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Abstract This phase 3 trial compared safety, tolerability, immunogenicity and efficacy of the self-amplifying mRNA COVID-19 vaccine, ARCT-154, with ChAdOx1-S adenovirus-vector vaccine. In four centers in Vietnam adult participants aged 18‒85 years were randomly assigned to receive two doses, 28 days apart, of either ARCT-154 (n = 1186) or ChAdOx1-S (n = 1180). Both vaccines were well tolerated with similar safety and reactogenicity profiles consisting of mainly mild-to-moderate solicited adverse events and few related serious adverse events. Higher neutralizing antibody responses persisting to one-year post-vaccination after ARCT-154 compared with ChAdOx1-S were associated with a generally higher efficacy against COVID-19. In an exploratory analysis relative vaccine efficacy of ARCT-154 vs. ChAdOx1-S against any COVID-19 from Day 36 to Day 394 was 19.8% (95% CI: 4.0–33.0). Self-amplifying mRNA vaccine offers potential immunological advantages in terms of immunogenicity and efficacy over adenovirus-vector vaccine without compromising safety.

This study examines a cooperative non-orthogonal multiple access (NOMA) network utilizing an energy-constrained unmanned aerial vehicle (UAV) relay (UAVR) to expand coverage and improve network throughput. In order to provide energy to the UAVR, we consider the hybrid simultaneous wireless information and power transmission (SWIPT) method, which allows the UAVR to harvest energy from the source (i.e., sink node) signal. Herein, a hybrid time switching (TS)-based and power splitting (PS)-based relaying scheme is applied to improve the UAVR’s energy harvesting (EH) efficiency and the system performance. Given this context, we derive the closed-form expression of the outage probability (OP) for the sensors to evaluate the network performance. Based on the achieved analytical results, we apply the bat algorithm optimization (BAO) method to determine the optimal working point (as a fraction of received power and power allocation coefficients, and the 3-D positions of the UAVR) for the system such that the OP is minimized. The numerical analysis indicates that BAO is effective in both exploring and exploiting solutions, making it a suitable choice for similar non-convex optimization problems in continuous search spaces for cooperative NOMA IoT networks.

The research aims to investigate the support suppliers’ decision making for Textile and Garment Vietnamese Enterprises based on using association rules mining. In order to increase the competitive advantage over other enterprises, Vietnamese enterprises are gradually shifting their production methods from CMT (Cut-Make-Trim) to FOB (Free On Board), ODM (Original Design Manufacturing), and OBM (Original Brand Manufacturing). One of the important steps for this transformation is to be self-sufficient in sourcing raw materials from selected suppliers with sufficient capabilities, meeting the requirements of price, and CSR (corporate social responsibility) to enable Vietnamese enterprises to enjoy tax incentives. The Vietnamese Enterprises participate in the textile and garment production process through importing raw materials from other countries such as China, Korea, etc. Therefore, the selection supplier issue is very important to them to enhance their business products. This study researches on finding positive ARs and negative ARs in Textile and Garment data domain via three scenarios to define the relationship of alternative criteria for selection suppliers. By using alternative method of ARM (Association Rule Mining), the experimental results show that Textile and Garment enterprises in Vietnam appreciate the CSR factor highly. This is completely in line with the previous survey research on the new factor in the supplier selection criteria of Textile and Garment enterprises in Vietnam. In another word, in this field, the CSR plays as an important role to reflect company’s image and it is the first of decision choice. The other criteria are considered after including Cost, Quality, and Delivery respectively. The remain factors of Service, Capability, Relationship, and Sourcing Country are not highly evaluated via the experiments. With different scenarios along with the association rules mined, they reflect different sets of criteria that textile and garment enterprises use to select their suppliers. Furthermore, the rare rules show situations that are very unlikely but have high reliability in reality. Based on the findings, the factors will show the essentials for suppliers to improve themselves to meet the requirements.

