Virtual Remote Pathology Education in Support of Virtual Remote Gynecologic-Oncology Training: The Open Pathology Education Network Pilot Proof of Concept Experience.

CONTEXT.­: The subspecialty workforce in pathology globally is inadequate for the demands of many modern therapies. The Open Pathology Education Network (OPEN) was formed to augment the global pathology workforce. The International Gynecologic Cancer Society (IGCS) virtual gynecologic-oncology (gyn-onc) fellowship training identified needs for higher-level pathology support. OBJECTIVE.­: To report on an OPEN-IGCS pilot project to support gyn-onc and pathology education efforts in a developing country. DESIGN.­: Curriculum with learning objectives and content from open sources was assembled. Mentoring sessions included bidirectional case sharing. Trainees received sequential curricula assignments and had options for communication outside mentoring sessions. Pretest and posttest digital slide assessments were included. Mentors attended the gynecology tumor board, allowing for the assessment of quality and accuracy of pathology diagnosis for cases discussed. RESULTS.­: Learners completing the pretest and posttest showed substantial improvement, with 2 practicing pathologists improving their diagnostic scores from 60% to an average of 95%. A third trainee-level participant also improved, but to a lesser degree. Qualitative assessments included increased confidence in presentation and an increased ability to anticipate questions, raise issues of expanded differential diagnoses, and articulate appropriate workup. Observations of clinicians who participated also noted increased confidence in participating pathologists. Secondary value included establishing an expanded network of support in other subspecialties for participants. Pathologic issues at the tumor board decreased, from more than 50% in the first 3 months of study to 0% in the last 3 months of study. The curriculum was embedded into a self-paced learning portal at courses.open-pathology.org. CONCLUSIONS.­: The OPEN-IGCS collaboration model shows the potential to provide subspecialty pathology training remotely.

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

Flavonoids as dual-target inhibitors against α-glucosidase and α-amylase: a systematic review of in vitro studies.

Diabetes mellitus remains a major global health issue, and great attention is directed at natural therapeutics. This systematic review aimed to assess the potential of flavonoids as antidiabetic agents by investigating their inhibitory effects on α-glucosidase and α-amylase, two key enzymes involved in starch digestion. Six scientific databases (PubMed, Virtual Health Library, EMBASE, SCOPUS, Web of Science, and WHO Global Index Medicus) were searched until August 21, 2022, for in vitro studies reporting IC50 values of purified flavonoids on α-amylase and α-glucosidase, along with corresponding data for acarbose as a positive control. A total of 339 eligible articles were analyzed, resulting in the retrieval of 1643 flavonoid structures. These structures were rigorously standardized and curated, yielding 974 unique compounds, among which 177 flavonoids exhibited inhibition of both α-glucosidase and α-amylase are presented. Quality assessment utilizing a modified CONSORT checklist and structure-activity relationship (SAR) analysis were performed, revealing crucial features for the simultaneous inhibition of flavonoids against both enzymes. Moreover, the review also addressed several limitations in the current research landscape and proposed potential solutions. The curated datasets are available online at https://github.com/MedChemUMP/FDIGA .

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization.

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Assessment of Hepatic Lesions After non-Thermal Tumor Ablation by Irreversible Electroporation in a Pig Model.

Irreversible electroporation (IRE) is a non-thermal and minimal invasive modality to ablate pathologic lesions such as hepatic tumors. Histological analysis of the initial lesions after IRE can help predict ablation efficacy. We aimed to investigate the histological characteristics of early hepatic lesions after IRE application using animal models. IRE (1500 V/cm, a pulse length of 100 µs, 60 or 90 pulses) was applied to the liver of miniature pigs. H&E and TUNEL staining were performed and analyzed. Ablated zones of pig liver were discolored and separated from the normal zone after IRE. Histologic characteristics of ablation zones included preserved hepatic lobular architecture with a unique hexagonal-like structure. Apoptotic cells were detected, and sinusoidal dilatation and blood congestion were observed, but hepatic arteries and bile ducts were intact around the ablation zones. The early lesions obtained by delivering monophasic square wave pulses through needle electrodes reflected typical histological changes induced by IRE. Therefore, it was found that the histological assessment of the early hepatic lesion after IRE can be utilized to predict the IRE ablation effect.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA).

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

Laparoscopic pancreatic duct exploration, electrohydraulic shock wave lithotripsy combined with internal drainage for pancreatic duct stones: A case report.

