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Background: Diabetes, among multiple systemic harmful health issues, also may deteriorate normal regenerative and reparative functions of periodontium. The aim of this research was to study the role of periodontal ligament in tissue remodeling under the orthodontic appliance stimulation in two rat experimental models (healthy Wistar rats and Goto-Kakizaki, rodent model of non-obese type 2 diabetes). Materials and methods: It was defined four groups of rats: Wistar (n = 8) and Goto-Kakizaki (n = 8) control groups without orthodontic appliances, and Wistar (n = 16) and Goto-Kakizaki (n = 16) appliance groups with orthodontic appliances. After 42 days, rats were sacrificed and histopathology descriptive analysis about periodontal ligament and adjacent structures was performed as well as cellularity of periodontal ligament and Kappa curvature of tooth roots were measured. Results: Goto-Kakizaki control rats showed statistically significantly higher cellularity in comparison with Wistar control rats (p

In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understandings need further studies, among which the compounding error in long-horizon decisions is a major issue. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning (BC) and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding error compared to BC. Furthermore, we establish the lower bounds of IL under two settings, suggesting the significance of environment interactions in IL. By considering the environment transition model as a dual agent, IL can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than BC. Particularly, we obtain a policy evaluation error that is linear with the effective planning horizon w.r.t. the model bias, suggesting a novel application of adversarial imitation for model-based reinforcement learning (MBRL). We hope these results could inspire future advances in IL and MBRL.

For multimodal representation learning, traditional black-box approaches often fall short of extracting interpretable multilayer hidden structures, which contribute to visualize the connections between different modalities at multiple semantic levels. To extract interpretable multimodal latent representations and visualize the hierarchial semantic relationships between different modalities, based on deep topic models, we develop a novel multimodal Poisson gamma belief network (mPGBN) that tightly couples the observations of different modalities via imposing sparse connections between their modality-specific hidden layers. To alleviate the time-consuming Gibbs sampler adopted by traditional topic models in the testing stage, we construct a Weibull-based variational inference network (encoder) to directly map the observations to their latent representations, and further combine it with the mPGBN (decoder), resulting in a novel multimodal Weibull variational autoencoder (MWVAE), which is fast in out-of-sample prediction and can handle large-scale multimodal datasets. Qualitative evaluations on bimodal data consisting of image-text pairs show that the developed MWVAE can successfully extract expressive multimodal latent representations for downstream tasks like missing modality imputation and multimodal retrieval. Further extensive quantitative results demonstrate that both MWVAE and its supervised extension sMWVAE achieve state-of-the-art performance on various multimodal benchmarks.

Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its transparency and simplicity while being competitive to state-of-the-art classifiers. Despite their relative empirical success in terms of prediction rates, there are limited studies on FRCNs' dynamic properties and how their building blocks contribute to the algorithm's performance. In this article, we theoretically study these issues and conclude that boundary and negative neurons always converge to a unique fixed-point attractor. Moreover, we demonstrate that negative neurons have no impact on the algorithm's performance and that the ranking of positive neurons is invariant. Moved by our theoretical findings, we propose two simpler fuzzy-rough classifiers that overcome the detected issues and maintain the competitive prediction rates of this classifier. Toward the end, we present a case study concerned with image classification, in which a convolutional neural network is coupled with one of the simpler models derived from the theoretical analysis of the FRCN model. The numerical simulations suggest that once the features have been extracted, our granular neural system performs as well as other RNNs.

Flavonoids are a class of substances of a vegetal origin with many interesting actions from the point of view of human disease. Interest in flavonoids in the diet has increased in recent years due to the publication of basic, clinical and epidemiological studies that have shown a whole array of salutary effects related to intake of flavonols and flavones as well as a lower morbility and mortality of cardiovascular diseases. Since arterial hypertension is the most common modifiable risk factor for cardiovascular diseases, this review will focus mainly on the effects of flavonoids on the cardiovascular system with relation to the elevation of blood pressure. Its antihypertensive effects as well as the many investigations performed in experimental models of arterial hypertension, are reviewed in this mini-review.

Purpose/Objective(s) To develop a comprehensive framework to simulate the response to immune checkpoint inhibitors (ICIs) in combination with radiotherapy (RT) and to apply the framework for investigating ICI-RT combination regimen in hepatocellular carcinoma (HCC) patients. Materials/Methods The mechanistic mathematical model is based on dynamic biological interactions between the immune system and the tumor using input data from patient blood samples and outcomes of clinical trials. The cell compartments are described by ordinary differential equations and represent irradiated and non-irradiated tumor cells and lymphocytes. The effect of ICI is modeled using an immune activation term that is based on tumor size changes observed in a phase 1/2 clinical trial for HCC. Simulated combination regimen are based on ongoing ICI-RT trials. Results The proposed framework successfully describes tumor volume trajectories observed in early-stage clinical trials of durvalumab monotherapy in HCC patients. For ICI-RT treatment regimen the irradiated tumor fraction is the most important parameter for the efficacy. For 90% of the tumor cells being irradiated, adding RT to ICI yields an increase in clinical benefit from 33% to 71% in non-irradiated tumor sites. The model agrees with clinical data showing an association of outcome with initial tumor volume and lymphocyte counts. We demonstrate model application in clinical trial design to predict progression-free survival curves, showing that the cohort size to show significant improvement heavily depends on the irradiated tumor fraction. Conclusion We present a framework extending radiation cell kill models to include circulating lymphocytes and the effect of ICIs and enable simulation of combination strategies. The simulations predict that a significant amount of the benefit from RT in combination with ICI stems from the reduction in irradiated tumor burden and associated immune suppression. This aspect needs to be included in the interpretation of outcomes and the design of novel combination trials.

Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regional, hierarchical‐tier mathematical model for better understanding the complexity and heterogeneity of COVID‐19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi‐regional, hierarchical‐tier SLIHR model. With this model, we revealed insight into how COVID‐19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi‐regional, hierarchical‐tier SLIHR model revealed insight into how COVID‐19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID‐19 pandemic.

