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Background Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. Objective To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. Methods 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. Results Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08–8.72], P  < 0.001), alcoholic AP (OR = 3.60 [1.67–7.72], P  = 0.001), hyperlipidemic AP (OR = 2.63 [1.28–5.37], P  = 0.008) and tumor AP (OR = 4.57 [2.14–9.72], P  < 0.001). APCU yielded the highest clinical net benefit, comparatively. Conclusion Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.

Background and aims Digestive tract cancers, encompassing esophageal, stomach, colorectal cancers, account for about 20% of all cancer cases worldwide. This study seeks to examine the global epidemiological trends of these cancers in adolescents and young adults (AYA) from 1990 to 2021, as well as the related risk factors, to offer valuable insights for the formulation of health policies and the efficient distribution of public health resources. Methods Data from the Global Burden of Disease 2021 (GBD 2021) were utilized to conduct a comprehensive assessment of the global incidence, death, and disability-adjusted life years (DALYs) of digestive tract cancers within the AYA population. The correlation between cancer burden and socioeconomic development was examined by Socio-Demographic Index (SDI) regions. Use comparative risk assessment from GBD to evaluate risk factors for cancer deaths and DALYs. Assess incidence trends with BAPC model. Results The global burden of digestive tract cancer increased at different rates from 1990 to 2021. In 2021, colorectal cancer in AYA accounted for 70,201 incidences, 26,222 deaths, and 1,538,837 DALYs worldwide. In contrast, esophageal and stomach cancer had lower burdens in 8,164 and 42,039 incidences, respectively. While the incidence and death rates of esophageal and stomach cancers have declined, colorectal cancer have shown a significant increase in AYA. The age-standardized incidence rate (ASIR) of colorectal cancer correlates positively with SDI. In AYA, level 2 risk factors for colorectal cancer include alcohol use, dietary risks, high body mass index, high fasting plasma glucose, low physical activity, and tobacco. For esophageal cancer, the risk factors are alcohol use, dietary risks, and tobacco, and for stomach are dietary risks and tobacco. By 2040, the ASIR of the three digestive tract cancers in AYA will all decrease according to the BAPC model prediction. Conclusion The study of digestive tract cancers in AYA serves to identify risk factors within the realm of public health, facilitate the rational allocation of medical resources. Moreover, it can attend to the physical and mental health requirements of AYA individuals and enhance the cognitive framework regarding cancer.

BackgroundEarly detection is critical in limiting the spread of 2019 novel coronavirus (COVID-19). Although previous data revealed characteristics of GI symptoms in COVID-19, for patients with only GI symptoms onset, their diagnostic process and potential transmission risk are still unclear.MethodsWe retrospectively reviewed 205 COVID-19 cases from January 16 to March 30, 2020, in Renmin Hospital of Wuhan University. All patients were confirmed by virus nuclei acid tests. The clinical features and laboratory and chest tomographic (CT) data were recorded and analyzed.ResultsA total of 171 patients with classic symptoms (group A) and 34 patients with only GI symptoms (group B) were included. In patients with classical COVID-19 symptoms, GI symptoms occurred more frequently in severe cases compared to non-severe cases (20/43 vs. 91/128, respectively, p < 0.05). In group B, 91.2% (31/34) patients were non-severe, while 73.5% (25/34) patients had obvious infiltrates in their first CT scans. Compared to group A, group B patients had a prolonged time to clinic services (5.0 days vs. 2.6 days, p < 0.01) and a longer time to a positive viral swab normalized to the time of admission (6.9 days vs. 3.3 days, respectively, p < 0.01). Two patients in group B had family clusters of SARS-CoV-2 infection.ConclusionPatients with only GI symptoms of COVID-19 may take a longer time to present to healthcare services and receive a confirmed diagnosis. In areas where infection is rampant, physicians must remain vigilant of patients presenting with acute gastrointestinal symptoms and should do appropriate personal protective equipment.

