Optimizacija prehospitalnih strategija upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou pomoću algortima za duboko učenje

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the pre-hospital first aid efficiency. At the Department of Emergency, Huizhou Third People’s Hospital as the research subject, the common pre-hospital first aid procedure for infectious diseases was identified. The Petri net was used to model and determine the execution time of each link of the pre-hospital first aid process. The isomorphic Markov chain was used to optimize the pre-hospital first aid procedure for infectious diseases. In terms of the emergency path, deep learning was combined with the reinforcement learning model to construct the reinforcement learning model for ambulance path planning. Isomorphic Markov chain analysis revealed that the patient status when returning to the hospital, the time needed for the ambulance to come to designated location, and the on-site treatment were the main problems in the first aid process, and the time needed for the pre-hospital first aid process was reduced by 25.17% after optimization. In conclusion, Petri net and isomorphic Markov chain can optimize the pre-hospital first aid management strategies for patients with infectious diseases, and the use of deep learning algorithm can effectively plan the emergency path, achieving intelligent and informationalized pre-hospital transfer, which provides a basis for reducing the suffering, mortality, and disability rate of patients with infectious diseases.Cilj istraživanja bio je optimizirati strategiju prehospitalnog upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou, Kina, za koju se očekuje da pruži osnovu za prevenciju i kontrolu epidemije, da spasi živote te da poveća učinkovitost prehospitalne prve pomoći. Istraživanje je provedeno na Hitnom odjelu Treće narodne bolnice u gradu Huizhou, gdje je utvrđen opći prehospitalni postupak prve pomoći za zarazne bolesti. Petrijeva mreža je primijenjena kako bi se modeliralo i odredilo vrijeme izvršenja svake karike u procesu prehospitalne prve pomoći. Izomorfni Markovljev lanac primijenjen je za optimizaciju prehospitalnog postupka prve pomoći za zarazne bolesti. Za putanju hitnosti, duboko učenje je kombinirano s modelom pojačanog učenja kako bi se konstruirao model osnaživanja učenja za planiranje putanje vozila hitne pomoći. Analiza Markovljeva lanca pokazala je da su status bolesnika na povratku u bolnicu, vrijeme potrebno da vozilo hitne pomoći dođe na određenu lokaciju i skrb na mjestu događaja glavni problemi u procesu prve pomoći te da je vrijeme potrebno za prehospitalni proces prve pomoći smanjeno za 25,17% nakon optimizacije. Zaključeno je da Petrijeva mreža i izomorfni Markovljev lanac mogu optimizirati strategije upravljanja prehospitalnom prvom pomoći za bolesnike sa zaraznim bolestima te da primjena algoritma dubokog učenja može učinkovito planirati putanju tima hitne pomoći, čime se postiže pametan i informatizirani prehospitalni prijevoz, što čini osnovu za smanjenje patnje, smrtnosti i stope invalidnosti za bolesnike sa zaraznim bolestima

Tianshengyuan-1 (TSY-1) regulates cellular Telomerase activity by methylation of TERT promoter.

Telomere and Telomerase have recently been explored as anti-aging and anti-cancer drug targets with only limited success. Previously we showed that the Chinese herbal medicine Tianshengyuan-1 (TSY-1), an agent used to treat bone marrow deficiency, has a profound effect on stimulating Telomerase activity in hematopoietic cells. Here, the mechanism of TSY-1 on cellular Telomerase activity was further investigated using HL60, a promyelocytic leukemia cell line, normal peripheral blood mononuclear cells, and CD34+ hematopoietic stem cells derived from umbilical cord blood. TSY-1 increases Telomerase activity in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells with innately low Telomerase activity but decreases Telomerase activity in HL60 cells with high intrinsic Telomerase activity, both in a dose-response manner. Gene profiling analysis identified Telomerase reverse transcriptase (TERT) as the potential target gene associated with the TSY-1 effect, which was verified by both RT-PCR and western blot analysis. The β-galactosidase reporter staining assay showed that the effect of TSY-1 on Telomerase activity correlates with cell senescence. TSY-1 induced hypomethylation within TERT core promoter in HL60 cells but induced hypermethylation within TERT core promoter in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells. TSY-1 appears to affect the Telomerase activity in different cell lines differently and the effect is associated with TERT expression, possibly via the methylation of TERT promoter