Background: A measles outbreak involving 60 cases occurred in Yamagata, Japan in 2017. Using two different mathematical models for different datasets, we aimed to estimate measles transmissibility over time and explore any heterogeneous transmission patterns. Methods: The first model relied on the temporal distribution for date of illness onset for cases, and a generation-dependent model was applied to the data. Another model focused on the transmission network. Using the illness-onset date along with the serial interval and geographical location of exposure, we reconstructed a transmission network with 19 unknown links. We then compared the number of secondary transmissions with and without clinical symptoms or laboratory findings. Results: Using a generation-dependent model (assuming three generations other than the index case), the reproduction number (R) over generations 0, 1, and 2 were 25.3, 1.3, and

Background: During the COVID-19 pandemic, the disruption in care for sexually transmitted infections (STIs) and the social distancing measures have led to reductions in STI testing and sexual behavior. We assessed the impact of these COVID-19-related changes on transmission of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) among men who have sex with men (MSM) in The Netherlands. Methods: We developed a mathematical model for CT and NG transmission among MSM, accounting for COVID-19-related changes in sexual behavior and testing in 2020 to 2021. Changes in 2020 were estimated from data from the Dutch COVID-19, Sex, and Intimacy Survey among MSM and the National Database of STI Clinics. Because of the lack of data for 2021, we examined several scenarios covering a range of changes. Results: A reduction of 10% and 40% in STI testing of symptomatic and asymptomatic, respectively, individuals with a 10% to 20% reduction in numbers of casual partners (according to partner status and activity level) during the second lockdown, resulted in a 2.4% increase in CT prevalence, but a 2.8% decline in NG prevalence in 2021. A 5% and 30% reduction in STI testing of symptomatic and asymptomatic, respectively, individuals with the same reduction in casual partners resulted in a 0.6% increase in CT prevalence and a 4.9% decrease in NG prevalence in 2021. Conclusions: The disruption in STI care due to COVID-19 might have resulted in a small increase in CT prevalence, but a decrease in NG prevalence. Scaling up STI care is imperative to prevent increases in STI transmission.

In December 2019, a novel coronavirus crossed species barriers to infect humans and was effectively transmitted from person to person, leading including vaccines and antiviral drugs that could prevent or limit the burden or transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health priority. It is thus of utmost importance to assess possible therapeutic strategies against SARS-CoV-2 using experimental models that recapitulate aspects of the human disease. Here, we review available models currently being developed and used to study SARS-CoV-2 infection and highlight their application to screen potential therapeutic approaches, including repurposed antiviral drugs and vaccines. Each identified model provides a valuable insight into SARS-CoV-2 cellular tropism, replication kinetics, and cell damage that could ultimately enhance understanding of SARS-CoV-2 pathogenesis and protective immunity. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Background and Purpose: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are Methods: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011–2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. Results: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. Conclusions: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

Part of this study was presented at the 27th European Congress of Clinical Microbiology and Infectious Diseases, Vienna, Austria 2017., [Introduction] Immune response stimulation may be an adjuvant to antimicrobial treatment. Here, we evaluated the impact of immune response modification by lysophosphatidylcholine (LPC), combined with imipenem or ceftazidime, in murine models of peritoneal sepsis (PS) and pneumonia induced by Pseudomonas aeruginosa., [Methods] The imipenem and ceftazidime-susceptible strain (Pa39) and imipenem and ceftazidime-resistant strain (Pa238) were used. Ceftazidime pharmacokinetic and pharmacodynamic parameters were determined. The therapeutic efficacy and TNF-α and IL-10 levels were determined in murine models of PS and pneumonia induced by Pa39 and Pa238 and treated with LPC, imipenem or ceftazidime, alone or in combination., [Results] In the PS model, LPC+ceftazidime reduced spleen and lung Pa238 concentrations (−3.45 and −3.56 log10 CFU/g; P < 0.05) to a greater extent than ceftazidime monotherapy, while LPC + imipenem maintained the imipenem efficacy (−1.66 and −1.45 log10 CFU/g; P > 0.05). In the pneumonia model, LPC + ceftazidime or LPC + imipenem reduced the lung Pa238 concentrations (−2.37 log10 CFU/g, P = 0.1, or −1.35 log10 CFU/g, P = 0.75). For Pa39, no statistically significant difference was observed in the PS and pneumonia models between combined therapy and monotherapy. Moreover, LPC + imipenem and LPC+ceftazidime significantly decreased and increased the TNF-α and IL-10 levels, respectively, in comparison with the untreated controls and monotherapies., [Conclusions] These results demonstrate the impact of immune response modification by LPC plus antibiotics on the prognosis of infections induced by ceftazidime-resistant P. aeruginosa., [Introducción] La estimulación de la respuesta inmunitaria podría ser adyuvante al tratamiento antimicrobiano. En este estudio, hemos evaluado el impacto de la modificación de la respuesta inmunitaria por la lisofosfatidilcolina (LPC), combinada con imipenem ó ceftazidima, en modelos murinos de sepsis peritoneal (SP) y de neumonía por Pseudomonas aeruginosa (P. aeruginosa)., [Métodos] La cepa sensible a imipenem y ceftazidima (Pa39) y la cepa resistente a ambos antibióticos (Pa238) fueron usadas. Los parámetros farmacocinéticos/farmacodinámicos de ceftazidima fueron determinados. La eficacia terapéutica y los niveles de TNF-α and IL-10 fueron determinados en los modelos murinos de SP y de neumonía por Pa39 y Pa238 y tratados con LPC, imipenem o ceftazidima, en monoterapia ó en combinación., [Resultados] En el modelo de SP, LPC + ceftazidima redujo la concentración de Pa238 en el bazo y el pulmón (–3,45 y –3,56 log10 UFC/g; p < 0,05) en comparación con ceftazidima, mientras LPC + impenem mantuvo la eficacia de imipenem (–1,66 y –1,45 log10 UFC/g; p > 0,05). En el modelo de neumonía, LPC + ceftazidima o LPC + imipenem redujo la concentración de Pa238 en pulmón (–2,37 log10 UFC/g, p = 0,1 o –1,35 log10 UFC/g, p = 0,75). Para Pa39, no se observó diferencia estadística significativa entre la terapia combinada y la monoterapia en los modelos de SP y de neumonía. Además, LPC + imipenem y LPC + ceftazidime redujeron y aumentaron los niveles de TNF-α y IL-10, respectivamente, en comparación con los controles no tratados y las monoterapias., [Conclusiones] Estos resultados demuestran el impacto de la modificación de la respuesta inmunitaria por LPC en combinación con antibióticos en el pronóstico de las infecciones por P. aeruginosa ceftazidima-resistente., This study was supported by the Instituto de Salud Carlos III, Proyectos de Investigación en Salud (grant PI13/01744, PI16/01306), and by Consejería de Innovación, Ciencia y Empresa (P11-CTS-6317). Younes Smani is supported by the Subprograma Miguel Servet Tipo I from the Ministerio de Economía y Competitividad of Spain (CP15/01358). The MEDINA authors disclosed the receipt of financial support from Fundación MEDINA, a public-private partnership of Merck Sharp & Dohme de España S.A./Universidad de Granada/Junta de Andalucía.

