Chemiresistive and resistive switching semiconductor based sensor for biomolecule detection

Recently, chemiresistive semiconductor, which varies its resistance or conductance status based on chemical phenomena at its surface, has been developed as a sensor device for biomolecule detection. Particularly, graphene has been one of the best example for the chemiresistive semiconductors, even for resistive switching semiconductors. In addition, the graphene is two-dimensional (2D) carbon structure having a large surface area, where significant biosensing applications have been continuously reported. In this study, we demonstrated reduced graphene oxide (rGO) biosensor structure for a stress hormone, i.e. cortisol, sensing. The device structure was stepwise self-assembly monolayers (SAMs) stacked by reduced graphene oxide between source and drain. Then, cortisol monoclonal antibody (c-Mab) was chemically tethered on reduced graphene oxide layer for the cortisol detection by its specific antigen-antibody binding. The current versus voltage (I-V) curve exhibited resistance changes and resistive switching I-V behaviors as a sensing mechanism, which demonstrated a unique possibility of rGO semiconductor based sensor. Also, chemiresistance change in the forms of resistance ratio was calibrated in terms of sensing cortisol concentration as shown in Figure 1. Please click Additional Files below to see the full abstract

Heart rate variability analysis in acute poisoning by cholinesterase inhibitors

Heart rate variability (HRV) has been associated with a variety of clinical situations. However, few studies have examined the association between HRV and acute poisoning. Organophosphate (OP) and carbamate inhibit esterase enzymes, particularly acetylcholinesterase, resulting in an accumulation of acetylcholine and thereby promoting excessive activation of corresponding receptors. Because diagnosis and treatment of OP and carbamate poisoning greatly depend on the severity of cholinergic symptoms, and because HRV reflects autonomic status, some HRV parameters may be of value in diagnosing OP and carbamate poisoning among patients visiting the emergency department. Patients who visited the emergency department of the study hospital between September 2008 and May 2010 with the chief complaint of acute poisoning or overdose were included. Cases that involved ingestion of OP or carbamate insecticides were classified as poisoning by cholinesterase inhibitors and compared with other cases of poisoning or overdose. The timedomain analysis included descriptive statistics of R-R intervals and instantaneous heart rates. The frequency-domain analysis used fast Fourier transformation. A Poincaré plot, which is a scatterplot of R-R intervals against the preceding R-R interval, was used for the nonlinear analysis. Very-low-frequency (VLF) power and the ratio of low-frequency-to-high-frequency power (LF/HF) were the most effective parameters for distinguishing cholinesterase inhibitor poisoning among cases of acute poisoning, with areas under the receiveroperating characteristic curve of 0.76 and 0.87, respectively. Cholinesterase inhibitor poisoning was a significant factor determining VLF power and the LF/HF ratio after adjusting for possible confounding variables, including age over 40, gender, and tracheal intubation. Frequency-domain parameters of HRV, such as VLF power and the LF/HF ratio, might be considered as potential diagnostic methods to distinguish cholinesterase inhibitor poisoning from other cases of intoxication in the early stages of emergency care

Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction

Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation. However, the high computational requirements of ViTs hinder their widespread implementation. To address this issue, researchers have proposed efficient hybrid transformer architectures that combine convolutional and transformer layers with optimized attention computation of linear complexity. Additionally, post-training quantization has been proposed as a means of mitigating computational demands. For mobile devices, achieving optimal acceleration for ViTs necessitates the strategic integration of quantization techniques and efficient hybrid transformer structures. However, no prior investigation has applied quantization to efficient hybrid transformers. In this paper, we discover that applying existing PTQ methods for ViTs to efficient hybrid transformers leads to a drastic accuracy drop, attributed to the four following challenges: (i) highly dynamic ranges, (ii) zero-point overflow, (iii) diverse normalization, and (iv) limited model parameters ($<$5M). To overcome these challenges, we propose a new post-training quantization method, which is the first to quantize efficient hybrid ViTs (MobileViTv1, MobileViTv2, Mobile-Former, EfficientFormerV1, EfficientFormerV2) with a significant margin (an average improvement of 8.32\% for 8-bit and 26.02\% for 6-bit) compared to existing PTQ methods (EasyQuant, FQ-ViT, and PTQ4ViT). We plan to release our code at \url{https://github.com/Q-HyViT}.Comment: 12 pages, 8 figure