Abstract Background Streptococcus pneumoniae (S. pneumoniae), Haemophilus influenzae (H. influenzae), and Neisseria meningitidis (N. meningitidis) are leading causes of childhood bacterial meningitis and preventable by vaccines. The aim of this hospital-based sentinel surveillance is to describe the epidemiological characteristics of pneumococcal meningitis, including disease burden, and to provide baseline data on pneumococcal serotype distribution to support decision making for pneumococcal conjugate vaccine (PCV) introduction in Vietnam. Methods Surveillance for probable bacterial meningitis in children 1–59 months of age is conducted in three tertiary level pediatric hospitals: one in Hanoi and two in Ho Chi Minh City. Cerebrospinal fluid (CSF) specimens were collected via lumbar puncture from children with suspected meningitis. Specimens were transferred immediately to the laboratory department of the respective hospital for cytology, biochemistry, and microbiology testing, including culture. PCR testing was conducted on CSF specimens for bacterial detection (S. pneumoniae, H. influenzae, and N. meningitidis) and pneumococcal serotyping. Results During 2015–2018, a total of 1,803 children with probable bacterial meningitis were detected; 1,780 had CSF specimens available for testing. Of 245 laboratory-confirmed positive cases, the majority were caused by S. pneumoniae (229,93.5%). Of those with S. pneumoniae detected, over 70% were caused by serotypes included in currently available PCV products; serotypes 6 A/6B (27.1%), 14 (19.7%), and 23 F (16.2%) were the most common serotypes. Children with laboratory-confirmed pneumococcal meningitis were more likely to live in Hanoi (p

Vasily Lukyanov,1 Purvee Parikh,2 Manish Wadhwa,2 Alexandria Dunn,2 Roderick van Leerdam,1 Johan Engdahl,3 Goran Medic1 1Chief Medical Office, Philips Healthcare, Eindhoven, Netherlands; 2Chief Medical Office, Philips, San Diego, CA, USA; 3Karolinska Institutet, Danderyd Hospital, Stockholm, SwedenCorrespondence: Goran Medic, Chief Medical Office, Philips Healthcare, High Tech Campus 52, Eindhoven, Netherlands, Tel +31-(0)6-18 19 37 82, Email goran.medic@philips.comBackground: Patients who have experienced a cryptogenic stroke (CS) may benefit from extended monitoring and possible earlier detection of atrial fibrillation (AF), allowing for the timely initiation of appropriate pharmacotherapy.Objective: This economic study aimed to evaluate the clinical and cost outcomes of using mid-term cardiac monitors (referred to as “ePatch”) versus ILR-only in post-CS patients in the UK, Netherlands (NL) and Sweden.Methods: An existing cost-minimization model was modified to fit healthcare settings in the UK, Netherlands and Sweden. The model’s target population was composed of adult patients who had previously experienced a CS, but had no documented history of AF. The model compares the one-year direct medical costs between two groups: one group receiving wearable ePatch, the other group proceeding directly to ILR.Results: When applied to a group of 1,000 patients, the ePatch versus ILR approach resulted in cost savings, due to combination of reduced expenses and decreased modelled occurrence of recurrent strokes in all three countries studied. In the base case analysis, the cost savings per patient with detected AF for ePatch ranged from 3.4– 6.0 times, depending on the country.Conclusion: Utilizing ePatch extended wear Holter for mid-term ECG monitoring in CS patients represents a cost-saving alternative to monitoring with ILR. The cost savings were achieved by reducing device expenses and by prevention of recurrent strokes via earlier anticoagulation initiation. Preventing recurrent strokes in this population is highly significant, as it can lead to improved long-term health outcomes and reduced overall healthcare costs.Keywords: outpatient cardiac monitoring, stroke, holter, atrial fibrillation, electrocardiography, economic evaluation, secondary prevention, telemedicine, remote monitoring

Background: We conducted this study to investigate and evaluate the correlation between the serum parathyroid hormone (PTH) concentrations and 24-hour ABPM parameters in hypertensive patients Methods and Results: A cross-sectional descriptive study was conducted in the Cardiology Department of Hue Central Hospital on 74 hypertensive patients. All patients underwent 24-hour ABPM. Serum PTH was measured using a sandwich electrochemiluminescence immunoassay on a Cobas 8000. The mean values of daytime systolic blood pressure (SBP), daytime diastolic blood pressure (DBP), nighttime SBP, and nighttime DBP were 127.74±14.44 mmHg, 77.24±9.01 mmHgm, 122.86±16.57 mmHg, and 76.92±11.69 mmHg, respectively. The median value of serum PTH concentration was 68.6 pg/mL (39.83 pg/mL - 87.46 pg/mL). All patients were divided into two groups. Group 1 included 40 AH patients with serum PTH >65 pg/mL, and Group 2 included 34 AH patients with serum PTH≤65 pg/mL. The mean values of 24-hour SBP, nighttime SBP, nighttime DBP, and nighttime mean blood pressure (MBP) in Group 1 were significantly higher than in Group 2 (P