In emergency medical services (EMS), response time is a key factor that shows the performance of the system. This factor is composed of two parts including activation time and travel time. The activation time of an emergency call is the time a call center spends on taking an incoming call for EMS and decides whether an ambulance should be dispatched or the emergency can be resolved by giving some medical advices. Having a good number of call center operators including call takers and dispatchers can help to reduce this time and thus the response time. However, this alone may not be enough to have a reasonable cycle time. In fact, having idle ambulances located in right places when emergency calls occur is an influential factor to reduce the travel time and thus have an optimal response time and save people's lives. Although many researchers have studied improving either the activation time or the travel time, to the best of our knowledge, there exists no study focusing on both. Motivated by this gap, in this study, a framework is proposed which uses queuing theory and location analysis to address both activation and travel times. In this regard, first, queuing theory is applied to evaluate the performance of an EMS call center and reduce the average activation time. Then, three mathematical models including mobile stations, MEXCLP and MECRP are applied to locate ambulances such that the average travel time is minimized. To shed light on the merits of using queuing theory and location models, computational results based on the real-world data from Tehran EMS Center are provided. The proposed methodology can reduce the average response time by approximately four minutes and increase the average coverage by 35%. This work is an example of the tremendous positive impact that the application of queuing theory and mathematical modeling can have. The proposed framework can significantly decrease the cycle time and increase the ambulance coverage level. The findings of this study help the policy makers of EMS centers with evaluating the performance of their systems and determining the optimal number of operators and locations of ambulances in a way that enhances the quality of emergency services.

Graphical abstract, Highlights • Established a new experimental rat model of the developmental trochlear dysplasia; • Using the macroscopic morphological and micro-CT to assess trochlear dysplasia; • Using Histological staining to investigate the cartilage degradation of the model; • Investigated the relationship of the PI3K/AKT signaling pathway with trochlear dysplasia cartilage degeneration; • Using immunohistochemistry and qPCR to investigate the PI3K/AKT and the marker of the cartilage degeneration., Introduction Trochlear dysplasia is a commonly encountered lower extremity deformity in humans. However, the molecular mechanism of cartilage degeneration in trochlear dysplasia is unclear thus far. Objectives The PI3K/AKT signaling pathway is known to be important for regulating the pathophysiology of cartilage degeneration. The aim of this study was to investigate the relationship of the PI3K/AKT signaling pathway with trochlear dysplasia cartilage degeneration. Methods In total, 120 female Sprague-Dawley rats (4 weeks of age) were randomly separated into control and experimental groups. Distal femurs were isolated from the experimental group at 4, 8, and 12 weeks after surgery; they were isolated from the control group at the same time points. Micro-computed tomography and histological examination were performed to investigate trochlear anatomy and changes in trochlear cartilage. Subsequently, expression patterns of PI3K/AKT, TGFβ1, and ADAMTS-4 in cartilage were investigated by immunohistochemistry and quantitative polymerase chain reaction. Results In the experimental group, the trochlear dysplasia model was successfully established at 8 weeks after surgery. Moreover, cartilage degeneration was observed beginning at 8 weeks after surgery, with higher protein and mRNA expression levels of PI3K/AKT, TGFβ1, and ADAMTS-4, relative to the control group. Conclusion Patellar instability might lead to trochlear dysplasia in growing rats. Moreover, trochlear dysplasia may cause patellofemoral osteoarthritis; cartilage degeneration in trochlear dysplasia might be associated with activation of the PI3K/AKT signaling pathway. These results provide insights regarding the high incidence of osteoarthritis in patients with trochlear dysplasia. However, more research is needed to clarify the underlying mechanisms.

Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the "combiome," combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome.

Background We created an estimation model for hypothermic circulatory arrest time and analyzed the risk factors for major adverse outcomes in total arch replacement. Methods This study involved 272 patients who underwent total arch replacement. The estimation model for hypothermic circulatory arrest time was established using multiple linear regression analysis, and the predicted hypothermic circulatory arrest time from this model was analyzed to detect risk factors. Results Atrial fibrillation, rupture, malperfusion, saccular aneurysm, cardiopulmonary bypass time, and hypothermic circulatory arrest time were identified as independent risk factors associated with major adverse outcomes. The estimation model for hypothermic circulatory arrest time was established as follows: hypothermic circulatory arrest time = 99.3 – 0.19 × age + 0.65 × body mass index + 6.19 × previous cardiac operation + 11.7 × acute dissection + 8.9 × rupture + 0.19 × aortic angulation + 0.15 × length to the distal anastomosis site – 6.17 × total arch replacement surgeon case volume – 3.06 × surgery year. The predicted hypothermic circulatory arrest time calculated by this estimation model was evaluated using multivariate logistic analysis, which identified atrial fibrillation, rupture, malperfusion, saccular aneurysm, and predicted hypothermic circulatory arrest time as risk factors. Conclusions As with the actual hypothermic circulatory arrest time, the predicted hypothermic circulatory arrest time using our model detected significant factors associated with major adverse outcomes. These results indicated that this prediction model for hypothermic circulatory arrest time may be effective.

11 Pág., Climate change and land-use changes are the main drivers altering fire regimes and leading to the occurrence of megafires. Current management policies mainly focus on short-term restoration without considering how climate change might affect regeneration dynamics. We aimed to test the usefulness of ecological niche models (ENMs) to integrate the effects of climate change on tree species distributions into post-fire restoration planning. We also examined different important conceptual and methodological aspects during this novel process. We constructed ENM at fine spatial resolution (25 m) for the four main tree species (Pinus pinaster, Quercus pyrenaica, Q. faginea and Q. ilex) in an area affected by a megafire in Central Spain at two scales (local and regional), two periods (2 and 14 years after the fire) at the local scale, and under two future climate change scenarios. The usefulness of ENMs as support tools in decision-making for post-fire management was confirmed for the first time. As hypothesized, models developed at both scales are different, since they represent different scale dependent drivers of species distribution patterns. However, both provide objective information to be considered by stakeholders in combination with other sources of information. Local models generated with vegetation data 14 years after the fire provided valuable information about local and current vegetation dynamics (i.e., current microecology spatial niche prediction). Regional models are capable of considering a higher proportion of the climatic niche of species to generate reliable climate change forecasts (i.e., future macroclimate spatial niche forecast). The use of precise ENMs provide both an objective interpretation of potential habitat conditions and the opportunity of examining vegetation patches, that can be very valuable in managing restoration of areas affected by megafires under climate change conditions., This publication is part of the R&D&I projects PID2020-116494RR-C41 funded by MCIN/AEI/10.13039/501100011033/, VIS4FIRE (RTA2017-00042-C05-01), FORESTCHANGE (AGL2016-76769-C2-1-R) and CONGESTION (PID2020-119204RB-C21). Cristina Carrillo was financed by the Ministry of Science and Innovation, the European Social Fund and the State Research Agency through the pre-doctoral aid programme (PRE2018).

BackgroundApproaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes.MethodsThis was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes.FindingsThe analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, pConclusionsA 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions.

Background aims Extracellular vesicles (EVs) are being tested for their use as novel therapeutics. However, the optimal source of EVs is currently under investigation. Amniotic fluid (AF) is a natural source of EVs that can be easily obtained for use in regenerative medicine, yet AF-EV characterization has not been fully explored. Methods Here the authors demonstrate AF as a rich source of EVs and identify the microRNA and proteomic cargo. Bioinformatics analysis of this cargo revealed multiple pathway targets, including immunomodulatory, anti-inflammatory and free radical scavenging networks. The authors further demonstrated the therapeutic potential of this EV product as a novel preventative agent for bronchopulmonary dysplasia (BPD). Results Intra-tracheal administration of AF-EVs preserved alveolar development, attenuated vascular remodeling and pulmonary hypertension, decreased lung pro-inflammatory cytokine expression and reduced macrophage infiltration in an experimental BPD model. Conclusions The authors’ results suggest that AF is a viable biological fluid for EV harvest and that AF-EVs have strong therapeutic potential for pulmonary diseases, such as BPD, warranting further development to transition this novel EV product into the clinic.

Internal quality control (IQC) is traditionally interpreted against predefined control limits using multi-rules or 'Westgard rules'. These include the commonly used 1:3s and 2:2s rules. Either individually or in combination, these rules have limited sensitivity for detection of systematic errors. In this proof-of-concept study, we directly compare the performance of three moving average algorithms with Westgard rules for detection of systematic error.In this simulation study, 'error-free' IQC data (control case) was generated. Westgard rules (1:3s and 2:2s) and three moving average algorithms (simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA); all using ±3SD as control limits) were applied to examine the false positive rates. Following this, systematic errors were introduced to the baseline IQC data to evaluate the probability of error detection and average number of episodes for error detection (ANEed).From the power function graphs, in comparison to Westgard rules, all three moving average algorithms showed better probability of error detection. Additionally, they also had lower ANEed compared to Westgard rules. False positive rates were comparable between the moving average algorithms and Westgard rules (all0.5%). The performance of the SMA algorithm was comparable to the weighted algorithms forms (i.e. WMA and EWMA).Application of an SMA algorithm on IQC data improves systematic error detection compared to Westgard rules. Application of SMA algorithms can simplify laboratories IQC strategy.

ObjectiveTo determine predictors of change in neuropsychiatric (NP) event status in a large, prospective, international inception cohort of patients with systemic lupus erythematosus (SLE).MethodsUpon enrollment and annually thereafter, NP events attributed to SLE and non-SLE causes and physician-determined resolution were documented. Factors potentially associated with the onset and resolution of NP events were determined by time-to-event analysis using a multistate modeling structure.ResultsNP events occurred in 955 (52.3%) of 1,827 patients, and 593 (31.0%) of 1,910 unique events were attributed to SLE. For SLE-associated NP (SLE NP) events, multivariate analysis revealed a positive association with male sex (P=0.028), concurrent non-SLE NP events excluding headache (P < 0.001), active SLE (P = 0.012), and glucocorticoid use (P = 0.008). There was a negative association with Asian race (P = 0.002), postsecondary education (P = 0.001), and treatment with immunosuppressive drugs (P = 0.019) or antimalarial drugs (P = 0.056). For non-SLE NP events excluding headache, there was a positive association with concurrent SLE NP events (P < 0.001) and a negative association with African race (P = 0.012) and Asian race (P < 0.001). NP events attributed to SLE had a higher resolution rate than non-SLE NP events, with the exception of headache, which had comparable resolution rates. For SLE NP events, multivariate analysis revealed that resolution was more common in patients of Asian race (P = 0.006) and for central/focal NP events (P < 0.001). For non-SLE NP events, resolution was more common in patients of African race (P = 0.017) and less common in patients who were older at SLE diagnosis (P < 0.001).ConclusionIn a large and long-term study of the occurrence and resolution of NP events in SLE, we identified subgroups with better and worse prognosis. The course of NP events differs greatly depending on their nature and attribution.

The purpose of this narrative review was to discuss the scientific milestones that led to the current understanding of the root dentinal microcrack phenomenon based on the interplay between the usage of micro-computed tomography (micro-CT) as an analytical tool alongside a close-to-mouth experimental model. In 2009, reports on the development of dentinal microcracks in extracted teeth after root canal preparation triggered an awareness of the potential for vertical root fractures (VRFs) of endodontically treated teeth could be developed from defects created by the mechanical stress of nickel-titanium preparation systems on dentine. This assumption was taken for granted, even though no cause-effect relationship had been scientifically demonstrated. Since then, several studies using the sectioning method with extracted teeth have been published and the large discrepancy amongst their outcomes soon become evident. Moreover, the high frequency of reported dentinal microcracks largely contrasted with the clinical incidence of VRFs, raising doubts on their methodological reliability. Using micro-CT technology, it was demonstrated by several studies that, in extracted teeth, dentinal defects already existed before the endodontic procedures, indicating that the initial reports framed a non-existing cause-effect relationship between canal preparation and dentinal microcracks. Although these new findings contributed to a better comprehension of this phenomenon, the misconception that microcracks were the starting point for VRFs was only surpassed with a new in situ approach using fresh cadavers. Surprisingly, microcracks were not identified in sound teeth. As a conclusion, dentinal microcracks in extracted teeth can be considered a non-natural occurrence observed only in a laboratory set-up as a consequence of dehydration and storage conditions. Thus, dentinal microcracks shall not be considered as the starting point for VRFs as they do not manifest in non-extracted teeth. Identifying dentinal microcracks as a laboratory phenomenon highlights the impact of recent scientific developments to disclaim the clinical relevance of laboratory-obtained outcomes.