INTRODUCTION: Chronic pancreatitis is characterized by irreversible structural damage, including fibrosis and compression of the pancreatic ducts, often leading to stones forming in the pancreatic duct and parenchyma. Surgery is indicated when severe obstruction with chronic pain is presented and conventionally drained by pancreatojejunostomy. CASE REPORT: A 56-year-old female patient with epigastric pain for many years. Computed tomography revealed an atrophic pancreas with a dilated pancreatic duct (18 mm) obstructed by a stone sized 1.3 cm. The patient underwent laparoscopic pancreatic duct exploration, used electrohydraulic lithotripsy for pancreatic duct stones, and then placed pancreaticoduodenal internal drainage with primary closure of the pancreatic duct. The operative time was 185 min, and the total blood loss was around 50 ml without intraoperative complication. The patient was discharged from the hospital on postoperative day 5 uneventfully. The epigastric pain symptoms dramatically decreased in the follow-up visit after one month. CLINICAL DISCUSSION: We combined several minimally invasive techniques to treat a chronic pancreatitis patient with a stone forming in the main duct in this patient. We used lithotripsy and internal drainage without the need for anastomosis. To our knowledge, this is the first report on this technique in literature. We found this technique is safe and applicable in selected patients to treat pancreatic stones with the dilated pancreatic duct. CONCLUSIONS: In this case, we demonstrate a novel surgical treatment option for chronic pancreatitis with a simple and effective technique to manage pancreatic stones in chronic pancreatitis patients.

Predicting the severity of COVID-19 patients using the CD24-CSF1R index in whole blood samples.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.

Isolation and Identification of Constituents Exhibiting Antioxidant, Antibacterial, and Antihyperuricemia Activities in Piper methysticum Root.

The aqueous extract of kava (Piper methysticum) root is known as a traditional beverage for daily intake in the Western Pacific Islands, such as Fiji, Tonga, and Vanuatu, to induce relaxation and health-beneficial effects. In this study, the antioxidant, anti-hyperuricemia, and antibacterial properties of kava root were investigated through the isolation and purification of bioactive compounds in ten fractions separated by column chromatography (CC). They included six flavonoids, 5-hydroxy-4',7-dimethoxyflavanone (C1), matteucinol (C2), isosakuranetin (C3), 5,7- dimethoxyflavanone (C4), 2',4'-dihydroxy-6'-methoxydihydrochalcone (in MC5) and alpinetin (C10), and seven kavalactones, 5,6-dehydrokawain (DK) (in MC5 and C6), kavain (in MC7), yangonin (in MC7 and C8), dihydro-5,6-dehydrokavain (DDK) (in MC9), 7,8-dihydromethysticin (in MC9), dihydromethysticin (in MC9), methysticin (in MC9). The chemical structures of the compounds were illustrated by the analyses of gas chromatography-mass spectrometry (GC-MS), electrospray ionization-mass spectrometry (ESI-MS), nuclear magnetic resonance (1H and 13C-NMR), and X-ray diffraction. The evaluation of the free radical scavenging activity of the isolated substances via the DPPH and ABTS assays revealed that C3 (IC50: ABTS = 76.5; DPPH = 74.8 µg/mL) possessed the strongest antioxidant property. In terms of anti-hyperuricemia activity evaluated via the xanthine oxidase inhibitory in vitro assay, the compound C10 was the most promising inhibitor, revealing an IC50 of 134.52 µg/mL. The two kavalactone mixtures in MC5 and a pure compound C6 inhibited the growth of bacteria Listeria monocytogenes, while MC7 can constrain the development of Klebsiella pneumoniae. This is the first study to isolate, purify, and identify the flavonoids isosakuranetin, 2',4'-dihydroxy-6'-methoxydihydrochalcone and alpinetin in kava root and report their pharmaceutical potential. The identified bioactive compounds showed potent antioxidant, anti-hyperuricemia, and antibacterial activity and thus can enhance the value of beverages and foods derived from kava root.

Seaport throughput forecasting and post COVID-19 recovery policy by using effective decision-making strategy: A case study of Vietnam ports.

This study deals with the dynamic interactions between seaports and decision-making strategy for seaport operations by utilizing four-dimensional fractional Lotka-Volterra competition model under frequently disrupted by time-delay factor. Nonlinear analysis methods, including equilibrium analysis, stability evaluation, and time series investigation, are intensely explored to describe the cooperation and competition dynamics in maritime logistics. The dynamical analysis indicates that the port competition system shows a complex and highly nonlinear behaviour, notably illustrating unstable equilibria and even chaotic phenomena. Besides, nonlinear dynamical interactions in seaport management have been analysed by exploiting fractional calculus (FC) and system dynamics theory. Novel multi-criteria decision-making strategies realized by the neural network prediction controller (NNC) and adaptive fractional-order super-twisting sliding mode control (AFOSTSM) have been presented for dealing with throughput dynamics under parametric perturbations and external disturbances. Particularly, the active control algorithms are implemented to ensure the recovery strategy for throughput growth of Vietnam ports in the post-coronavirus (COVID-19) pandemic era. The case study has confirmed the efficacy of the proposed strategy by using system dynamics and control theory. The simulation results show that the average growth rates of container throughput can be ensured up to 7.46% by exploiting resilience management scheme. The presented method can be also utilized for providing managerial insights and solutions on efficient port operations. In addition, the control strategies with neural network forecasting can help managers obtain timely and cost-effective decision-making policy for port sustainability against unprecedented impacts on global supply chains related to COVID-19 pandemic.