Yildiray Topcu,1 Robbert JJ Gobbens,2– 5 Tjeerd van der Ploeg,2 Fatih Tufan6 1Prof. Dr. Cemil Taşcıoğlu Şehir Hastanesi, Istanbul, Turkiye; 2Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands; 3Zonnehuisgroep Amstelland, Amstelveen, the Netherlands; 4Department Family Medicine and Population Health, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; 5Tranzo, Tilburg University, Tilburg, the Netherlands; 6Istanbul Esenler Avicenna Hospital, Department of Internal Medicine and Geriatrics, Istanbul, TurkiyeCorrespondence: Fatih Tufan, Assoc. Prof. of Geriatrics, Istanbul Esenler Avicenna Hospital, Department of Internal Medicine and Geriatrics, Istanbul, Turkiye, Tel +90 5331683458, Email fatihtufan@gmail.comPurpose: To our knowledge, there have been no comparative studies evaluating the associations of frailty defined using the Tilburg Frailty Indicator (TFI), frailty phenotype by Fried et al, and FRAIL scale with all-cause mortality in Turkiye. In this study, we aimed to evaluate the ability of these instruments in predicting all-cause mortality in outpatients admitted to the outpatient geriatrics clinic of a university hospital.Patients and Methods: This historical prospective study was performed in the geriatrics outpatient clinic of a university hospital in Istanbul, Turkiye. Consecutive older adults (aged ≥ 70 years) who provided written informed consent were enrolled in the study. The survival status of participants was checked electronically using the official death registry system. Univariate analyses and multivariate Cox regression analyses were performed to determine the independent predictors of mortality.Results: A total of 198 participants with a median age of 77 years were enrolled. During the median follow-up period of 2236 days, 54 (27.3%) patients died. In univariate analyses, male sex, history of falls in the previous year, dependency in instrumental activities of daily living, malnutrition, and frailty with respect to the phenotype by Fried et al, FRAIL scale, and TFI were associated with mortality. In multivariate Cox regression analyses, frailty according to each of the three frailty instruments, male sex, older age, history of falls, and malnutrition or malnutrition risk were independently associated with mortality. The Fried scale was the best frailty tool among the three frailty instruments used to predict all-cause mortality.Conclusion: The findings of this study suggest that frailty, determined using each of the three instruments used in the present study, is independently associated with all-cause mortality in patients admitted to the outpatient geriatrics clinic of a university hospital in Turkiye. The Fried scale appears to be the best for predicting all-cause mortality.Keywords: Turkiye, frailty, mortality, malnutrition, falls, dependency

In this study, activated carbon from dragon fruit peel was synthesized and applied for its ability to adsorb organic pigments in water. The results obtained from the SEM and FTIR analyzes revealed the presence of the characteristic functional groups of activated carbon. The adsorption capacity of synthetic activated carbon was evaluated against methylene blue dye. This study also evaluated factors affecting the adsorption process, including time, content, temperature, concentration, and solution pH. The optimal adsorption conditions were recorded at pH 6, concentration 95 mg/L, time 43 min, and temperature 30 °C. At optimal adsorption conditions, the adsorption capacity predicted from the model is said to be 54.12 mg/g with an efficiency of 27.5%. The experimental data of the study were evaluated using adsorption kinetic models and adsorption isotherm models of the material. The results showed that the activated carbon material from dragon fruit peel is a potential material in the application for adsorbing pollutant dyes in wastewater.

Background and objective: Medical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as “wrong level surgery”. However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images. Methods: We proposed an integrated system with three key modules: hand landmark detection, hand pointing, and hand gesture recognition. A proposed depth enhancement algorithm is combined with a deep learning hand landmark detector to generate hand landmarks. Based on the designed system, a proposed hand-pointing system combined with projection and ray-pointing techniques allows for reducing fatigue during manipulation. A proposed landmark geometry constraint algorithm and deep learning method were applied to detect six gestures including click, open, close, zoom, drag, and rotation. Additionally, a control menu was developed to effectively activate common functions. Results: The proposed hand-pointing system allowed for a large control range of up to 1200 mm in both vertical and horizontal direction. The proposed hand gesture recognition method showed high accuracy of over 97% and real-time response. Conclusion: This paper described the contagious infection-free medical interaction system that enables precise and effective manipulation of medical images within the large control range, while minimizing hand fatigue.