Development of computer science has led to the blooming of artificial intelligence (AI), and neural networks are the core of AI research. Although mainstream neural networks have done well in the fields of image processing and speech recognition, they do not perform well in models aimed at understanding contextual information. In our opinion, the reason for this is that the essence of building a neural network through parameter training is to fit the data to the statistical law through parameter training. Since the neural network built using this approach does not possess memory ability, it cannot reflect the relationship between data with respect to the causality. Biological memory is fundamentally different from the current mainstream digital memory in terms of the storage method. The information stored in digital memory is converted to binary code and written in separate storage units. This physical isolation destroys the correlation of information. Therefore, the information stored in digital memory does not have the recall or association functions of biological memory which can present causality. In this paper, we present the results of our preliminary effort at constructing an associative memory system based on a spiking neural network. We broke the neural network building process into two phases: the Structure Formation Phase and the Parameter Training Phase. The Structure Formation Phase applies a learning method based on Hebb's rule to provoke neurons in the memory layer growing new synapses to connect to neighbor neurons as a response to the specific input spiking sequences fed to the neural network. The aim of this phase is to train the neural network to memorize the specific input spiking sequences. During the Parameter Training Phase, STDP and reinforcement learning are employed to optimize the weight of synapses and thus to find a way to let the neural network recall the memorized specific input spiking sequences. The results show that our memory neural network could memorize different targets and could recall the images it had memorized.

Artificial Intelligence (AI) has become very popular due to both the increasing demands from applications and the booming of computer techniques. Spiking Neural Network (SNN), as the third generation of Artificial Neural Network, receives more and more attention in the field of AI. With the high similarity to biological neural network, SNN has the potential to break through the barriers of strong AI. However, the using of SNNs on practical scenarios is rather limited, as a result of the lack of high efficient learning algorithms. Nowadays, learning methods of SNNs are designed mainly based on previous biological discoveries. The fact that there are both excitatory neurons and inhibitory neurons in the biological neural network has stimulated the motive of this research. The existence of inhibitory neurons could strengthen the self-regulation ability of neural networks and improve learning efficiency. Inspired by the ancient Chinese “Yin and Yang” Theory, we first presented our effort at constructing SNN structure with equilibrated excitatory neurons and inhibitory neurons. Then an ensemble learning optimized supervised learning method is designed and tailored for this SNN structure. Experiments are conducted using MNIST data sets, and results show that, with the designed learning mechanism, our equilibrated bipolar SNN structure could gain reasonable accuracy with much more compact structure and much more sparse synapse connections.

The primary factor leading to elevated rates of diarrhea and decreased performance in piglets is immunological stress. The regulation of immune stress through the intestinal flora is a crucial mechanism to consider. In total, 30 weaned piglets were randomly allocated to five groups: the basal diet group (Control), basal diet + lipopolysaccharides group (LPS), basal diet + 250 μg/kg 6-Formylindolo [3,2-b] carbazole + LPS group (FICZ), basal diet + 3mg/kg Cardamonin + LPS group (LCDN), and basal diet + 6mg/kg Cardamonin + LPS group (HCDN/CDN). The results showed that compared with those of the LPS group, the expression of tight junction proteins (occludin; claudin-1) in the FICZ group was significantly increased, and the mRNA levels of IL-1β and TNF-α were significantly reduced (p < 0.05). HCDN treatment had a better effect on LPS-induced intestinal barrier damage in this group than it did in the LCDN group. HCDN treatment leads to a higher villus height (VH), a higher ratio of villi height to crypt depth (V/C), higher tight junction proteins (ZO-1; occludin), and higher short-chain fatty acids (SCFAs). In addition, correlation analyses showed that Succinivibrio was positively correlated with several SCFAs and negatively correlated with prostaglandin-related derivatives in the FICZ group and CDN group (p < 0.05). In summary, Cardamonin alleviates intestinal mucosal barrier damage and inflammatory responses by regulating the intestinal microbiota and its metabolism.

Flaviviruses are single-stranded RNA viruses predominantly transmitted by the widely distributed Aedes mosquitoes in nature. As important human pathogens, the geographic reach of Flaviviruses and their threats to public health are increasing, but there is currently no approved specific drug for treatment. In recent years, the development of peptide antivirals has gained much attention. Natural host defense peptides which uniquely evolved to protect the hosts have been shown to have antiviral properties. In this study, we firstly collected the venom of the Alopecosa nagpag spider from Shangri-La County, Yunnan Province. A defense peptide named Av-LCTX-An1a (Antiviral-Lycotoxin-An1a) was identified from the spider venom, and its anti-dengue serotype-2 virus (DENV2) activity was verified in vitro. Moreover, a real-time fluorescence-based protease inhibition assay showed that An1a functions as a DENV2 NS2B–NS3 protease inhibitor. Furthermore, we also found that An1a restricts zika virus (ZIKV) infection by inhibiting the ZIKV NS2B–NS3 protease. Together, our findings not only demonstrate that An1a might be a candidate for anti-flavivirus drug but also indicate that spider venom is a potential resource library rich in antiviral precursor molecules.