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings.

The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and optimizing the hyperparameters of a prediction model is necessary. This study aimed to propose a hybrid artificial intelligence model for forecasting one-day ahead time-series energy consumption in buildings. The proposed model was developed based on the integration of the Seasonal Autoregressive integrated Moving average, the Firefly-inspired Optimization algorithm, and the support vector Regression (SAMFOR). A large dataset of energy consumption in 30-min intervals, temporal data, and weather data from six real-world buildings in Vietnam was used to train and test the model. Sensitivity analyses were performed to identify appropriate model inputs. Comparison results show that the SAMFOR model was more effective than the others such as the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR), SARIMA-SVR, and random forests (RF) models. Evaluation results on real-world building depicted that the proposed SAMFOR model achieved the highest accuracy with the root-mean-square error (RMSE) of 1.77 kWh in, mean absolute percentage error (MAPE) of 9.56%, and correlation coefficient (R) of 0.914. The comparison results confirmed that the SAMFOR model was effective for forecasting one-day-ahead energy consumption. The study contributes to (1) the knowledge domain by proposing the hybrid SAMFOR model for forecasting energy consumption in buildings; and (2) the state of practice by providing building managers or users with a powerful tool for analyzing and improving building energy performance.

Cross-cultural measurement invariance of the Quality of Life Enjoyment and Satisfaction Questionnaire-Short form across ten countries: the application of Bayesian approximate measurement invariance.

BACKGROUND: The Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF) is the most frequently used generic quality of life (QOL) measure in many countries and cultures worldwide. However, no single study has been carried out to investigate whether this questionnaire performs similarly across diverse cultures/countries. Accordingly, this study aimed to assess the cross-cultural measurement invariance of the Q-LES-Q-SF across ten different countries. METHODS: The Q-LES-Q-SF was administrated to a sample of 2822 university students from ten countries: Bangladesh, Brazil, Croatia, India, Nepal, Poland, Serbia, Turkey, the United Arab Emirates, and Vietnam. The Bayesian approximate measurement invariance approach was used to assess the measurement invariance of the Q-LES-Q-SF. RESULTS: Approximate measurement invariance did not hold across the countries for the Q-LES-Q-SF, with only two out of 14 items being non-invariant; namely items related to doing household and leisure time activities. CONCLUSIONS: Our findings did not support the cross-cultural measurement invariance of the Q-LES-Q-SF; thus, considerable caution is warranted when comparing QOL scores across different countries with this measure. Item rewording and adaptation along with calibrating non-invariant items may narrow these differences and help researchers to create an invariant questionnaire for reliable and valid QOL comparisons across different countries.

The MAPS: Toward a Novel Mobility Assistance System for Visually Impaired People.

This paper introduces the design of a novel indoor and outdoor mobility assistance system for visually impaired people. This system is named the MAPS (Mobility Assistance Path Planning and orientation in Space), and it is based on the theoretical frameworks of mobility and spatial cognition. Its originality comes from the assistance of two main functions of navigation: locomotion and wayfinding. Locomotion involves the ability to avoid obstacles, while wayfinding involves the orientation in space and ad hoc path planning in an (unknown) environment. The MAPS architecture proposes a new low-cost system for indoor-outdoor cognitive mobility assistance, relying on two cooperating hardware feedbacks: the Force Feedback Tablet (F2T) and the TactiBelt. F2T is an electromechanical tablet using haptic effects that allow the exploration of images and maps. It is used to assist with maps' learning, space awareness emergence, path planning, wayfinding and effective journey completion. It helps a VIP construct a mental map of their environment. TactiBelt is a vibrotactile belt providing active support for the path integration strategy while navigating; it assists the VIP localize the nearest obstacles in real-time and provides the ego-directions to reach the destination. Technology used for acquiring the information about the surrounding space is based on vision (cameras) and is defined with the localization on a map. The preliminary evaluations of the MAPS focused on the interaction with the environment and on feedback from the users (blindfolded participants) to confirm its effectiveness in a simulated environment (a labyrinth). Those lead-users easily interpreted the system's provided data that they considered relevant for effective independent navigation.