Background Cell-based quadrivalent influenza vaccines (QIVc) can increase effectiveness against seasonal influenza by avoiding mismatch from egg adaption of vaccine viruses. This study evaluates the population-level cost-effectiveness and impacts on health outcomes of QIVc versus an egg-based vaccine (QIVe) in children aged 6 months to 17 years in the US.Research Design and Methods A dynamic age-structured susceptible-exposed-infected-recovered model was used to simulate influenza transmission in low and high incidence seasons for two scenarios: 1. QIVe for 6 months–17 year-olds, QIVc for 18–64 year-olds, and adjuvanted QIV (aQIV) for ≥ 65 year-olds, and 2. QIVc for 6 months–64 year-olds, and aQIV for ≥ 65 year-olds. Probabilistic sensitivity analysis was performed to account for uncertainty in parameter estimates. Cost-effectiveness was evaluated as incremental cost-effectiveness ratios (ICERs).Results Extension of QIVc to children resulted in 3–4% reductions in cases (1,656,271), hospitalizations (16,688), and deaths (2,126) at a population level in a high incidence season, and 65% reductions (cases: 2,856,384; hospitalizations: 31667; deaths: 4,163) in a low incidence season. Use of QIVc would be cost-saving, with ICERs of -$16,427/QALY and -$8,100/QALY from a payer perspective and -$22,669/QALY and -$15,015/QALY from a societal perspective, for low and high incidence seasons respectively. Cost savings were estimated at approximately $468 million and $1.366 billion for high and low incidence seasons, respectively.Conclusion Use of QIVc instead of QIVe in children > 6 months of age in the US would reduce the disease burden and be cost-saving from both a payer and societal perspective.

Abstract Background Dengue is the most important arboviral disease, and Aedes aegypti and Aedes albopictus are the major vectors. It is one of the most recent emerging diseases in Mizoram state with a fast-growing burden over the years. The present study aimed to explore the epidemiological patterns of dengue in Mizoram, and to identify vector mosquito species. Methods In the present study, two years secondary data of dengue disease in Mizoram was studied. Dengue vector mosquitoes and dengue virus serotypes circulating in vector mosquitoes in Aizawl City were identified by sequence analysis of COX1 and C-prM genes respectively. Results In Mizoram State, the annual case prevalence of dengue was found to be 300.45 per 100,000 persons. Seasonality of the disease was also noted, with the peak season occurring from July to December. There is a significant positive relationship between age and the disease prevalence (p

Abstract Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as “proof of concept” regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

Aysun Damci,1,2 Janneke GJ Hoeijmakers,1,2 Marlies den Hollander,3– 5 Catharina G Faber,1,2 Sophie Waardenburg,6,7 Charlotte CM van Laake-Geelen,3– 5 Albère JA Köke,3,4 Jeanine AMCF Verbunt3– 5 1MHeNS, Mental Health and Neuroscience Research Institute, Maastricht University, Maastricht, 6200 MD, The Netherlands; 2Department of Neurology, Maastricht University Medical Center+, Maastricht, 6229 HX, The Netherlands; 3Care and Public Health Research Institute, Maastricht university, Maastricht, 6200 MD, The Netherlands; 4Department of Rehabilitation Medicine, Maastricht University Medical Center+, Maastricht, 6229 HX, The Netherlands; 5Adelante Zorggroep, Center of Rehabilitation Medicine, Hoensbroek, 6432 CC, The Netherlands; 6Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, Maastricht, 6229 HX, The Netherlands; 7Department of Anesthesiology and Pain Medicine, Maastricht University Medical Center+, Maastricht, 6229 HX, The NetherlandsCorrespondence: Aysun Damci, Department of Neurology, Maastricht University Medical Centre+, P.O. Box 5800, Maastricht, 6202 AZ, The Netherlands, Tel +31-43-3877059, Fax +31-43-3877055, Email aysun.damci@mumc.nlPurpose: Small fiber neuropathy (SFN) is characterized by neuropathic pain, associated with decreased quality of life (QOL). It remains unclear which psychosocial factors play a role in SFN. The experience sampling method (ESM) allows a profound understanding of the real-time fluctuations in reaction to events. The main goal of this study was to increase knowledge of the interrelationships between pain intensity, physical activity, and psychosocial factors in patients with SFN in daily practice over time.Patients and Methods: A prospective observational ESM study with the PsyMate© application (smart-eHealth GmbH, Luxembourg) was conducted at the Adelante location of Maastricht University Medical Center+ in the Netherlands. Participants with idiopathic SFN, older than 18 years, with an indication for rehabilitation, were included. Pain intensity, fatigue, positive and negative affect, physical activity, avoidance behavior, and pain catastrophic thoughts were incorporated into the ESM questions. Participants received 10 beep signals per day followed by the above-mentioned questions, for consecutive 7 days. The results were analyzed with linear mixed-effect models.Results: Twenty-one participants were included with a mean age of 48.24 (SD ± 13.89) years, of whom 76.2% were female. More pain (now) resulted in more physical activity (later) (a) and more physical activity (now) resulted in more pain (later) (b). The first association (a) is influenced by pain catastrophic thoughts and fatigue, and the second (b) by an increase in affective states and a lower level of avoidance behavior.Conclusion: In idiopathic SFN, pain intensity, and physical activity showed a 2-sided association, influenced by catastrophic thoughts, fatigue, affective states, and avoidance behavior.Keywords: chronic pain, rehabilitation therapy, biopsychosocial factors, small fiber neuropathy

