blood glucose and insulin. Additionally, high-dose CLA could restore the intestinal microbiota composition, including increasing the relative abundances of short-chain fatty acid (SCFA)-producing microbiota, such as Dubosiella, Faecalibaculum and Bifidobacterium; decreasing the relative abundances of Enterococcus and Ruminococcus_2; and increasing the content of SCFAs in feces and serum. Further analysis showed that high-dose CLA could increase the expression levels of Insr, Irs-2, Akt and Glut4 in the liver tissue of HFD-induced obese rats. Consistently, high dose of CLA could reversibly improve the downregulation of INSR, AKT, PI3K and GLUT4 protein expression caused by HFD and reverse the decline in AKT phosphorylation levels. Correlation clustering analysis with a heatmap showed that the changes in specific microbiota induced by high-dose CLA were correlated with changes in obesity-related indices and gene expression. The molecular docking analysis showed that the molecular docking of SCFAs with the IRS-2, AKT and GLUT4 proteins had high linking activity. The results supported that CLA can alleviate glycolipid metabolic imbalances associated with obesity by altering the intestinal microbiota to induce the production of SCFAs and thereby activate the INSR/IRS-2/AKT/GLUT4 pathway. This study supports CLA may be preferentially used by the intestinal microbiota of the host to promote its health.

Medical emergency calls and calls for central nervous system symptoms during the COVID-19 outbreak in Hangzhou, China.

Background: Since January 2020, the continuous and severe COVID-19 epidemic has ravaged various countries around the world and affected their emergency medical systems (EMS). The total number of emergency calls and the number of emergency calls for central nervous system (CNS) symptoms during the 2020 COVID-19 outbreak in Hangzhou, China (January 20-March 20) were investigated, and it was investigated whether these numbers had decreased as compared with the corresponding period in 2019. Methods: The number of daily emergency calls, ambulance dispatches, and rescues at the Hangzhou Emergency Center (HEC) was counted. The CNS symptoms considered in this study included those of cerebrovascular diseases, mental and behavioral disorders, and other neurological diseases. Results: It was found that, during the 2020 study period, the number of emergency calls was 33,563, a decrease of 19.83% (95% CI: 14.02-25.41%) as compared to the 41,863 emergency calls in 2019 (P < 0.01). The number of ambulances dispatched was 10,510, a decrease of 25.55% (95 %CI: 18.52-35.11%) as compared to the 14,117 ambulances dispatched in 2019 (P < 0.01). The number of rescues was 7,638, a decrease of 19.67% (95% CI: 16.12-23.18%) as compared with the 9,499 rescues in 2019 (P < 0.01). It was also found that the number of emergency calls related to CNS symptoms, including symptoms of cerebrovascular diseases, mental and behavioral disorders, and other neurological diseases, was significantly reduced (P < 0.01). Conclusion: The total number of medical emergency calls and the number of emergency calls for CNS symptoms occurring in a large city in China decreased significantly during the COVID-19 epidemic.

[Research Progress and Application of Digital Pathology Technology in Bone Marrow Morphology].

Advances in digital pathology technology have enabled pathologists and laboratory physicians to perform quick, easy, accurate and reproducible analysis of digital images of tissues and cells with the aid of electronic screens and software tools, rather than relying solely on traditional optical microscopy observations. The conventional clinical cytology testing practice is to be replaced by a digital workflow, which includes both digital imaging and image analysis. This article provides an overview of the basic principles of digital pathology techniques, the advances of development of device in cytology digital pathology, and their clinical applications in bone marrow morphology, and existing problems and prospects of digital pathology application in hematology.

Exploring the impact of the COVID-19 epidemic on the medical emergency calls and calls for cardiovascular diseases in Hangzhou, China.

AIMS: We aimed to evaluate the impact of the COVID-19 epidemic on emergency and cardiovascular disease-related calls in Hangzhou, China. METHODS: We conducted a single-center retrospective study, collecting data on emergency calls to the Hangzhou Emergency Center (HEC) during the COVID-19 epidemic (January 20, 2020, to March 15, 2020). Data were compared with the same period in 2019. RESULTS: Compared to 2019, the number of emergency calls has dropped by 21.63%, ambulance calls by 29.02%, rescue calls by 22.57%, and cardiovascular disease-related emergency calls by 32.86%. The numbers of emergency, ambulance, and rescue calls in 2020 were significantly lower than in 2019. CONCLUSIONS: During the COVID-19 epidemic in Hangzhou, the numbers of emergency and cardiovascular disease-related calls have decreased significantly. These results point to a severe social problem that requires the attention of the medical community and the government.

Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm.

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population. Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived. Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05-6.85), platelet packed volume (OR: 2.63, CI:2.31-3.79), leukocyte count (OR: 2.01, CI:1.79-2.19), red blood cell count (OR: 1.99, CI:1.80-2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12-1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram. Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow.

Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.

The Dilemma of Masks During the COVID-19 Outbreak.

BACKGROUND: Many people have experienced novel coronavirus pneumonia since the beginning of the COVID-19 pandemic in Wuhan, China. The Chinese government has encouraged people to wear face masks in public places; however, due to the large population, there may be a series of problems related to this recommendation, including shortages of masks and lack of an optimal disposal method for used masks. OBJECTIVE: The purpose of this study is to understand the current status of mask shortages and used masks in China. METHODS: A questionnaire survey was designed to assess the current status of mask shortages and used masks. The differences among groups were analyzed with chi-square tests. RESULTS: The constituent ratio of those who reuse masks was 61%. Obtaining masks from the drugstore was reported to be very difficult due to high demand and short supply, and approximately 1/3 of the respondents purchased expensive masks. Most people know how to properly handle used masks, and only 7% of them casually discard masks. However, 50% of respondents have seen others throw away used masks at will. A further subgroup analysis showed that respondents in Central China tended to use masks repeatedly, as did medical personnel. Females, people living in the central region, and medical personnel may find it more difficult to purchase masks in drugstores. Non-medical personnel may be more likely to buy expensive masks. Females, people living in the western region, and medical personnel may be more likely to know how to properly handle used masks and not to discard used masks at will. Medical personnel may be more likely to observe others discarding used masks at will. CONCLUSION: In response to COVID-19, the public should be encouraged to use face masks and are advised not to reuse or throw away masks at will due to safety concerns.

Developing a Machine Learning Algorithm for Identifying Abnormal Urothelial Cells: A Feasibility Study.

INTRODUCTION: Urine cytology plays an important role in diagnosing urothelial carcinoma (UC). However, urine cytology interpretation is subjective and difficult. Morphogo (ALAB, Boston, MA, USA), equipped with automatic acquisition and scanning, optical focusing, and automatic classification with convolutional neural network has been developed for bone marrow aspirate smear analysis of hematopoietic diseases. The goal of this preliminary study was to determine the feasibility of developing a machine learning algorithm on Morphogo for identifying abnormal urothelial cells in urine cytology slides. METHODS: Thirty-seven achieved abnormal urine cytology slides from cases with the diagnosis of atypical urothelial cells and above (suspicions or positive for UC) were obtained from 1 hospital. A pathologist (J.R.) reviewed the slides and manually selected and annotated representative cells to feed into Morphogo with following categories: benign (urothelial cells, squamous cells, degenerated cells, and inflammatory cells), atypical cells, and suspicious cells. Initial validation of the algorithm was performed on a subset of the original 37 cases. Urine samples from additional 12 unknown cases with various histological diagnoses (6 cases of high-grade urothelial carcinoma (HGUC), 1 case of low-grade urothelial carcinoma (LGUC), 1 case of prostate adenocarcinoma, 1 case of renal cell carcinoma, and 4 cases of non-neoplastic conditions) were collected from another hospital for initial blind testing. RESULTS: A total of 1,910 benign and 1,978 abnormal (atypical and suspicious) cells from 37 slides were annotated for developing and training of the algorithm. This algorithm was validated on 27 slides that resulted in identification of at least 1 abnormal cell per slide, with a total of 200 abnormal cells, and an average of 7.4 cells per slide. Of the 12 unknown cases tested, the original cytology was positive for tumor cells in 2 HGUC samples. Morphogo was abnormal (atypical or suspicious) for 6 samples from patients with UC, including one with LGUC and one with prostate adenocarcinoma. CONCLUSION: Morphogo machine learning algorithm is capable of identifying abnormal urothelial cells. Further validation studies with a larger number of urine samples will be needed to determine if it can be used to assist the cytological diagnosis of UC.

Effect of COVID-19 outbreak on the treatment time of patients with acute ST-segment elevation myocardial infarction.

