Neuromuscular Blockade Agents Reversal with Sugammadex Compared to Neostigmine in the Living Kidney Donors

Backround: The reversation of NMBA (neuromuscular blocking agents) prevents numerous postoperative complications, increases quality of recovery and decreases the time, expenditure spending in hospital. The choice of medicine used to reverse NMBA depends  considered as a key fators to gain the best outcome and to avoid the side effects. Aim: To evaluate the postoperative effect on muscle relaxation reversal and side effects of sugammadex 2 mg/kg versus the combination of  neostigmine and atropine sulfate in the living kidney donors. MethodS: A randomised controlled trial on 70 patients undergoing living kidney donation surgery were allocated to 2 groups. Patients in group I (SUGA) were reversed with sugammadex 2 mg/kg and in group II (NEO/ATR) with the combination of neostigmine and atropine sulfat. Results: With 35 patients in each group, the study results showed that after 3 mintutes of reversal patients reaching TOF value ≥ 0.9 in group SUGA is 91.4%, after 5 minutes 100% of patients in group SUGA reached TOF value ≥ 0.9 . In group NEO/ATR after 3 minutes 28.6% patients reached TOF ≥ 0.9 and 40% patients reached TOF≥ 0.9 after 5 minutes. The difference in percentage of patients reaching TOF ≥ 0.9 after 3 minutes, 5 minutes of reversal between two groups is significant (p<0.05). After 10 minutes, 100% patients in both group got TOF ≥ 0.9. Time to exutubation of group SUGA was 249.43 ± 81.75 seconds and it was 456.29 ± 146.45 seconds in group NEO/ATR. Nausea, bradycardia, and increased phlegm production in group NEO/ATR was 22.9%; 28.5%; 25.7% respectively; while those side effects were not met in group SUGA, the difference was significant (p<0.05). Conclusion: The muscle relaxation reversal effect of sugammadex was faster than that of neostigmine, the duration TOF ≥ 0.9 and the time to extubation was significantly faster. Sugammadex did not cause hemodynamic changes before and after muscle relaxation reversal, neostigmine resulted in the bradycardia, increased phlegm secreting and other side effects. The renal function after 24 hours postoperatively of two groups was similar

Multiple Recurrent Acute Ischemic Strokes Treated by Thrombectomy in a Patient with Acute Pulmonary Embolism

BACKGROUND: Thrombectomy is recommended to treat for an acute ischemic stroke (AIS) patient with anterior large vessel occlusion. However, there were neither detailed guidelines nor systematic reviews of acute ischemic stroke patients having multiple times or re-occluded arteries. CASE REPORT: In our case report, we struggled a multiple (4-times) AIS patient underwent by one intravenous r-tpA and 3 remaining of endovascular treatment of thrombectomy. Especially, the finding of both pulmonary embolism and cerebral arteries occlusion in this patient made us difficult to decide the appropriate treatment plan. The patient was considered having multiple cardiac thrombi pumping out to the brain and pulmonary vessels even in treatment with NOAC (New Oral Anticoagulant). Our priority, normally, was to recanalize the brain vessels compared to the pulmonary arteries. CONCLUSION: In conclusion, based on this noticed case study, we want to share our experiences on the diagnosis of ischemic stroke, the strategy in treatment and prevention with anticoagulant therapy

Interventional Treatment of Lymphatic Leakage Post Appendectomy: Case Report

BACKGROUND: Postoperative lymphatic complications are not common, and lymphatic leakage complication post appendectomy (LLCPC) is even rarer. However, the number of this operation is high so LLCPC can occur. CASE REPORT: Here, we report a female patient post appendectomy with severe chylous ascites. This patient underwent six operations. A leakage point at the right iliac-fossa, which was embolized successfully after two sessions, was spotted during intranodal lymphangiography. After 6 months, the ascites were significantly reduced while some lymphatic aneurysms still existed in the lumbar-retroperitoneal region. CONCLUSIONS: Basing the knowledge of this clinical case and literature, we have concluded that lymphatic leakage can be diagnosed and embolized by percutaneous intervention

Expansion of KPC-producing Enterobacterales in four large hospitals in Hanoi, Vietnam

Objectives : The incidence of carbapenem resistance among nosocomial Gram-negative bacteria in Vietnam is high and increasing, including among Enterobacterales. In this study, we assessed the presence of one of the main carbapenemase genes, blaKPC, among carbapenem-resistant Enterobacterales (CRE) from four large hospitals in Hanoi, Vietnam, between 2010 and 2015, and described their key molecular characteristics. Methods : KPC-producing Enterobacterales were detected using conventional PCR and were further analysed using S1 nuclease pulsed-field gel electrophoresis (S1-PFGE), Southern blotting and whole-genome sequencing (WGS) for sequence typing and genetic characterisation. Results : blaKPC genes were detected in 122 (20.4%) of 599 CRE isolates. blaKPC-carrying plasmids were diverse in size. Klebsiella pneumoniae harbouring blaKPC genes belonged to ST15 and ST11, whereas KPC-producing Escherichia coli showed more diverse sequence types including ST3580, ST448, ST709 and ST405. Genotypic relationships supported the hypothesis of circulation of a population of ‘resident’ resistant bacteria in one hospital through the years and of transmission among these hospitals via patient transfer. WGS results revealed co-carriage of several other antimicrobial resistance genes and three different genetic contexts of blaKPC-2. Among these, the combination of ISEcp1–blaCTX-M and ISKpn27–blaKPC–ΔISKpn6 on the same plasmid is reported for the first time. Conclusion : We describe the dissemination of blaKPC-expressing Enterobacterales in four large hospitals in Hanoi, Vietnam, since 2010, which may have started earlier, along with their resistance patterns, sequence types, genotypic relationship, plasmid sizes and genetic context, thereby contributing to the overall picture of the antimicrobial resistance situation in Enterobacterales in Vietnam

