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Objectives: In Vietnam, the burden of healthcare-associated infections (HAIs), especially by multidrug-resistant organisms (MDROs), can be greater at national hospitals where a high number of severe patients from lower-tier hospitals are received for treatment. To reduce MDRO and HAI incidences at the tropical diseases intensive care unit (ICU) of Cho Ray Hospital, the final line of treatment for 20 southern provinces of Vietnam, a comprehensive infection prevention bundle was developed and implemented in 2019. In this retrospective study, we evaluated the effectiveness of this intervention in preventing MDRO infections and HAIs among patients at risk. Methods: The infection prevention bundle included elements to improve administration controls, environmental controls, and personal protective equipment usage. The bundle was implemented via training and active monitoring. Medical data, such as microbiology results, length of hospital stay, treatment cost of all patients admitted to the targeted ICU, and data on adherence to the bundle elements, were collected via routine monitoring from July to December 2019. These data were reviewed and analyzed. An independent 2-sample t test was used to calculate the significance of the differences in MDRO and HAI rates before and after the intervention. Results: The mean number of MDRO infections decreased significantly after implementation of the infection prevention bundle (7.0 vs 3.3; P = .011). HAI and ventilator-associated pneumonia (VAP) rates also decreased significantly (5.9 vs 3.7; P= .013 and 20.4 vs 13.7; P=0.047, respectively). The mean total treatment cost per patient was reduced by 1.8 million VND (US $76.76). Bundle-element adherence was high throughout the intervention period, ranging from 72.7% (putting MDRO sign on beds) to 100% (hand hygiene, cohort patients, environment cleaning). Conclusions: Implementation of an appropriate infection prevention bundle with a high adherence rate by healthcare workers helped to effectively reduce MDRO infection and HAI rates in the tropical diseases ICU.

BackgroundSARS-CoV-2 Delta variant caused a large number of COVID-19 cases in many countries, including Vietnam. Understanding mortality risk factors is crucial for the clinical management of severe COVID-19.MethodsWe conducted a retrospective study at an intensive care center in Ho Chi Minh City that urgently built by Bach Mai Hospital during the COVID-19 outbreak in Vietnam, when the Delta variant predominated. Participants were laboratory-confirmed patients with SARS-CoV-2 infection, admitted in August 2021. Data on patients' demographic and clinical characteristics, radiographic and laboratory findings, treatment, and clinical time course were compared between survivors and non-survivors. Risk factors to mortality were assessed using logistic regression.ResultsAmong 504 eligible COVID-19 patients, case fatality was 52.2%. Unvaccinated patients accounted for 61.2% of non-survivors and 43.6% of survivors (p < 0.001). The time from onset to hospital admission was 8 days in non-survivors and 7 days in survivors (p = 0.004). Among non-survivors, 90.2% developed acute respiratory distress syndrome (ARDS). Oxygen therapy was administered for all patients, but antiviral agent was given to 51.7% of non-survivors. 54.2% of non-survivors tested positive for the bacterial infection using blood culture. The risk factors for mortality were diabetes mellitus, respiration rate, oxygen saturation, vaccination status, time from onset to admission, and older age.ConclusionsCritical patients with COVID-19 owing to the Delta variant in Vietnam had delayed hospital admission, leading to ARDS and death. Early availability of vaccines and preventing bacterial infections are crucial for reducing mortality of COVID-19, especially in low- and middle-income countries.

Abstract Background Understanding the spatiotemporal distribution of emerging infectious diseases is crucial for implementation of control measures. In the first 7 months from the occurrence of COVID-19 pandemic, Vietnam has documented comparatively few cases of COVID-19. Understanding the spatiotemporal distribution of these cases may contribute to development of global countermeasures. Methods We assessed the spatiotemporal distribution of COVID-19 from 23 January to 31 July 2020 in Vietnam. Data were collected from reports of the World Health Organization, the Vietnam Ministry of Health, and related websites. Temporal distribution was assessed via the transmission classification (local or quarantined cases). Geographical distribution was assessed via the number of cases in each province along with their timelines. The most likely disease clusters with elevated incidence were assessed via calculation of the relative risk (RR). Results Among 544 observed cases of COVID-19, the median age was 35 years, 54.8% were men, and 50.9% were diagnosed during quarantine. During the observation period, there were four phases: Phase 1, COVID-19 cases occurred sporadically in January and February 2020; Phase 2, an epidemic wave occurred from the 1st week of March to the middle of April (Wave 1); Phase 3, only quarantining cases were involved; and Phase 4, a second epidemic wave began on July 25th, 2020 (Wave 2). A spatial cluster in Phase 1 was detected in Vinh Phuc Province (RR, 38.052). In Phase 2, primary spatial clusters were identified in the areas of Hanoi and Ha Nam Province (RR, 6.357). In Phase 4, a spatial cluster was detected in Da Nang, a popular coastal tourist destination (RR, 70.401). Conclusions Spatial disease clustering of COVID-19 in Vietnam was associated with large cities, tourist destinations, people’s mobility, and the occurrence of nosocomial infections. Past experiences with outbreaks of emerging infectious diseases led to quick implementation of governmental countermeasures against COVID-19 and a general acceptance of these measures by the population. The behaviors of the population and the government, as well as the country’s age distribution, may have contributed to the low incidence and small number of severe COVID-19 cases.

Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative.

