All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing.

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

Alternatones A and B, two polyketides possessing novel skeletons from entophyte Alternaria alternate L-10.

On our ongoing searching for bioactive natural products derived from entophytes, two polyketides possessing novel skeletons, alternatones A-B (1-2), were identified from the culture of Alternaria alternate L-10. Their structures were established by a combination of spectroscopic and single-crystal X-ray diffraction with Cu Ka radiation. Alternatone A (1) exhibited cytotoxic activity against human hepatoma carcinoma HepG-2 cell line. The putative biosynthetic pathways for compounds 1-2 were also proposed.

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model's prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.

Protective effects of alpinetin on lipopolysaccharide/d-Galactosamine-induced liver injury through inhibiting inflammatory and oxidative responses.

Alpinetin, a type of novel plant flavonoid derived from Alpinia katsumadai Hayata, has been reported to have anti-inflammatory effects. The aim of this investigation was designed to reveal the protective effects of alpinetin on Lipopolysaccharide (LPS)/d-galactosamine (D-Gal)-induced liver injury in mice. Alpinetin (12.5, 25, 50 mg/kg) were given 1 h before LPS and D-Gal treatment. 12 h after LPS and D-Gal treatment, the liver tissues and serum were collected. Our results showed that alpinetin treatment improved liver histology, indicating a marked decrease of inflammatory cell infiltration and restore hepatic lobular architecture. Alpinetin also inhibited liver myeloperoxidase (MPO) activity and malondialdehyde (MDA) level. Furthermore, LPS/D-Gal-induced tumor necrosis factor-α (TNF-α) and Interleukin-1ß (IL-1ß) production were dose-dependently inhibited by alpinetin. Alpinetin also attenuated LPS/D-Gal-induced expression of phospho-NF-κB p65 and phospho-IκBα. In addition, alpinetin was found to increase the expression of nuclear factor E2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1). In conclusion, these findings suggested that alpinetin inhibited liver injury through inhibiting NF-κB and activating the Nrf2 signaling pathway.

Efficacy of poly-unsaturated fatty acid therapy on patients with nonalcoholic steatohepatitis.

AIM: To examine whether poly-unsaturated fatty acid (PUFA) therapy is beneficial for improving nonalcoholic steatohepatitis (NASH). METHODS: In total, 78 patients pathologically diagnosed with NASH were enrolled and were randomly assigned into the control group and the PUFA therapy group (added 50 mL PUFA with 1:1 ratio of EHA and DHA into daily diet). At the initial analysis and after 6 mo of PUFA therapy, parameters of interest including liver enzymes, lipid profiles, markers of inflammation and oxidation, and histological changes were evaluated and compared between these two groups. RESULTS: At the initial analysis, in patients with NASH, serum levels of alanine aminotransferase (ALT) and aspartase aminotransferase (AST) were slightly elevated. Triglyceride (TG), total cholesterol (TC) and low-density lipoprotein cholesterol levels, markers of systemic inflammation [C-reactive protein (CRP)] and oxidation [malondialdehyde (MDA)], as well as fibrosis parameters of type IV collagen and pro-collagen type III pro-peptide were also increased beyond the normal range. Six months later, ALT and AST levels were significantly reduced in the PUFA group compared with the control group. In addition, serum levels of TG and TC, CRP and MDA, and type IV collagen and pro-collagen type III pro-peptide were also simultaneously and significantly reduced. Of note, histological evaluation showed that steatosis grade, necro-inflammatory grade, fibrosis stage, and ballooning score were all profoundly improved in comparison to the control group, strongly suggesting that increased PUFA consumption was a potential way to offset NASH progression. CONCLUSION: Increased PUFA consumption is a potential promising approach for NASH prevention and reversal.

4-[(3-Meth-oxy-anilino)methyl-idene]-2-phenyl-1,3-oxazol-5(4H)-one.

In the title compound, C(17)H(14)N(2)O(3), the oxazolone ring is essentially planar [maximum deviation = 0.004 (1) Å] and is oriented with respect to the phenyl and benzene rings at 10.06 (9) and 5.63 (8)°, respectively; the dihedral angle between the phenyl ring and the benzene ring is 15.69 (8)°. In the crystal, N-H⋯O hydrogen bonds link the mol-ecules into chains running along the a axis. Neighbouring chains are inter-connected by π-π stacking, the centroid-centroid distance being 3.6201 (9) Å.

Enantioselective self-assembly of chiral calix[4]arene acid with amines.

A chiral calix[4]arene bearing l-2,3-dibenzoyltartaric acid groups at the lower rim was synthesized and could enantioselectively self-assemble to result in a gel or suspension only with one enantiomer of chiral amines; as revealed by electron microscopy, the resultant gel was of nanofibers with a high aspect ratio but the suspension was composed of nanospheres.