Modality- and task-specific brain regions involved in Chinese lexical processing

fMRI was used to examine lexical processing in native adult Chinese speakers. A 2 task (semantics and phonology) x 2 modality (visual and auditory) within-subject design was adopted. The semantic task involved a meaning association judgment and the phonological task involved a rhyming judgment to two sequentially presented words. The overall effect across tasks and modalities was used to identify seven ROIs, including the left fusiform gyrus (FG), the left superior temporal gyrus (STG), the left ventral inferior frontal gyrus (VIFG), the left middle temporal gyrus (MTG), the left dorsal inferior frontal gyrus (DIFG), the left inferior parietal lobule (IPL), and the left middle frontal gyrus (MFG). ROI analyses revealed two modality-specific areas, FG for visual and STG for auditory, and three task-specific areas, IPL and DIFG for phonology and VIFG for semantics. Greater DIFG activation was associated with conflicting tonal information between words for the auditory rhyming task, suggesting this region's role in strategic phonological processing, and greater VIFG activation was correlated with lower association between words for both the auditory and the visual meaning task, suggesting this region's role in retrieval and selection of semantic representations. The modality- and task-specific effects in Chinese revealed by this study are similar to those found in alphabetical languages. Unlike English, we found that MFG was both modality- and task-specific, suggesting that MFG may be responsible for the visuospatial analysis of Chinese characters and orthography-to-phonology integration at a syllabic level

The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression

This study investigates the impact of foreign direct investment (FDI), economic growth and energy consumption on carbon emissions in five selected member countries in the Association of South East Asian Nations (ASEAN-5), including Indonesia, Malaysia, the Philippines, Singapore and Thailand. This paper employs a panel quantile regression model that takes unobserved individual heterogeneity and distributional heterogeneity into consideration. Moreover, to avoid an omitted variable bias, certain related control variables are included in our model. Our empirical results show that the effect of the independent variables on carbon emissions is heterogeneous across quantiles. Specifically, the effect of FDI on carbon emissions is negative, except at the 5th quantile, and becomes significant at higher quantiles. Energy consumption increases carbon emissions, with the strongest effects occurring at higher quantiles. Among the high-emissions countries, greater economic growth and population size appear to reduce emissions. The results of the study also support the validity of the halo effect hypothesis in higher-emissions countries. However, we find little evidence in support of an inverted U-shaped curve in the ASEAN-5 countries. In addition, a higher level of trade openness can mitigate the increase in carbon emissions, especially in low- and high-emissions nations. Finally, the results of the study also provide policymakers with important policy recommendations

Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide

Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and human migration factors shaped the specific geographical distribution of the population structure. This study provides a different approach using clustering methods to study the population structure of a never-seen-before and fast-growing species such as SARS-CoV-2. Moreover, clustering techniques can be used for further studies of local population structures of the proliferating virus

Optimization of Pre-Hospital First Aid Management Strategies for Patients with Infectious Diseases in Huizhou City using Deep Learning Algorithm

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the pre-hospital first aid efficiency. At the Department of Emergency, Huizhou Third People’s Hospital as the research subject, the common pre-hospital first aid procedure for infectious diseases was identified. The Petri net was used to model and determine the execution time of each link of the pre-hospital first aid process. The isomorphic Markov chain was used to optimize the pre-hospital first aid procedure for infectious diseases. In terms of the emergency path, deep learning was combined with the reinforcement learning model to construct the reinforcement learning model for ambulance path planning. Isomorphic Markov chain analysis revealed that the patient status when returning to the hospital, the time needed for the ambulance to come to designated location, and the on-site treatment were the main problems in the first aid process, and the time needed for the pre-hospital first aid process was reduced by 25.17% after optimization. In conclusion, Petri net and isomorphic Markov chain can optimize the pre-hospital first aid management strategies for patients with infectious diseases, and the use of deep learning algorithm can effectively plan the emergency path, achieving intelligent and informationalized pre-hospital transfer, which provides a basis for reducing the suffering, mortality, and disability rate of patients with infectious diseases