OBJECTIVE: To explore the effect of COVID-19 outbreak on the treatment time of patients with ST-segment elevation myocardial infarction (STEMI) in Hangzhou, China. METHODS: We retrospectively reviewed the data of STEMI patients admitted to the Hangzhou Chest Pain Center (CPC) during a COVID-19 epidemic period in 2020 (24 cases) and the same period in 2019 (29 cases). General characteristics of the patients were recorded, analyzed, and compared. Moreover, we compared the groups for the time from symptom onset to the first medical contact (SO-to-FMC), time from first medical contact to balloon expansion (FMC-to-B), time from hospital door entry to first balloon expansion (D-to-B), and catheter room activation time. The groups were also compared for postoperative cardiac color Doppler ultrasonographic left ventricular ejection fraction (LVEF),the incidence of major adverse cardiovascular and cerebrovascular events (MACCE),Kaplan-Meier survival curves during the 28 days after the operation. RESULTS: The times of SO-to-FMC, D-to-B, and catheter room activation in the 2020 group were significantly longer than those in the 2019 group (P < 0.05). The cumulative mortality after the surgery in the 2020 group was significantly higher than the 2019 group (P < 0.05). CONCLUSION: The pre-hospital and in-hospital treatment times of STEMI patients during the COVID-19 epidemic were longer than those before the epidemic. Cumulative mortality was showed in Kaplan-Meier survival curves after the surgery in the 2020 group was significantly different higher than the 2019 group during the 28 days.The diagnosis and treatment process of STEMI patients during an epidemic should be optimized to improve their prognosis.

[Effects of Adding Straw and Biochar with Equal Carbon Content on Soil Respiration and Microbial Biomass Carbon and Nitrogen].

In order to investigate the response of soil respiration, soil microbial biomass carbon and nitrogen, and hydrothermal factors to the addition of biochar and straw, we used an LI-8100 soil carbon flux meter (LI-COR, Lincoln, USA) to study changes in soil respiration and microbial biomass under four treatments:conventional fertilization (CK), conventional fertilization +2.25t·hm-2 biochar-C (T1), conventional fertilizer +2.25t·hm-2 straw-C (T2), and conventional fertilizer +2.25t·hm-2 (biochar-C+straw-C), biochar-C:straw-C=1:1 (T3). The results showed that:① the addition of biochar and straw significantly increased the soil respiration rate and total CO2 emissions, with the largest increase in T3 and the smallest increase in T1. The effect of T1 on soil respiration was promoted in the early stage and later inhibited. ② The microbial biomass carbon and nitrogen and the number of functional bacterial colonies increased significantly with biochar and straw amendments. T1 had a significant promotion effect on nitrogen-fixing bacteria, while T2 had no significant effect on the number of fungi, and T3 showed a positive interaction effect. Soil respiration rates were significantly and positively related to soil microbial biomass carbon and nitrogen as well as to the number of bacteria and actinomycetes. ③ The 5 cm soil temperature of T3 significantly increased by 4.53%. The soil respiration rate and soil temperature showed a significant exponential correlation. To sum up, adding straw and biochar with equal carbon content can significantly increase the soil respiration rate and microbial biomass, and the interaction effect between biochar and straw is positive. Compared with that of the straw treatments, the application of biochar can reduce carbon mineralization to a certain extent, and the effect of carbon sequestration is better.

Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.

Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8-90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

INTRODUCTION: The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications. MATERIALS AND METHODS: Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (Morphogo) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated. RESULTS: In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200-500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (r ≥ 0.762, p < 0.001). DISCUSSION/CONCLUSION: This pilot study confirmed that the Morphogo system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.

Nitric oxide synthase and nitric oxide alterations in chronically stressed rats: a model for nitric oxide in major depressive disorder.

Nitric oxide (NO) and NO synthase-1 (NOS1) are involved in the stress response and in depression. We compared NOS-NO alterations in rats exposed to chronic unpredictable stress (CUS) with alterations in major depressive disorder (MDD) in humans. In the hypothalamus of male CUS rats we determined NOS activity, and in the paraventricular nucleus (PVN) we determined NOS1-immunoreactive (ir) cell densities and co-localization of NOS1 with stress-related neuropeptides corticotropin-releasing hormone (CRH), vasopressin (AVP) or oxytocin (OXT). We measured plasma NO levels and cortisol in male medicine-naïve MDD patients and plasma NO and corticosterone (CORT) in CUS rats. In the CUS rat total NOS activity in the hypothalamus (P=0.018) and NOS1-ir cell density in the PVN were both significantly decreased (P=0.018), while NOS1 staining was mainly expressed in OXT-ir neurons in this nucleus. Interestingly, plasma NO levels were significantly increased both in male CUS rats (P=0.001) and in male MDD patients (P<0.001). Plasma CORT levels were increased in male CUS rats (P=0.001), while male MDD patients did not show a significant change in cortisol levels. In conclusion, the changes in plasma and hypothalamic NOS-NO of CUS rats and MDD were similar. The male CUS rat model may thus help us with our investigation of the mechanism underlying NOS-NO alterations in depression.