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Mapping multi-decadal mangrove extent in the northern coast of Vietnam using Landsat time-series data on Google Earth Engine platform

A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam

Segmentation of hard exudate lesions in color fundus image using two-stage CNN-based methods

The presence of hard exudate (EX) lesions is an early clinical symptom of Diabetic Retinopathy (DR); its accurate segmentation is essential for diagnosis and treatment. Automatic segmentation of EX lesions is challenging because they have a wide range of sizes, shapes, and brightness, and can be confused with soft exudate lesions. In this paper, we present and assess an efficient segmentation method for interactively segmenting EX lesions in fundus images from DR patients. Our approach consisted of two main stages: (1) the first stage generates an automatic segmentation using a CNN-based method. (2) If the obtained segmentation is suboptimal, the second stage is used to refine each inaccurately segmented region using a CNN-based interactive segmentation method. To train and evaluate method performance, we used two public datasets (IDRiD and DDR) and two local datasets from two medical centers (EHos and EWT) containing a wide range of EX lesions. Several state-of-the art CNN-based models and the proposed method were trained and evaluated on IDRiD, DDR and EHos datasets using the area under the precision–recall curve (AUPR), the dice similarity coefficient (Dice) and the intersection over union (IoU). In addition, EWT dataset was used to evaluate the methods without retraining the CNN models. The evaluation results using IDRiD dataset showed that the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.893, 76.6% and 62.4%, respectively, which are comparable to the state-of-the-art method on EX segmentation. When evaluated on the DDR dataset with large portion of small EX lesions, the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.693, 60.8% and 45.5%, yielding state-of-the-art performance. Furthermore, with the main evaluation metric being AUPR, the evaluation results on two datasets, EHos and ETW, with scores of 0.689 and 0.667 demonstrated superiority of the proposed method over the other segmentation methods. The proposed two-stage CNN-based segmentation method is an useful approach for accurately segmenting EX lesions in color fundus images, enabling lesion quantification to aid experts in treating patients with DR. The source code, the trained CNN models and the data are publicly available at: https://github.com/dvquang2000/Interactive_HardExudates_Segmentation.</p

Segmentation of hard exudate lesions in color fundus image using two-stage CNN-based methods

The presence of hard exudate (EX) lesions is an early clinical symptom of Diabetic Retinopathy (DR); its accurate segmentation is essential for diagnosis and treatment. Automatic segmentation of EX lesions is challenging because they have a wide range of sizes, shapes, and brightness, and can be confused with soft exudate lesions. In this paper, we present and assess an efficient segmentation method for interactively segmenting EX lesions in fundus images from DR patients. Our approach consisted of two main stages: (1) the first stage generates an automatic segmentation using a CNN-based method. (2) If the obtained segmentation is suboptimal, the second stage is used to refine each inaccurately segmented region using a CNN-based interactive segmentation method. To train and evaluate method performance, we used two public datasets (IDRiD and DDR) and two local datasets from two medical centers (EHos and EWT) containing a wide range of EX lesions. Several state-of-the art CNN-based models and the proposed method were trained and evaluated on IDRiD, DDR and EHos datasets using the area under the precision–recall curve (AUPR), the dice similarity coefficient (Dice) and the intersection over union (IoU). In addition, EWT dataset was used to evaluate the methods without retraining the CNN models. The evaluation results using IDRiD dataset showed that the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.893, 76.6% and 62.4%, respectively, which are comparable to the state-of-the-art method on EX segmentation. When evaluated on the DDR dataset with large portion of small EX lesions, the proposed method achieved AUPR, mean Dice and mean IoU scores of 0.693, 60.8% and 45.5%, yielding state-of-the-art performance. Furthermore, with the main evaluation metric being AUPR, the evaluation results on two datasets, EHos and ETW, with scores of 0.689 and 0.667 demonstrated superiority of the proposed method over the other segmentation methods. The proposed two-stage CNN-based segmentation method is an useful approach for accurately segmenting EX lesions in color fundus images, enabling lesion quantification to aid experts in treating patients with DR. The source code, the trained CNN models and the data are publicly available at: https://github.com/dvquang2000/Interactive_HardExudates_Segmentation.</p