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings.

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0-1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam.

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments.

Rapid Identification of Geographical Origin of Commercial Soybean Marketed in Vietnam by ICP-MS.

Inductively coupled plasma mass spectrometry (ICP-MS) analytical method was used to determine the content of 40 elements in 38 soybean samples (Glycine Max) from 4 countries. Multivariate statistical methods, such as principal components analysis (PCA), were performed to analyze the obtained data to establish the provenance of the soybeans. Although soybean is widely marketed in many countries, no universal method is used to discriminate the origin of these cereals. Our study introduced the initial step to the identification of the geographical origin of commercial soybean marketed in Vietnam. The analysis pointed out that there are significant differences in the mean of 33 of the 40 analyzed elements among 4 countries' soybean samples, namely, 11B, 27Al, 44Ca, 45Sc, 47Ti, 55Mn, 56Fe, 59Co, 60Ni, 63Cu, 66Zn, 69Ga, 75As, 78Se, 85Rb, 88Sr, 89Y, 90Zr, 93Nb, 95Mo, 103Rh, 137Ba, 163Dy, 165Ho, 175Lu, 178Hf, 181Ta, 182W, 185Re, 197Au, 202Hg, 205Tl, and 208Pb. The PCA analysis showed that the soybean samples can be classified correctly according to their original locations. This research can be used as a prerequisite for future studies of using the combination of elemental composition analysis with statistical classification methods for an accurate provenance establishment of soybean, which determined a variation of key markers for the original discrimination of soybean.

Molecular Weight-Dependent Physical and Photovoltaic Properties of Poly(3-alkylthiophene)s with Butyl, Hexyl, and Octyl Side-Chains.

A series of poly-3-alkylthiophenes (P3ATs) with butyl (P3BT), hexyl (P3HT), and octyl (P3OT) side-chains and well-defined molecular weights (MWs) were synthesized using Grignard metathesis polymerization. The MWs of P3HTs and P3OTs obtained via gel permeation chromatography agreed well with the calculated MWs ranging from approximately 10 to 70 kDa. Differential scanning calorimetry results showed that the crystalline melting temperature increased with increasing MWs and decreasing alkyl side-chain length, whereas the crystallinity of the P3ATs increased with the growth of MWs. An MW-dependent red shift was observed in the UV-Vis and photoluminiscence spectra of the P3ATs in solution, which might be a strong evidence for the extended effective conjugation occurring in polymers with longer chain lengths. The photoluminescence quantum yields of pristine films in all polymers were lower than those of the diluted solutions, whereas they were higher than those of the phenyl-C61-butyric acid methyl ester-blended films. The UV-Vis spectra of the films showed fine structures with pronounced red shifts, and the interchain interaction-induced features were weakly dependent on the MW but significantly dependent on the alkyl side-chain length. The photovoltaic device performances of the P3BT and P3HT samples significantly improved upon blending with a fullerene derivative and subsequent annealing, whereas those of P3OTs mostly degraded, particularly after annealing. The optimal power conversion efficiencies of P3BT, P3HT, and P3OT were 2.4%, 3.6%, and 1.5%, respectively, after annealing with MWs of ~11, ~39, and ~38 kDa, respectively.

The management of tetanus in adults in an intensive care unit in Southern Vietnam.

Background: Tetanus remains common in many low- and middle-income countries (LMICs) yet the evidence base guiding management of this disease is extremely limited, particularly with respect to contemporary management options. Sharing knowledge about practice may facilitate improvement in outcomes elsewhere. Methods: We describe clinical interventions and outcomes of 180 adult patients ≥16 years-old with tetanus enrolled in prospective observational studies at a specialist infectious diseases hospital in Southern Vietnam. Patients were treated according to a holistic management protocol encompassing wound-care, antitoxin, antibiotics, symptom control, airway management, nutrition and de-escalation criteria. Results: Mortality rate in our cohort was 2.8%, with 90 (50%) patients requiring mechanical ventilation for a median 16 [IQR 12-24] days. Median [IQR] duration of ICU stay was 15 [8-23] days.  Autonomic nervous system dysfunction occurred in 45 (25%) patients. Hospital acquired infections occurred in 77 (43%) of patients. Conclusion: We report favourable outcomes for patients with tetanus in a single centre LMIC ICU, treated according to a holistic protocol. Nevertheless, many patients required prolonged intensive care support and hospital acquired infections were common.

The pandemic semesters: Examining public opinion regarding online learning amidst COVID-19.

The current educational disruption caused by the COVID-19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID-19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID-19, Corona, e-learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID-19, online learning, schools, distance learning, e-learning, students, and education were clustered among each other. The number of daily COVID-19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.