Sayeh Pourjavan,1 François Gouverneur,2 Benoit Macq,2 Thomas Van Drooghenbroeck,2 Patrick De Potter,1 Antonella Boschi,1 Adil El Maftouhi3 1Department of Ophthalmology, Cliniques Universitaires Saint Luc, UCL, Brussels, Belgium; 2Institute for Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Louvain School of Engineering, UCLouvain, Louvain-la-Neuve, Belgium; 3Centre Ophtalmologique de RIVE, Geneve, SwitzerlandCorrespondence: Sayeh Pourjavan, Email Sayeh.pourjavan@saintluc.uclouvain.bePurpose: To evaluate the discriminative power of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, identifying the best image combination for differentiating glaucoma from healthy eyes using deep learning (DL) with a convolutional neural network (CNN).Methods: This cross-sectional study included 157 subjects contributing 1,106 eye scans. We used en-face images of the superficial and choroid layers for OCTA-based vessel density and OCT-based structural thickness of the macula (M) and optic disc (D). Images were preprocessed, resized, and normalized for CNN analysis. The CNN architecture had two components: one extracted features for each image type (OCT-D, OCT-M, OCTA-D, OCTA-M), while the second combined these features to classify eyes as healthy or glaucomatous. Performance was measured by accuracy, sensitivity, specificity, and area under the curve (AUC).Results: For OCT images, the D+M combination outperformed disc (D) or macula (M) alone in three of the four metrics. For OCTA images, D+M also performed better than D or M alone, with D+M outperforming disc (D) in all criteria. Across all metrics for combined OCT+OCTA images, D+M performed better than D or M alone, and the macula (M) outperformed the disc (D). In disc (D) imaging, OCTA outperformed both OCT and OCT+OCTA in accuracy, sensitivity, and specificity, while OCT+OCTA had a higher AUC. OCTA consistently outperformed OCT and OCT+OCTA across all metrics for combined D+M images.Conclusion: The OCTA D+M combination performed best, followed by the OCT+OCTA D+M combination. When both en-face images are available, OCTA is preferred. Always include both disc and macula images for optimal diagnosis.Keywords: glaucoma, OCTA, optic disc, macula, vessel density, deep learning, AI-Glau-OCTA, health expenditure