Introduction: Excessive ultrafiltration is associated with intra-dialytic symptoms, loss of residual function, and mortality in haemodialysis patients. A major contributor to excessive ultrafiltration is within-individual variation in pre-dialysis weight and the concept of achieving a fixed target weight by the end of each dialysis session. Haemodialysis protocols which allow variable post-dialysis weight have not been proposed. Methods: Weight variation was observed in haemodialysis patients and healthy controls to estimate the proportion of pre-dialysis weight variation which could be considered natural variation. These estimates were used to derive a novel protocol for setting ultrafiltration, which was evaluated by mathematical modelling. Results: Amongst 20 haemodialysis patients, mean (SD) pre-dialysis weight was 102.74 (0.94)% of target weight after a 2-day gap and 103.50 (0.94)% after a 3-day gap. Amongst 10 healthy individuals, mean (SD) daily weight was 100.0 (0.71)% of average weight. A 4-component model of pre-dialysis weight was derived using these estimates, in which the best estimate of pre-dialysis excess fluid is the midpoint of excess weight and average fluid gain, and used to propose a novel protocol for ultrafiltration setting. In simulations, the novel protocol reduced ultrafiltration variation by more than half (standard deviation 0.6 vs. 1.3% of target weight, p < 0.001), without increasing the variation in post-dialysis fluid excess. Excessive ultrafiltration rates (over 13 mL/h/kg) were far less frequent using this protocol (2.6% vs. 7.5% of sessions, p = 0.001). Conclusion: Considering natural weight variation allows the development of a novel protocol for ultrafiltration in which target weight does not have to be achieved precisely: it is therefore a soft target. This protocol, which is predicted to substantially reduce excessive ultrafiltration variation, is a zero-cost intervention with the potential to improve symptoms and clinical outcome for haemodialysis patients.

Endodontic microbiology deals with the study of the microbial aetiology and pathogenesis of pulpal and periradicular inflammatory diseases. Research in endodontic microbiology started almost 130 years ago and since then has mostly focussed on establishing and confirming the infectious aetiology of apical periodontitis, identifying the microbial species associated with the different types of endodontic infections and determining the efficacy of treatment procedures in eradicating or controlling infection. Diverse analytical methods have been used over the years, each one with their own advantages and limitations. In this review, the main features and applications of the most used technologies are discussed, and advice is provided to improve study designs in order to properly address the scientific questions and avoid setbacks that can compromise the results. Finally, areas of future research are described.

Participatory modelling seeks to foster stakeholder engagement to better attune models to their decision-making and policy contexts. Such approaches are increasingly advocated for use in the field of health. We review the instrumental and epistemological claims made in support of participatory modelling approaches. These accentuate participatory models as offering a better evidence-base for health policy decisions. By drawing attention to recent modelling experiments in a sector outside of health, that of water management, we outline a different way of thinking about participation and modelling. Here, the participatory model is configured in relation to matters of 'knowledge controversy', with modelling constituted as an 'evidence-making intervention' in relation to the making of science and expertise. Rather than presenting participatory models as an improved technical solution to addressing given policy problems within an evidence-based intervention approach, models are alternatively potentiated as sites for the redistribution of expertise among actor networks as they seek to engage politically in a matter of concern. This leads us to consider possible new directions for participatory modelling in the field of health.

Background Kinetics of the uptake of inhaled anesthetics have been well studied, but the kinetics of elimination might be of more practical importance. The objective of the authors’ study was to assess the effect of the overall ventilation/perfusion ratio (VA/Q), for normal lungs, on elimination kinetics of desflurane and sevoflurane. Methods The authors developed a mathematical model of inhaled anesthetic elimination that explicitly relates the terminal washout time constant to the global lung VA/Q ratio. Assumptions and results of the model were tested with experimental data from a recent study, where desflurane and sevoflurane elimination were observed for three different VA/Q conditions: normal, low, and high. Results The mathematical model predicts that the global VA/Q ratio, for normal lungs, modifies the time constant for tissue anesthetic washout throughout the entire elimination. For all three VA/Q conditions, the ratio of arterial to mixed venous anesthetic partial pressure Part/Pmv reached a constant value after 5 min of elimination, as predicted by the retention equation. The time constant corrected for incomplete lung clearance was a better predictor of late-stage kinetics than the intrinsic tissue time constant. Conclusions In addition to the well-known role of the lungs in the early phases of inhaled anesthetic washout, the lungs play a long-overlooked role in modulating the kinetics of tissue washout during the later stages of inhaled anesthetic elimination. The VA/Q ratio influences the kinetics of desflurane and sevoflurane elimination throughout the entire elimination, with more pronounced slowing of tissue washout at lower VA/Q ratios. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

The CPqPy framework coupling COMSOL and PHREEQC based on Python was developed. This framework can achieve the simulation of diversified situations including multi-physics coupling and geochemical reactions of soil and groundwater. The multi-physics coupling models are calculated in COMSOL, whereas PHREEQC was applied to calculate the geochemical models through the Phreeqpy library in Python. Feasibility and accuracy of CPqPy were verified and applied to two cases, including a solute transport model considering equilibrium reaction and ion exchange as well as a reactive transport model of a variable saturation soil considering kinetic reaction. The results show a high degree of credibility of CPqPy. The framework has the advantages of strong portability, and it can be further used in conjunction with multiple Python calculation libraries, which greatly extends the application of the reactive transport model.

Despite enjoying high favourable outcome rates, root canal treatment is not always successful. Root canal retreatment is a widely practised option for managing a non-healing root treated tooth. A basic distinction between retreatment and initial treatment is the requirement to remove the existing root filling material occupying the root canal space. Only then, can the technical deficiencies and possible causes of failure be addressed. Hence, the successful removal of the root filling material is critical to achieving the objectives of retreatment. Many different materials and techniques have been used for root canal filling, which have been documented in a plethora of studies; these are mainly laboratory studies investigating the removal of root filling materials. To help guide future research, which may then better inform clinical practice in relation to retreatment procedures, the focus of this narrative review is on the research methods and experimental models employed to study the removal of root filling materials. The current available literature demonstrates great variation in the research methodologies used to study the removal of root filling material, including direct visualization by splitting tooth samples or clearing, three-dimensional radiographic imaging including using microcomputed tomography or cone-beam computed tomography, and a combination of these techniques. Depending on the research method employed, variation also exists in quantification of root filling material removal; these include measurements of surface area, volume or weight, and criteria-based (semi-quantitative) assessment. Experimental models, using human teeth or resin models, differ with respect to standardization techniques and replication of the clinical scenario including initial sample preparation, canal filling, allocation and technical aspects of retreatment procedures. Future models should replicate, as closely as possible, the clinical scenario. Currently, microcomputed tomography provides a highly detailed, non-invasive and non-destructive method of objectively assessing and quantifying root filling removal.

