| 3 | Metabolic puzzle: Exploring liver fibrosis differences in Asian metabolic-associated fatty liver disease subtypes显示文摘BACKGROUND Metabolic-associated fatty liver disease(MAFLD)is a liver condition marked by excessive fat buildup in the absence of heavy alcohol use.It is primarily linked with metabolic issues like insulin resistance,obesity,and abnormal lipid levels,and is often observed with other conditions such as type 2 diabetes and cardiovascular disease.However,whether the subtypes of MAFLD based on the metabolic disorder differentially impact liver fibrosis is not well explicated,especially in the Asian population.AIM To compare the severity of liver fibrosis among different MAFLD subtypes.METHODS A total of 322 adult patients of either gender with fatty liver on ultrasound were enrolled between January to December 2021.MAFLD was defined as per the Asian Pacific Association for the Study of the Liver guidelines.Fibrosis-4 index(Fib-4)and nonalcoholic fatty liver disease fibrosis score(NFS)were employed to evaluate liver fibrosis.RESULTS The mean age was 44.84±11 years.Seventy-two percent of the patients were female.Two hundred and seventy-three patients were classified as having MAFLD,of which 110(40.3%)carried a single,129(47.3%)had two,and 34(12.5%)had all three metabolic conditions.The cumulative number of metabolic conditions was related to elevated body mass index,triglyceride(TG)levels,and glycated hemoglobin,lower high-density lipoprotein(HDL)levels,higher liver inflammation(by aspartate aminotransferase andγ-glutamyl transferase),and higher likelihood of fibrosis(by NFS and Fib-4 scores)(P<0.05 for all).The proportion of advanced fibrosis also increased with an increase in the number of metabolic conditions(4.1%,25.5%,35.6%,and 44.1%by NFS and 6.1%,10.9%,17%,and 26.5%by Fib-4 for no MAFLD and MAFLD with 1,2,and 3 conditions,respectively).Among MAFLD patients,those with diabetes alone were the eldest and had the highest mean value of NFS score and Fib-4 score(P<0.05),while MAFLD patients diagnosed with lean metabolic dysfunction exhibited the highest levels of TG and alanine aminotransferase but the lowest HDL levels(P<0.05).CONCLUSION The study suggests that the severity of liver fibrosis in MAFLD patients is influenced by the number and type of metabolic conditions present.Early identification and management of MAFLD,particularly in patients with multiple metabolic conditions,are crucial to prevent liver-related complications. | Sabhita Shabir Shaikh Fakhar Ali Qazi-Arisar Saba Nafay Sidra Zaheer Hafeezullah Shaikh Zahid Azam | 2024 | World Journal of Hepatology2024，16，1： | 0 |
| 4 | Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5显示文摘Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible. | Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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