Acid sulfate soils pose significant challenges to rice production due to their negative impact on root development and nutrient uptake, reducing rice yield and quality. The excessive use of fungicides by farmers to control rice diseases has exacerbated the issue, as it contributes to environmental pollution and poses health risks. Our study aimed to isolate indigenous bacteria from rice grown in acid sulfate soils with potential biocontrol activity against common fungal pathogens in Vietnam. Using 16S rRNA amplicon sequencing, a total of 91 bacterial strains were identified up to the genus level. Bacillaceae were predominant on healthy rice plants, whereas Pseudomonas spp. also occurred on plants infested with the rice blast pathogen Pyricularia oryzae. Genome sequencing, protein orthology, and multilocus sequence analyses revealed the presence of six taxonomic groups of Bacillaceae: Rossellomorea marisflavi (basionym: Bacillus marisflavi), Priestia megaterium (basionym: Bacillus megaterium), Priestia koreensis (basionym: Bacillus koreensis), Bacillus thuringiensis, Bacillus altitudinis, and Bacillus siamensis. Dual-culture assays showed that most strains had antifungal activity against Pyricularia oryzae, Rhizoctonia solani AG2-1, and Bipolaris oryzae, whereas only B. siamensis and two B. thuringiensis strains were active against Rhizoctonia solani AG1-IA. R. marisflavi strains were in general inactive. Genome mining identified various biosynthetic gene clusters associated with specialized metabolite production, some of which were linked to potential antimicrobial activity. Production of these metabolites was confirmed by chemical analysis. These results suggest that indigenous Bacillaceae strains from acid sulfate soils could be used as biocontrol agents for the sustainable management of rice diseases under acidic conditions.

Abstract Diabetic kidney disease (DKD) is the leading cause of end stage kidney failure worldwide, of which cellular insulin resistance is a major driver. Here, we study key human kidney cell types implicated in DKD (podocytes, glomerular endothelial, mesangial and proximal tubular cells) in insulin sensitive and resistant conditions, and perform simultaneous transcriptomics and proteomics for integrated analysis. Our data is further compared with bulk- and single-cell transcriptomic kidney biopsy data from early- and advanced-stage DKD patient cohorts. We identify several consistent changes (individual genes, proteins, and molecular pathways) occurring across all insulin-resistant kidney cell types, together with cell-line-specific changes occurring in response to insulin resistance, which are replicated in DKD biopsies. This study provides a rich data resource to direct future studies in elucidating underlying kidney signalling pathways and potential therapeutic targets in DKD.

The effects of hydrolysis of muskmelon pomace on fruit juice recovery efficiency, transmittance, total phenolic content, total ascorbic acid content, total flavonoid content, and total carotenoid content have been studied. The aim of this study was to evaluate the potential use of pectinase enzyme in the hydrolysis process, specifically looking at the effects of pH, temperature, time, and enzyme concentration on the production of effective muskmelon juice. Additionally, the optimal mixing ratio of ingredients such as melon juice content, Brix level, citric acid, and additives (pectin, sodium carboxymethyl cellulose [CMC], xanthan gum) was investigated to create a high-quality muskmelon juice product. The results showed that using a pectinase enzyme concentration of 0.2%, a pH of 5.0, and a hydrolysis temperature of 45°C for 120 min resulted in a muskmelon juice product with a hydrolyzed juice content of 20%, a Brix level of 12%, and a citric acid addition rate of 0.04%. The addition of xanthan gum at a rate of 0.03% also contributed to the desired flavor and color of the juice. These findings provide valuable insights for the development of effective muskmelon juice processes.

Breast cancer remains a prevalent global health challenge, with tumor-removal surgeries being among the most common treatments but often leading to aesthetic defects. Adipose-derived stem cell (ADSC)-enriched fat grafting in breast reconstruction offers promising therapeutic benefits. However, concerns about its oncological safety persist, particularly regarding the potential risks of promoting cancer recurrence. This study investigated the effects of ADSCs on breast cancer progression by coculturing ADSCs with the MCF-7 breast cancer cell line for a short cell cultivation period of 3 days. We performed an RNA-seq analysis to identify significant transcriptomic changes in cocultured MCF-7 cells and carried out functional enrichment analyses to uncover key biological pathways influenced by ADSCs. Our findings revealed that transcriptomic alterations in MCF-7 cells are linked to aggressive cancer traits, including the upregulation of epithelial–mesenchymal transition (EMT) and the HIF-1 signaling pathway, which indicate a shift toward aerobic glycolysis. Some of the observed gene expression changes also correlated with relapse risk and mortality. These findings underscore the need for further research to explore the implications of these genes and pathways in driving aggressive cancer phenotypes and assess the safety of ADSCs in clinical settings.

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert’s knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds.