Axial Bradykinesia is an important feature of advanced Parkinson’s disease (PD). The purpose of this study is to quantify axial bradykinesia using wearable sensors with the long-term aim of quantifying these movements, while the subject performs routine domestic activities. We measured back movements during common daily activities such as pouring, pointing, walking straight and walking around a chair with a test system engaging a minimal number of Inertial Measurement (IM) based wearable sensors. Participants included controls and PD patients whose rotation and flexion of the back was captured by the time delay between motion signals from sensors attached to the upper and lower back. PD subjects could be distinguished from controls using only two sensors. These findings suggest that a small number of sensors and similar analyses could distinguish between variations in bradykinesia in subjects with measurements performed outside of the laboratory. The subjects could engage in routine activities leading to progressive assessments of therapeutic outcomes.

This article aims to provide a concise overview of the latest research concerning social media, with a particular focus on tendencies observed recent year. Furthermore, this study investigates and addresses aspects of social networking and e-commerce. Our study was conducted using bibliometric by using VOSviewer and content analytic methods on a dataset obtained from Scopus. The dataset consisted of 29,667 articles and covered the time span from 2010 to 2022. We explore and analyze biometrics in the scope of this study to identify significant groups and then offer a framework for future research. A brief description and practical standpoint of social media will be presented and accompanied by research recommendations. This research will provide a comprehensive examination of keywords associated with social media, as well as renowned universities that have a substantial volume of content. Furthermore, this study’s findings highlight different trends of research papers with potential keywords, and propose potential options for future research. Additionally, this study makes a practical contribution by assessing the broad range of concerns surrounding the topic of social media and assisting managers in recognizing many facets of social media.

Objectives: Quality of surgery has recently become an essential topic in the prognosis of colon cancer. Complete mesocolic excision for colon cancer has recently gained popularity with high-quality surgery. Patient specimens after complete mesocolic excision with central vessel ligation procedures have an integrity of the mesocolon and the yield of three fields of lymph node harvest. We apply the glacial acid, absolute ethanol, water, and formaldehyde solution to each specimen based on the Japanese classification of lymph node groups and station numbers. We aim to identify the distribution and status of lymph node metastasis according to each tumor site and some pathological characteristics related to this disease. Methods: A prospective cohort study was performed on 45 laparoscopic complete mesocolic excision surgery patients. Results: 2791 lymph nodes were harvested after complete mesocolic excision surgery. The average number was 62.0 ± 22.3 nodes. The mean tumor size (in the largest dimension) was 4.2 ± 1.8 cm. The average length of the resected bowel segments was 29.1 ± 7.7 cm. There are 63 (2.3%) node metastases in 2791 lymph nodes, in which 17/45 (37.8%) patients had pN(+). The minimum positive node size was 1 mm. The positive pericolic lymph nodes (station 1) accounted for the highest rate, with 53 nodes (1.9%). The number of lymph nodes in young age ⩽60 is more significant than in older. The results were similar, with a more significant node retrieval in the group with a tumor size >4.5 cm and specimen length >25 cm. The number of lymph nodes in lower tumor invasive (pT1,3) was smaller than pT4. Our research shows that the cecum, ascending, and descending colon had greater nodes than others, with a mean number of 78.6, 74.2, and 71.3, respectively. Conclusions: The metastasis and harvested lymph nodes accounted for the highest rate of colon cancer in station 1 and the lowest rate in station 3. The number of retrieved lymph nodes was significantly associated with tumor location, size, specimen length, and patient age.

Machine learning (ML) algorithms have gained widespread application in constructing computational models for predicting the bioactivity and physicochemical properties of numerous compounds, notably HDAC inhibitors. In this work, 2801 unique compounds with confirmed bioassays on HDAC2 were collected and employed to train ML models to virtually screening and possibly design potential inhibitors for HDAC2. A meticulous 3‐step procedure was first proposed to ensure the hits are within the application domain of the screening models. Through virtual screening of 91 million compounds, 59 structures were suggested by the four highest predictability models as potential HDAC2 inhibitors. The bioactivity of these compounds is further confirmed through a validated docking protocol, wherein 57 out of the 59 active hits proposed by the four models exhibited superior affinity with HDAC2 compared to vorinostat, with docking scores ranging from −10.74 to −7.06 kcal/mol. Lead optimization strategies were then implemented on the top‐performing compound from the molecular docking scores, CID 122648337, enhancing its binding affinity and interactions within the HDAC2 active site. This optimization led to the creation of two novel lead compounds, demonstrating higher affinity to HDAC2 than the initial hits. The stability of lead compounds in the active site was confirmed via MD simulations. [ABSTRACT FROM AUTHOR]

Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

Let 퓟n := H*((ℝP∞)n) ≅ ℤ2[x1, x2, ..., xn] be the graded polynomial algebra over ℤ2, where ℤ2 denotes the prime field of two elements. We investigate the Peterson hit problem for the polynomial algebra 퓟n, viewed as a graded left module over the mod-2 Steenrod algebra, 퓐. For n > 4, this problem is still unsolved, even in the case of n = 5 with the help of computers. In this article, we study the hit problem for the case n = 6 in the generic degree dr = 6(2r − 1) + 4.2r with r an arbitrary non-negative integer. By considering ℤ2 as a trivial 퓐-module, then the hit problem is equivalent to the problem of finding a basis of ℤ2-vector space ℤ2 ⊗퓐퓟n. The main goal of the current article is to explicitly determine an admissible monomial basis of the ℤ2 vector space ℤ2 ⊗퓐퓟6 in some degrees. As an application, the behavior of the sixth Singer algebraic transfer in the degree 6(2r − 1) + 4.2r is also discussed at the end of this paper. [ABSTRACT FROM AUTHOR]