Background. Aging is a process that biological changes accumulate with time and lead to increasing susceptibility to diseases like cancer. This study is aimed at establishing an aging-related prognostic signature in colon adenocarcinoma (COAD). Methods. The transcriptome data and clinical variables of COAD patients were downloaded from TCGA database. The genes in GOBP_AGING gene set was used for prognostic evaluation by the univariate and multivariate Cox regression analyses. The model was presented by a nomogram and assessed by the Kaplan-Meier curves and calibration curves. The drug response and gene mutation were also performed to implicate the clinical significance. The GO and KEGG analyses were employed to unravel the potential functional mechanism. Results. The Gene Set Enrichment Analysis result indicates that GOBP_AGING pathway is significantly enriched in COAD samples. Four aging-related genes are finally used to construct the aging-related prognostic signature: FOXM1, PTH1R, KL, and CGAS. The COAD patients with high risk score have much shorter overall survival in both train cohort and test cohort. The nomogram is then assembled to predict 1-year, 3-year, and 5-year survival. Patients with high risk score have elevated infiltrating B cell naïve and attenuated cisplatin sensitivity. The mutation landscape shows that the TTN, FAT4, ZFHX4, APC, and OBSCN gene mutation are different between high risk score patients and low risk score patients. The differentially expressed genes between patients with high score and low score are enriched in B cell receptor signaling pathway. Conclusion. We constructed an aging-related signature in COAD patients, which can predict oncological outcome and optimize therapeutic strategy.

Background Gram-negative pathogens, such as Escherichia coli, are common causes of bloodstream infections (BSIs) and increasingly demonstrate antimicrobial resistance. Molecular rapid diagnostic tests (mRDTs) offer faster pathogen identification and susceptibility results, but higher costs compared with conventional methods. We determined the cost-effectiveness of the BioFire FilmArray Blood Culture Identification (BCID) Panel, as a type of mRDT, compared with conventional methods in the identification of E. coli BSIs. Methods We constructed a decision analytic model comparing BCID with conventional methods in the identification and susceptibility testing of hospitalized patients with E. coli BSIs from the perspective of the public healthcare payer. Model inputs were obtained from published literature. Cost-effectiveness was calculated by determining the per-patient admission cost, the QALYs garnered and the incremental cost-effectiveness ratios (ICERs) where applicable. Monte Carlo probabilistic sensitivity analyses and one-way sensitivity analyses were conducted to assess the robustness of the model. All costs reflect 2019 Canadian dollars. Results The Monte Carlo probabilistic analyses resulted in cost savings ($27 070.83 versus $35 649.81) and improved QALYs (8.65 versus 7.10) in favour of BCID. At a willingness to pay up to $100 000, BCID had a 72.6%–83.8% chance of being cost-effective. One-way sensitivity analyses revealed length of stay and cost per day of hospitalization to have the most substantial impact on costs and QALYs. Conclusions BCID was found to be cost-saving when used to diagnose E. coli BSI compared with conventional testing. Cost savings were most influenced by length of stay and cost per day of hospitalization.

Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologies to contribute to the design of screening programs in this population. We conducted a retrospective cross-sectional screening program of imported diseases in all immigrant patients who attended the Tropical Medicine Unit between January 2009 and December 2016. We designed a mathematical model based on machine learning methodologies to establish the set of most discriminatory prognostic variables to predict the onset of the: HIV infection, malaria, chronic hepatitis B and C, schistosomiasis, and Chagas in immigrant population. We analyzed 759 patients. HIV was predicted with an accuracy of 84.9% and the number of screenings to detect the first HIV-infected person was 26, as in the case of Chagas disease (with a predictive accuracy of 92.9%). For the other diseases the averages were 12 screenings to detect the first case of chronic hepatitis B (85.4%), or schistosomiasis (86.9%), 23 for hepatitis C (85.6%) or malaria (93.3%), and eight for syphilis (79.4%) and strongyloidiasis (88.4%). The use of machine learning methodologies allowed the prediction of the expected disease burden and made it possible to pinpoint with greater precision those immigrants who are likely to benefit from screening programs, thus contributing effectively to their development and design.

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding., Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.

During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important., The state of Victoria, Australia experienced a substantial second wave of COVID-19 but brought it under control with strict non-pharmaceutical interventions. Here, the authors model the second wave in Victoria to estimate the impacts of the different interventions.

Studying biological systems is a difficult but important task. Traditional methods include laboratory experimentation and computer simulations. However, often researchers need to explore important but potentially rare events that are not easily observed or simulated. We use UPPAAL-SMC, a formal verification tool to support a methodology that allows modelling biological systems, specify events and conditions that we want to analyze, and to explore system executions using controlled simulations. We also describe an efficient way to reproduce laboratory experiments in silico. Unlike traditional simulations, we are able to guide the experiment to explore special events and conditions expressing these conditions in temporal logic formulas. We have applied this methodology to create a more detailed model of Palytoxin-induced Na + /K + pump channels than was previously possible. Moreover, we have reproduced experimental protocols and their associated electrophysiological recordings, which has not been done in previous works. As a consequence, we have been able to propose a new diprotomeric model for the PTX-pump complex and study its behaviour. The use of our methodology enabled us to reduce the effort and time to perform this research. It can be used to model other complex biological systems, potentially increasing the productivity of such studies.

Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. Results In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. Conclusion Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

Background An endpoint that has received some attention in recent cardiovascular trials is ‘days alive and out of hospital’ (DAOH). Percent DAOH is a natural extension of DAOH that adjusts for differences in length of follow-up. This endpoint measure incorporates mortality and morbidity together in a way that has the potential to give more insight regarding treatment effects compared to conventional time-to-event endpoints. Other advantages of this measure include the relative ease of collection and interpretation. However, research on how to analyze this measure is still limited. Methods We propose using the one-inflated beta model to analyze percent DAOH. This model is appropriate for highly left-skewed data with a large proportion of boundary values. Data from the Prospective Comparison of ARNI [Angiotensin Receptor-Neprilysin Inhibitor] with ACEI [Angiotensin-Converting-Enzyme Inhibitor] to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial (PARADIGM-HF) and Candesartan in Heart Failure Assessment of Reduction in Mortality and morbidity (CHARM) trials are used to illustrate this method. Results Statistically significant differences in percent DAOH were observed for PARADIGM-HF and CHARM in favor of treatment. In PARADIGM-HF, treatment with sacubitril plus valsartan increased DAOH on average by 11 days (95% CI: 1.4-20.9 days) and increased percent DAOH by 1.64% at a fixed follow-up length of 1,000 days (95% CI: 0.61%- 2.67%). For the CHARM overall program, the candesartan group has 1.79% more DAOH (95% CI: 0.91%- 2.68%). Conclusion DAOH, and especially percent DAOH, can enhance our understanding of treatment effects in future cardiovascular trials, and the one-inflated beta model is an appropriate choice for its analysis.

Environmental sound classification is one of the important issues in the audio recognition field. Compared with structured sounds such as speech and music, the time–frequency structure of environmental sounds is more complicated. In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this paper. Firstly, an experiment that is used as motivation in proposed method is designed to verify the effect of a specific frequency band in the spectrogram on model classification. Secondly, two new attention mechanisms, temporal attention mechanism and frequency attention mechanism, are proposed. These mechanisms can focus on key frequency bands and semantic related time frames on the spectrogram to reduce the influence of background noise and irrelevant frequency bands. Then, a feature information complementarity is formed by combining these mechanisms to more accurately capture the critical time–frequency features. In such a way, the representation ability of the network model can be greatly improved. Finally, experiments on two public data sets, UrbanSound 8 K and ESC-50, demonstrate the effectiveness of the proposed method.

BACKGROUND: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient’s healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health. OBJECTIVE: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients. METHOD: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient’s eye movement. The collected data are used in the cloud database to evaluate the patient’s health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers. RESULTS: The experimental results show that patient health monitoring is a reliable way to improve health effectively.

Saccadic eye movements (saccades) disrupt the continuous flow of visual information, yet our perception of the visual world remains uninterrupted. Here we assess the representation of the visual scene across saccades from single-trial spike trains of extrastriate visual areas, using a combined electrophysiology and statistical modeling approach. Using a model-based decoder we generate a high temporal resolution readout of visual information, and identify the specific changes in neurons’ spatiotemporal sensitivity that underly an integrated perisaccadic representation of visual space. Our results show that by maintaining a memory of the visual scene, extrastriate neurons produce an uninterrupted representation of the visual world. Extrastriate neurons exhibit a late response enhancement close to the time of saccade onset, which preserves the latest pre-saccadic information until the post-saccadic flow of retinal information resumes. These results show how our brain exploits available information to maintain a representation of the scene while visual inputs are disrupted., A late enhancement of the perisaccadic neural response may exist in extrastriate areas. Here the authors show this preserves pre-saccadic information until the post-saccadic information is received, maintaining an integrated representation of the visual scene across saccadic eye movements.

Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results. There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion. The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.

Background: Neural cells undergo functional or sensory loss due to neurological disorders. In addition to environmental or genetic factors, oxidative stress is a major contributor to neurodegeneration. In this context, there has been a growing interest in investigating the effects of EOs (EOs) in recent years, especially in the treatment of neuropathologies. The chemical and biological effects of EOs have led to important treatment tools for the management of various neurological disorders. Objective: In the present study we performed a systematic review that sought to comprehend the neuroprotective effects of different EOs. Method: This work is a systematic review where an electronic search was performed on PubMed, ScienceDirect, Cochrane Library and SciELO (Scientific Electronic Library Online) databases, covering the last 10 years, using “Essential oil” and “Neuroprotective effect” as reference terms. Results: A total of 9 articles were identified, in which the efficacy of EOs was described in experimental models of anxiety, dementia, oxidative stress, cerebral ischemia, Alzheimer’s disease, and oxidative toxicity. Conclusion: EOs from different species of medicinal plants have shown positive responses in neurological disorders such as anxiety, dementia, oxidative stress, cerebral ischemia, and oxidative toxicity. Thus, EOs emerges with the potential to be used as alternative agents in the treatment of neurological disorders.

Introduction It is common for patients to switch between several healthcare providers. In this context, the long-term follow-up of medical conditions based on laboratory test results obtained from different laboratories is a challenge. The measurement uncertainty in an inter-laboratory context should also be considered in data mining research based on routine results from randomly selected laboratories. As a proof-of-concept study, we aimed at estimating the inter-laboratory reference change value (IL-RCV) for exemplary analytes from publicly available data on external quality assessment (EQA) and biological variation. Materials and methods External quality assessment data of the Reference Institute for Bioanalytics (RfB, Bonn, Germany) for serum creatinine, calcium, aldosterone, PSA, and of whole blood HbA1c from campaigns sent out in 2019 were analysed. The median CVs of all EQA participants were calculated based on 8 samples from 4 EQA campaigns per analyte. Using intra-individual biological variation data from the EFLM database, positive and negative IL-RCV were estimated with a formula based on log transformation under the assumption that the analytes under examination have a skewed distribution. Results We estimated IL-RCVs for all exemplary analytes, ranging from 13.3% to 203% for the positive IL-RCV and - 11.8% to - 67.0% for the negative IL-RCV (serum calcium - serum aldosterone), respectively. Conclusion External quality assessment data together with data on the biological variation - both freely available - allow the estimation of inter-laboratory RCVs. These differ substantially between different analytes and can help to assess the boundaries of interoperability in laboratory medicine.

Wilson disease (WD) is characterized by a wide variety of clinical manifestations. Our study aimed to correlate genotype with clinical and radiological features in Indian WD patients. We conducted a descriptive observational study in a tertiary care neurology referral center of eastern India over a period of 2 years. Demographic data collection, clinical examination and relevant investigations were done for all WD patients meeting the inclusion criteria. Based on previous reports of mutation hotspots for WD in Eastern India, we performed PCR-Sanger sequencing of selected exons of ATP7B gene. To understand the role of each of these covariates on the occurrence of common mutation, we applied a logistic regression as well as random forest in a supervised learning framework. Fifty-two WD patients were included in the study. c.813C > A (p.C271X) was the commonest identified mutation. The statistical methods applied to our data-set reveal the most important features for predicting common mutation or its absence. We also found that the state-of-the-art classification algorithms are good at predicting the absence of common mutation (with true positive rates being 0.7647 and 0.8823 for logistic classifier and random forest, respectively), but predicting the occurrence remains a harder modeling challenge. WD patients in eastern India have significant genotypic and phenotypic diversity. Statistical methods for binary classification show some early promise of detecting common mutations and suggest important covariates, but further studies with larger samples and screening of remaining exons are warranted for understanding the full genetic landscape of Wilson disease.