Lung diseases often result in severe damage to the respiratory tract, and lead to a high risk of mortality within a short period of time. DL models based on ViT are considered to have promising advantages over CNN architectures in terms of computational efficiency, and accuracy when trained on large ImageNet datasets. In this study, we present a new DL approach based on the combination of CNN with ViT to improve the efficiency of pneumonia diagnosis using medical images. In the first stage, raw images are passed through a local filter to capture local relations on the inputs. The local filter block includes two convolutional layers with kernel 3 × 3. This local filtering method aims to enhance rich features before being fed into the patching layer of the ViT block. The proposed method is experimented on the benchmark chest X-ray dataset. The proposed method is evaluated and compared to some well-known models, which include ViT, VGG19, Resnet50, Densnet201. Experimental results demonstrated that the proposed approach based on CNN and ViT reaches higher efficiency with about 1% accuracy to the standard ViT model, and about 2% higher with VGG19, Resnet50, Densnet201 and smaller in model architecture.

Abstract Background Cataract surgery requires a high level of dexterity and experience to avoid serious intra- and post-operative complications. Proper surgical training and evaluation during the learning phase are crucial to promote safety in the operating room (OR). This scoping review aims to report cataract surgery training efficacy for patient safety and trainee satisfaction in the OR when using virtual reality simulators (EyeSi [Haag-Streit, Heidelberg, Germany] or HelpMeSee [HelpMeSee foundation, Jersey city, New Jersey, United States]) or supervised surgical training on actual patients programs in residents. Methods An online article search in the PubMed database was performed to identify studies proposing OR performance assessment after virtual-reality simulation (EyeSi or HelpMeSee) or supervised surgical training on actual patients programs. Outcome assessment was primarily based on patient safety (i.e., intra- and post- operative complications, OR performance, operating time) and secondarily based on trainee satisfaction (i.e., subjective assessment). Results We reviewed 18 articles, involving 1515 participants. There were 13 using the EyeSi simulator, with 10 studies conducted in high-income countries (59%). One study used the HelpMeSee simulator and was conducted in India. The four remaining studies reported supervised surgical training on actual patients, mostly conducted in low- middle- income countries. Training programs greatly differed between studies and the level of certainty was considered low. Only four studies were randomized clinical trials. There were 17 studies (94%) proposing patient safety assessments, mainly through intraoperative complication reports (67%). Significant safety improvements were found in 80% of comparative virtual reality simulation studies. All three supervised surgery studies were observational and reported a high amount of cataract surgeries performed by trainees. However, intraoperative complication rates appeared to be higher than in virtual reality simulation studies. Trainee satisfaction was rarely assessed (17%) and did not correlate with training outcomes. Conclusions Patient safety assessment in the OR remains a major concern when evaluating the efficacy of a training program. Virtual reality simulation appears to lead to safer outcomes compared to that of supervised surgical training on actual patients alone, which encourages its use prior to performing real cases. However, actual training programs need to be more consistent, while maintaining a balance between financial, cultural, geographical, and accessibility factors.

Abstract Background Two-drug regimens (2DRs) have been introduced in recent years to potentially reduce antiretroviral therapy (ART) toxicities and drug-drug interactions while demonstrating comparable efficacy to three-drug regimens (3DRs) for people with HIV (PWH). The objective of this study was to compare the real-world effectiveness and durability of a single-tablet 2DR of dolutegravir/lamivudine (DTG/3TC) with that of commonly prescribed 3DRs in ART-experienced, virologically suppressed PWH during the first 24 months of DTG/3TC availability in the United States. Methods Virologically suppressed (viral load [VL]

This study aimed to evaluate if integrating diverse molecular biomarkers in plasma and sputum could improve the diagnosis of lung cancer. The study analyzed miRNAs in plasma and bacterial DNA in sputum from 58 lung cancer patients and 62 cancer-free smokers using droplet digital PCR. The individual plasma miRNA and sputum bacterial biomarkers had sensitivities of 62–71% and specificities of 61–79% for diagnosing lung cancer. A panel of plasma miRNA or sputum bacterial biomarkers produced sensitivities of 79–85% and specificities of 74–82%. An integromic signature consisting of two miRNAs in plasma and three bacterial biomarkers in sputum had a higher sensitivity (87%) and specificity (89%) compared to individual biomarkers. The signature’s diagnostic value was confirmed in a validation cohort of 56 lung cancer patients and 59 controls, independent of tumor stage, histological type, and demographic factors. Integrating diverse molecular biomarkers in plasma and sputum could improve the diagnosis of lung cancer.

Abstract Predicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer‐based methods, although cutting‐edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi‐scale attention modules within transformer layers. This model employs spatio‐temporal transformer blocks, enriched with multi‐scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long‐sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.