The economic impact of COVID-19 interventions: A mathematical modeling approach

Prior to vaccination or drug treatment, non-pharmaceutical interventions were almost the only way to control the coronavirus disease 2019 (COVID-19) epidemic. After vaccines were developed, effective vaccination strategies became important. The prolonged COVID-19 pandemic has caused enormous economic losses worldwide. As such, it is necessary to estimate the economic effects of control policies, including non-pharmaceutical interventions and vaccination strategies. We estimated the costs associated with COVID-19 according to different vaccination rollout speeds and social distancing levels and investigated effective control strategies for cost minimization. Age-structured mathematical models were developed and used to study disease transmission epidemiology. Using these models, we estimated the actual costs due to COVID-19, considering costs associated with medical care, lost wages, death, vaccination, and gross domestic product (GDP) losses due to social distancing. The lower the social distancing (SD) level, the more important the vaccination rollout speed. SD level 1 was cost-effective under fast rollout speeds, but SD level 2 was more effective for slow rollout speeds. If the vaccine rollout rate is fast enough, even implementing SD level 1 will be cost effective and can control the number of critically ill patients and deaths. If social distancing is maintained at level 2 at the beginning and then relaxed when sufficient vaccinations have been administered, economic costs can be reduced while maintaining the number of patients with severe symptoms below the intensive care unit (ICU) capacity. Korea has wellequipped medical facilities and infrastructure for rapid vaccination, and the public&apos;s desire for vaccination is high. In this case, the speed of vaccine supply is an important factor in controlling the COVID-19 epidemic. If the speed of vaccination is fast, it is possible to maintain a low level of social distancing without a significant increase in the number of deaths and hospitalized patients with severe symptoms, and the corresponding costs can be reduced

Detection of COVID-19 epidemic outbreak using machine learning

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic

Medication intensification in diabetes in rural primary care: a cluster-randomised effectiveness trial

OBJECTIVE: To determine the effectiveness of a provider-based intervention to improve medication intensification among patients with diabetes. DESIGN: Effectiveness cluster-randomised trial. Baseline and follow-up cross-sections of diabetes physicians\u27 patients. SETTING: Eleven U.S. Southeastern states, 2006-2008. PARTICIPANTS: 205 Rural primary care physicians, 95 completed the study. INTERVENTION: Multicomponent interactive intervention including web-based continuing medical education (CME), performance feedback and quality improvement tools. PRIMARY OUTCOME MEASURES: Medication intensification, a dose increase of an existing medication or the addition of a new class of medication for glucose, blood pressure and lipids control on any of the three most recent office visits. RESULTS: Of 364 physicians attempting to register, 102 were randomised to the intervention and 103 to the control arms; 95 physicians (intervention, n=48; control, n=47) provided data on their 1182 of their patients at baseline (intervention, n=715; control, n=467) and 945 patients at follow-up (intervention, n=479; control, n=466). For A1c control, medication intensification increased in both groups (intervention, pre 26.4% vs post 32.6%, p=0.022; control, pre 24.8% vs post 31.1%, p=0.033) (intervention, adjusted OR (AOR) 1.37; 95% CI 1.06 to 1.76; control, AOR 1.41 (95% CI 1.06 to 1.89)); however, we observed no incremental benefit solely due to the intervention (group-by-time interaction, p=0.948). Among patients with the worst glucose control (A1c \u3e9%), intensification increased in both groups (intervention, pre 34.8% vs post 62.5%, p=0.002; control, pre 35.7% vs post 61.4%, p=0.008). CONCLUSIONS: A wide-reach, low-intensity, web-based interactive multicomponent intervention had no significant incremental effect on medication intensification for control of glucose, blood pressure or lipids for patients with diabetes of physicians practising in the rural Southeastern USA. TRIAL REGISTRATION: NCT00403091

Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020. The first case of COVID-19 was reported in December 2019 in Wuhan, China. Since then, there have been more than 21 million incidences and 761 thousand casualties worldwide as of 16 August 2020. One of the epidemiological characteristics of COVID-19 is that its symptoms and fatality rates vary with the ages of the infected individuals. This study aims at assessing the impact of social distancing on the reduction of COVID-19 infected cases by constructing a mathematical model and using epidemiological data of incidences in Korea. We developed an age-structured mathematical model for describing the age-dependent dynamics of the spread of COVID-19 in Korea. We estimated the model parameters and computed the reproduction number using the actual epidemiological data reported from 1 February to 15 June 2020. We then divided the data into seven distinct periods depending on the intensity of social distancing implemented by the Korean government. By using a contact matrix to describe the contact patterns between ages, we investigated the potential effect of social distancing under various scenarios. We discovered that when the intensity of social distancing is reduced, the number of COVID-19 cases increases; the number of incidences among the age groups of people 60 and above increases significantly more than that of the age groups below the age of 60. This significant increase among the elderly groups poses a severe threat to public health because the incidence of severe cases and fatality rates of the elderly group are much higher than those of the younger groups. Therefore, it is necessary to maintain strict social distancing rules to reduce infected cases

Racial Differences in Abnormal Ambulatory Blood Pressure Monitoring Measures: Results From the Coronary Artery Risk Development in Young Adults (CARDIA) Study

Background: Several ambulatory blood pressure monitoring (ABPM) measures have been associated with increased cardiovascular disease risk independent of clinic blood pressure (BP). African Americans have higher clinic BP compared with Whites but few data are available on racial differences in ABPM measures. Methods: We compared ABPM measures between African American (n = 178) and White (n = 103) participants at the Year 5 Coronary Artery Risk Development in Young Adults study visit. BP was measured during a study visit and the second and third measurements were averaged. ABPM was conducted over the following 24 hours. Results: Mean ± SD age of participants was 29.8±3.8 years and 30.8±3.5 years for African Americans and Whites, respectively. Mean daytime systolic BP (SBP) was 3.90 (SD 1.18) mm Hg higher among African Americans compared with Whites (P < 0.001) after age–gender adjustment and 1.71 (SD 1.03) mm Hg higher after multivariable adjustment including mean clinic SBP (P = 0.10). After multivariable adjustment including mean clinic SBP, nighttime SBP was 4.83 (SD 1.11) mm Hg higher among African Americans compared with Whites (P < 0.001). After multivariable adjustment, the African Americans were more likely than Whites to have nocturnal hypertension (prevalence ratio: 2.44, 95% CI: 0.99–6.05) and nondipping (prevalence ratio: 2.50, 95% CI: 1.39–4.48). The prevalence of masked hypertension among African Americans and Whites was 4.4% and 2.1%, respectively, (P = 0.49) and white coat hypertension was 3.3% and 3.9%, respectively (P = 0.99). Twenty-four hour BP variability on ABPM was higher among African Americans compared with Whites. Conclusions: These data suggest racial differences in several ABPM measures exist

Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age. Thus, when the vaccination supply is limited, age-dependent vaccination priority strategy should be implemented to minimize the incidences and mortalities. In this study, we developed an age-structured model for describing the transmission dynamics of COVID-19, including vaccination. Using the model and actual epidemiological data in Korea, we estimated the infection probability for each age group under different levels of social distancing implemented in Korea and investigated the effective age-dependent vaccination strategies to reduce the confirmed cases and fatalities of COVID-19. We found that, in a lower level of social distancing, vaccination priority for the age groups with the highest transmission rates will reduce the incidence mostly, but, in higher levels of social distancing, prioritizing vaccination for the elderly age group reduces the infection incidences more effectively. To reduce mortalities, vaccination priority for the elderly age group is the best strategy in all scenarios of levels of social distancing. Furthermore, we investigated the effect of vaccine supply and efficacy on the reduction in incidence and mortality