Objectives This study investigates curcumin's neuroprotective role and its potential in promoting neurogenesis in progenitor cells within the brain. Notably, curcumin's antioxidant properties have been implicated in Alzheimer's disease treatment. However, the association between curcumin's antioxidative effects and its impact on neural stem cells (NSCs) remains to be elucidated. Methods C17.2 neural stem cells were utilized as a model to simulate oxidative stress, induced by hydrogen peroxide (H2O2). We quantified the levels of superoxide dismutase (SOD), malondialdehyde (MDA), and intracellular reactive oxygen species (ROS), alongside the gene expression of SOD1 and SOD2, to assess intracellular oxidative stress. Additionally, Western blot analysis was conducted to measure the expressions of LC3‐II, Beclin‐1, and phosphorylated ERK (p‐ERK), thereby evaluating autophagy and ERK signaling pathway activation. Results Treatment with curcumin resulted in a reduction of MDA and ROS levels, suggesting a protective effect on NSCs against oxidative damage induced by H2O2. Furthermore, a decrease in the relative expressions of LC3‐II, Beclin‐1, and p‐ERK was observed post‐curcumin treatment. Conclusions The findings suggest that curcumin may confer protection against oxidative stress by attenuating autophagy and deactivating the ERK1/2 signaling pathways, which could contribute to therapeutic strategies for Alzheimer's disease. Curcumin, as a neuroprotective agent which promotes the neurogenesis, is also found to be contributing in the treatment of Alzheimer's disease with its antioxidant properties. Herein, we experimentally demonstrate that curcumin would protect against oxidative stress by inhibiting autophagy and inactivating the ERK1/2 signaling pathways, as illustrated by Figdraw.

Background Thymoquinone (TQ), an active ingredient of the seed oil extract of Nigella sativa Linn, has previously been shown to possess antitumor, antioxidant, and anti-inflammatory bioactivity. Whether TQ has any effect on colitis remains controversial. Aim The aim of this study was to determine whether treatment with TQ prevents and ameliorates colonic inflammation in a mouse model of inflammatory bowel disease. Methods C57BL/6 murine colitis was induced by the administration of dextran sodium sulfate (DSS) (3 % W/V) in the drinking water supplied to the mice for 7 consecutive days. The mice with colitis were treated with 5, 10, or 25 mg/kg TQ orally, and changes in body weight and macroscopic and microscopic colitis scores were examined. In addition, biochemical analyses were conducted. Results The treatment of mice with TQ prevented and significantly reduced the appearance of diarrhea and body weight loss. These results were associated with amelioration of colitis-related damage, as measured by macroscopic and microscopic colitis scores. In addition, there was a significant reduction in colonic myeloperoxidase activity and malondialdehyde levels and an increase in glutathione levels. Conclusions These results indicate that TQ administration can prevent and improve murine DSS-induced colitis. These findings suggest that TQ could serve as a potential therapeutic agent for the treatment of patients with inflammatory bowel disease.

It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posteriority learning. However, traditionally, artificial neural network, especially the Deep learning Neural Networks (DNNs) are implemented only through exhaustive training and learning. Fixed structure is built, and then parameters are trained through huge amount of data. In this way, there are a lot of redundancies in the implemented artificial neural network. This redundancy not only requires more effort to train the network, but also costs more computing resources when used. In this paper, we proposed a bionic way to implement artificial neural network through construction rather than training and learning. The hierarchy of the neural network is designed according to analysis of the required functionality, and then module design is carried out to form each hierarchy. We choose the Drosophila's visual neural network as a test case to verify our method's validation. The results show that the bionic artificial neural network built through our method could work as a bionic compound eye, which can achieve the detection of the object and their movement, and the results are better on some properties, compared with the Drosophila's biological compound eyes.