Abstract: Type 2 diabetes (T2D) is caused by a combination of lifestyle and genetic factors. Physical activity (PA) is a key element in improving the HbA1c index and reducing the risk of complications in a patient with T2D. A descriptive crosssectional study was conducted on 629 outpatients with T2D at Hue Transportation Hospital to describe patients’ PA levels and some related factors. The results showed that there were 48% of participants achieved the WHO recommended level of PA. The multivariate logistic regression model identifed some factors associated with PA among the T2D patients, including age group (group aged 45-64 with OR = 1.55; 95% CI: 1.10 – 2.18), academic level (≥ high school level with OR = 1.79; 95%CI: 1.15 – 2.78), sleeping time (6 - 9 hours with OR = 1.86; 95% CI: 1.20 – 2.87), blood pressure (normal blood pressure with OR = 1.42; 95% CI: 1.02 – 1.99) and HbA1c ( 〈 7% with OR = 1.43; 95%CI: 1.03 – 1.99). The proportion of patients meeting the recommended PA level was low, making it crucial to strengthen education about the health benefts of doing PAs for diabetic patients.

Abstract: The model of Late Pleistocene-Holocene sequence stratigraphy of the subaqueous Red River delta and the adjacent shelf is proposed by interpretation of high-resolution seismic documents and comparison with previous research results on Holocene sedimentary evolution on the delta plain. Four units (U1, U2, U3, and U4) and four sequence stratigraphic surfaces (SB1, TS, TRS and MFS) were determined. The formation of these units and surfaces is related to the global sea-level change in Late Pleistocene-Holocene. SB1, defined as the sequence boundary, was generated by subaerial processes during the Late Pleistocene regression and could be remolded partially or significantly by transgressive ravinement processes subsequently. 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Abstract: The AFFINITY trial (Assessment of Fluoxetine in Stroke Recovery) reported that oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and seizures. After trial medication was ceased at 6 months, survivors were followed to 12 months post-randomization. This preplanned secondary analysis aimed to determine any sustained or delayed effects of fluoxetine at 12 months post-randomization. Methods: AFFINITY was a randomized, parallel-group, double-blind, placebo-controlled trial in adults (n=1280) with a clinical diagnosis of stroke in the previous 2 to 15 days and persisting neurological deficit who were recruited at 43 hospital stroke units in Australia (n=29), New Zealand (4), and Vietnam (10) between 2013 and 2019. Participants were randomized to oral fluoxetine 20 mg once daily (n=642) or matching placebo (n=638) for 6 months and followed until 12 months after randomization. The primary outcome was function, measured by the modified Rankin Scale, at 6 months. Secondary outcomes for these analyses included measures of the modified Rankin Scale, mood, cognition, overall health status, fatigue, health-related quality of life, and safety at 12 months. Results: Adherence to trial medication was for a mean 167 (SD 48) days and similar between randomized groups. At 12 months, the distribution of modified Rankin Scale categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio, 0.93 [95% CI, 0.76–1.14]; P =0.46). Compared with placebo, patients allocated fluoxetine had fewer recurrent ischemic strokes (14 [2.18%] versus 29 [4.55%] ; P =0.02), and no longer had significantly more falls (27 [4.21%] versus 15 [2.35%] ; P =0.08), bone fractures (23 [3.58%] versus 11 [1.72%] ; P =0.05), or seizures (11 [1.71%] versus 8 [1.25%] ; P =0.64) at 12 months. Conclusions: Fluoxetine 20 mg daily for 6 months after acute stroke had no delayed or sustained effect on functional outcome, falls, bone fractures, or seizures at 12 months poststroke. The lower rate of recurrent ischemic stroke in the fluoxetine group is most likely a chance finding. Registration: URL: http://www.anzctr.org.au/ ; Unique identifier: ACTRN12611000774921.

Abstract: Colistin được sử dụng như là liệu pháp cuối cùng để điều trị cho những trường hợp nhiễm trùng vi khuẩn Enterobacteriaceae đa kháng thuốc. Hiện nay, dấy lên mối đe dọa toàn cầu về vi khuẩn kháng kháng sinh sau khi có những báo cáo phát hiện sự hiện diện của gen plasmid mcr-1 kháng colistin có khả năng truyền ngang. Nghiên cứu cắt ngang mô tả được thực hiện tại các chợ trên địa bàn Thành phố Hồ Chí Minh năm 2018, nhằm xác định tỷ lệ vi khuẩn Escherichia coli có mang gen đề kháng colistin phân lập từ thịt heo và người bán thịt heo sống tại Thành phố Hồ Chí Minh. Kết quả của nghiên cứu cho thấy các chủng E. coli phân lập từ thịt heo có tỷ lệ kháng colistin là 41,9% (26/62) và tỷ lệ mang gen mcr-1 là 35,8% (22/62). Trong khi đó, tỷ lệ kháng colistin ở các chủng E. coli phân lập từ phân của người bán thịt heo trong nghiên cứu này là 36,1% (13/36) và mang gen mcr-1 là 7,8% (10/36). Điều này cho thấy mcr-1 là cơ chế kháng colistin chủ yếu của các chủng E. coli kháng colistin phân lập từ thịt và phân của người bán thịt heo tại Việt Nam. Nghiên cứu này giúp cảnh báo về những nguồn chứa vi khuẩn E. coli kháng colistin và có thể làm lây lan âm thầm gen mcr-1 kháng colistin trong cộng đồng.

Abstract: Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.