Abstract Background Penicillin allergy adversely impacts patient care, yet most cases do not have true allergies. Clinicians require efficient, reliable clinical tools to identify low risk patients who can be safely de-labeled. Our center implemented the FIRSTLINE electronic point-of-care decision support tool to help non-allergist practitioners risk stratify patients with penicillin allergy. We sought to explore the reliability and validity of this tool in relation to allergist assessment and actual patient outcomes. We additionally compared it with two other published stratification tools, JAMA and PENFAST, to assess ability to accurately identify low risk patients appropriate for direct oral challenge. Methods In this single-center, retrospective, observational study, 181 pregnant females with self-reported penicillin allergy between July 2019 to June 2021 at BC Women’s Hospital, Vancouver, Canada were used to assess the reliability and validity of all three tools. Physician-guided history of penicillin use and symptoms were used for scoring. Results and recommendations were compared to actual patient outcomes after clinician decision for direct oral challenge or intradermal tests. We compared the performance of JAMA, PENFAST and FIRSTLINE. Results 181 patients were assessed. 176/181 (97.2%) patients were deemed not allergic. Each risk stratification tool labelled majority of patients as low risk with 88.4% of patients PENFAST 0–2, 60.2% of patients JAMA low risk, 86.7% of patients FIRSTLINE very low risk. Conclusion We demonstrate that our point-of-care electronic algorithm is reliable in identifying low risk pregnant patients, as compared to an allergist assessment. To our knowledge, this is the first study to provide direct comparison between multiple decision support tools using the same population, minimizing participant bias. Providing clinical algorithms to risk stratify patients, can enable healthcare professionals to safely identify individuals who may be candidates for direct penicillin oral challenges versus needing referral to specialists. This increases the generalizability and efficiency of penicillin allergy de-labeling.

Abstract Background Breast screening recommendation based on individual risk assessment is emerging as an alternative approach to improve compliance and efficiency to detect breast cancer (BC) early. In Vietnam, prior knowledge to stratify risk based on genetic factors is currently lacking. Methods This study recruited 892 BC patients and 735 healthy Vietnamese women from 2016 to 2021. DNA from blood samples of BC patients was first analyzed for pathogenic variants associated with hereditary breast and ovarian cancer syndrome (HBOC). For patients with no HBOC and healthy participants, DNA was genotyped for 398 BC susceptibility single-nucleotide polymorphism (SNPs) by next-generation sequencing to identify significantly associated SNPs and construct a polygenic risk score (PRS). Results The prevalence of HBOC predisposition gene mutations in Vietnamese women with BC was 5.4%. HBOC cases were significantly younger and enriched in the age group of 20–39 years old. In patients with no HBOC, we found 36 SNPs significantly associated with BC that were mostly similar to other Asian ethnicities; 34 of them were used to build a PRS model achieving an area under the receiver operating characteristics curve of 0.61 (95% CI: 0.56–0.68). Women in the top 1% PRS percentile had an odds ratio of 5.09 (95% CI: 3.10–7.86) while those in the bottom 1% had an odds ratio of 0.21 (95% CI: 0.09–0.39) to develop BC. Conclusions This study provides the first large datasets for HBOC gene analysis, BC susceptibility SNP association testing, and PRS modeling for Vietnamese women. Together, these data could aid the development of personalized BC screening recommendations for women in Vietnam.

The integrated and indivisible nature of the SDGs is facing implementation challenges due to the silo approaches. We present the three interconnected foci (SDG interactions, modeling, and tools) at the science-policy interface to address these challenges. Accounting for them will support accelerated SDG progress, operationalizing the integration and indivisibility principles.

This study introduces a highly efficient and straightforward method for synthesizing gold nanoparticles (AuNPs) within a glucosamine/alginate (GluN/Alg) nanocomposite via an ionotropic gelation mechanism in aqueous environment. The resulting nanocomposite, AuNPs@GluN/Alg, underwent thorough characterization using UV–vis, EDX, FTIR, SEM, TEM, SAED, and XRD analyses. The spherical AuNPs exhibited uniform size with an average diameter of 10.0 nm. The nanocomposites facilitated the recyclable reduction of organic dyes, including 2-nitrophenol, 4-nitrophenol, and methyl orange, employing NaBH4 as the reducing agent. Kinetic studies further underscored the potential of this nanocomposite as a versatile catalyst with promising applications across various industrial sectors.