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method’s performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F 1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secretase complex, leads to the production and release of amyloid-β peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-β in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.

Periodontitis is a disease that affects a wide group of people, and there has been an increased interest in the research of finding useful materials that help reduce inflammation and the further loss of tissue. In this study, we have tested a boron-containing compound (BCC) Calcium Fructoborate (CaFB) and Boric Acid (BA) hydrogels on the gingival level on Wistar rats. First, we have induced the periodontal disease at the lower incisors, we have applied the hydrogels and after a week, we have euthanized the rats. Next, the oral soft tissue reaction was clinically and then histologically investigated. Our study has shown good clinical response of the oral tissue, and we have noticed lower levels of inflammation on the experimental groups treated with the BCCs hydrogels. Despite the generally good response of the biological structures to the presence of BA and CaFB on periodontal level, more scientifically proved information is needed to obtain the desired biological responses in all clinical situations.

Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions.

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster9s symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we show that gait is compromised in healthy aging and that in young FMR1 premutation carriers, gait forecasts, even by 15 years ahead, symptoms resembling those of elderly with Parkinson9s disease. Our methods combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping, defining a new type of internal motor timings amenable to create personalized clinical interventions. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

Breastfeeding has a protective effect against acute respiratory and diarrheal infections. There are psychological and social effects due to physical isolation in the population in the mother-child group.To assess the impact on infant mortality due to a decrease in the prevalence of breastfeeding during 2020 due to the physical isolation against the SARS CoV-2 (COVID-19) pandemic in Colombia.We used the population attributable risk approach taking into account the prevalence of breastfeeding and its potential decrease associated with the measures of physical isolation and the relative risk (RR) of the association between exclusive breastfeeding and the occurrence of acute infection consequences in the growth (weight for height) of children under the age of five through a mathematical modeling program.We found an increase of 11.39% in the number of cases of growth arrest in the age group of 6 to 11 months with a 50% decrease in breastfeeding prevalence, as well as an increase in the number of diarrhea cases in children between 1 and 5 months of age from 5% (5.67%) on, and an increased number of deaths in children under 5 years (9.04%) with a 50% decrease in the prevalence of exclusive breastfeeding.A lower prevalence of breastfeeding has an impact on infant morbidity and mortality in the short and medium-term. As a public health policy, current maternal and childcare strategies must be kept in order to reduce risks in the pediatric population.Introducción. La lactancia materna tiene un efecto protector frente a infecciones respiratorias y diarreicas agudas. Hay efectos psicológicos y sociales por el aislamiento físico en la población en el grupo materno-infantil. Objetivo. Evaluar el eventual impacto en la mortalidad infantil de la disminución en la prevalencia de la lactancia materna durante el 2020 a causa del aislamiento físico por la pandemia del SARS CoV-2 (COVID-19) en Colombia. Materiales y métodos. Se utilizó el enfoque de riesgo atribuible poblacional, teniendo en cuenta la prevalencia de la lactancia materna y su potencial disminución asociada con las medidas de aislamiento físico y el riesgo relativo (RR) de la asociación entre la lactancia materna exclusiva y el efecto de la aparición de infecciones agudas en el crecimiento (peso para la altura) de niños menores de cinco años mediante un programa de modelamiento matemático. Resultados. Se registró un aumento del número casos de detención del crecimiento en el grupo etario de 6 a 11 meses de 11,39 % al disminuir en 50 % la prevalencia de la lactancia materna, así como un mayor número de casos por diarrea en los cinco primeros meses a partir del 5 % (5,67 %), y un incremento en el número de muertes en menores de 5 años (9,04 %) al disminuirse en 50 % la prevalencia de la lactancia materna. Conclusiones. Se registró un impacto en la morbilidad y la mortalidad infantil a corto y mediano plazo al disminuir la prevalencia en la lactancia materna. Como política pública en salud, deben mantenerse las estrategias actuales de atención materno-infantil para disminuir riesgos en la población infantil.

Background: Emergency general surgery (EGS) patients require urgent surgical evaluation and intervention for various conditions, such as infectious or obstructive diseases of the gastrointestinal tract. We aimed to characterize the structures and processes that are relevant to the delivery of EGS care across Ontario hospitals and to evaluate the availability of critical resources at hospitals with formal EGS models. Methods: Between August 2019 and July 2020, we conducted a cross-sectional survey of Ontario hospitals that offered urgent general surgery (defined as the ability to provide nonelective surgical intervention within 24 to 48 hours of presentation) to adults. People with intimate knowledge of their hospital’s EGS program completed a Web-based or telephone survey characterizing the program’s organizational structure and staffing, operating room availability, interventional radiology and interventional endoscopy availability, intensive care unit availability and staffing, and regional participation. Their responses were compiled and comparisons were made between hospitals with and without formal EGS models of care, as well as between hospitals based on size and academic status. Results: Of the 114 Ontario hospitals identified, 109 responded (95.6% response rate). A third (34.6%; n = 37/107) of hospitals had EGS models of care. Thirty-four of these (91.9%) were large (> 100-bed) institutions that would be likely to have increased resources. However, even for hospitals of similar size, those with EGS models had increased staffing levels compared to those without (clinical associates 17.6% [n = 3/17] v. 10.0% [n = 2/20]; nurse practitioners or physician assistants 27.8% [n = 5/18] v. 14.3% [n = 3/21]). They also had better access to diagnostic and interventional equipment (24/7 access to computed tomography 94.1% [n = 16/17] v. 69.2% [n = 18/26]), interventional radiology (88.9% [n = 16/18] v. 42.3% [n = 11/26]), endoscopy (100% [n = 18/18] v. 69.2% [n = 18/26]) and endoscopic retrograde cholangiopancreatography (77.8% [n = 14/18] v. 42.3% [n = 11/26]), as well as dedicated operating room time (72.2% [n = 13/18] v. 0% [n = 0/25]). Interpretation: The structures and processes available to care for patients requiring EGS in Ontario were highly variable between hospitals. Hospitals with formal EGS models were more likely to have access to key resources.