Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

italic>Lamiophlomis rotata is an important medicinal plant species endemic to the Tibetan Plateau, which is prone to strong climate change impacts on its habitable range due to the high sensitivity of the Tibetan Plateau to climate change. Accurate quantification of species vulnerability to climate change is essential for assessing species extinction risk and developing effective conservation strategies. Therefore, we carried out the α-shape analysis to determine the habitat of L. rotata. We then carried out the climate-niche factor analysis (CNFA) to assess the vulnerability of L. rotata to climate change based on five climate variables (i.e., mean diurnal range, temperature seasonality, mean temperature of warmest quarter, precipitation of driest month and precipitation of warmest quarter) in the context of two shared socioeconomic pathways (i.e., SSP126 and SSP585) and three global climate models (CMCC-ESM2: Centro Euro-Mediterraneo sui Cambiamenti Climatici-Earth System Model version 2; HadGEM3-GC31-LL: Hadley Global Environment Model version 3-Global Coupled configuration 3.1; IPSL-CM6A-LR: Institut Pierre Simon Laplace-Climate Model version 6) during two different periods (2041-2060 and 2081-2100). The vulnerability of L. rotata to climate change was calculated by integrating the sensitivity and exposure indices of L. rotata to five climate variables. The results showed that L. rotata had the highest vulnerability to the precipitation of warmest quarter. Its vulnerability within its habitat range generally showed a spatial pattern of high value in the southern region and low in the northern region, high in the western region and low in the eastern region. In general, the vulnerability of L. rotata under the SSP585 scenario was higher than that under the SSP126 scenario. The climate data of different global climate models have some influence on the results, while the resulted uncertainty can be reduced by data integration methods. As a result of climate change, the pressure on the survival of L. rotata in the future will be intensified in the low-altitude areas such as the Yarlung Zangbo River, Yigongzangbu River, Zayu River, and Jiaomuzu River, etc., while the highly weathered scree flats or stony alpine meadows in the high-altitude zones, such as the eastern Tanggula Mountain Range, the northern part of Hengduan Mountain Range, and the western part of the Qinling Mountains, may become its refuge. It is necessary to focus on and strengthen the protection and management of L. rotata resources in these vulnerble and critical areas.

Abdomen/surgery* ; Humans ; Laparoscopy/methods* ; Pelvic Floor/surgery* ; Perineum/surgery* ; Proctectomy ; Rectal Neoplasms/surgery*

Objective:To explore whether renal artery involvement is an independent risk factor of acute renal injury (AKI) KDIGO stage 3 after moderate hypothermic circulatory arrest in patients with acute Stanford type A aortic dissection.Methods:From December 2015 to October 2017, 492 consecutive patients with acute Stanford A-type aortic dissection received surgical treatment, 486 of them were included in the study. All patients underwent aortic CTA to determine the extent of aortic dissection and renal artery involvement. According to the standard of Improving Global Outcomes (KDIGO), the renal function of patients after operation was graded. The risk factors of AKI KDIGO stage 3 were analyzed.Renal artery involvement and other risk factors were included in univariate analysis, and significant variables in univariate analysis were included in multivariate logistic regression analysis.Results:In 492 patients, 40 (8.13%) died in hospital, of which 6 died of severe bleeding during operation or failed to wean from cardiopulmonary bypass which lead to unable to leave the Weaning from cardiopulmonary bypass and these 6 patients were excluded in the research. Among 486 patients included in the study, 251 (51.64%) had AKI. Among them, 83 (17.08%) were in the KDIGO stage 1, 56 (11.52%) in stage 2 and 112 (23.05%) in stage 3.The results of univariate analysis showed that there were significant differences in renal artery involvement, age, time from onset to operation, D-dimer, leukocytes and platelets in peripheral blood, creatinine clearance rate, time of cardiopulmonary bypass during operation and aortic cross-clamping time( P>0.05). The above risk factors were included in multivariate logistic regression. The results showed that preoperative renal artery involvement ( OR=1.94, P=0.02), age ( OR=1.03, P=0.02), creatinine clearance rate<85 ml/min ( OR=2.28, P=0.001), and intraoperative cardiopulmonary bypass time ( OR=1.01, P=0.02) were independent risk factors. The incidence of AKI in patients with renal artery involvement was 54.65%, significantly higher than 41.98% in patients without renal artery involvement ( P>0.05). Conclusion:Renal artery involvement is an independent risk factor of AKI KDIGO stage 3 after moderate deep hypothermic circulatory arrest of acute Stanford type A aortic dissection.

Objective:To establish a method for evaluating the biological activity of water extract lyophilized powder of Qingjin Huatantang based on the phagocytic and secretory functions of macrophages， and to control the quality of this formula from the biological activity level. Method:The phagocytic and inflammation models of RAW264.7 macrophages were established， the inhibition rates of water extract lyophilized powder of Qingjin Huatantang on interleukin-6 （IL-6） secretion and phagocytic index of neutral red of RAW264.7 macrophages were chosen as indicators to investigate the biological activity of Qingjin Huatantang， and the biological limit was searched. Result:The optimal inoculation density of RAW264.7 macrophages was 3×10⁵ pcs/mL， and the concentration of lipopolysaccharide （LPS） was 1 mg·L^-1 after treatment for 24 h. When the concentration was 500 mg·L^-1， water extract lyophilized powder of Qingjin Huatantang had no toxicity and no obvious promotion effect on the proliferation of RAW264.7 macrophages， and at this concentration， the phagocytosis of RAW264.7 macrophages for neutral red was significantly promoted， the phagocytic index was >113%. In addition， the lyophilized powder had a significant and stable inhibitory effect on IL-6 secretion of RAW264.7 macrophages induced by LPS， the inhibitory rate was >45%. Conclusion:Combined with the anti-inflammatory and immunomodulatory effects of Qingjin Huatantang， this study establishes an in vitro biological limit method for evaluating the quality of water extract of Qingjin Huatantang based on the phagocytic and secretory functions of RAW264.7 macrophages， and 500 mg·L^-1 was confirmed as the limit concentration. Under the limit concentration， Qingjin Huatantang water extract can significantly promote the phagocytic index of macrophages or significantly inhibit the secretion of IL-6 of RAW264.7 macrophages induced by LPS， which can be judged as qualified.

Intercropping farming system is one of the essence of traditional agriculture in China and one of the most common and basic patterns of modern ecological planting. Intercropping system uses the principle of species diversity to create reasonable interspecific interaction conditions with obvious productivity advantages. In this paper, the interspecies interaction is divided into aboveground and underground parts from the space view, and its influence and mechanism on the yield and secondary metabolites of medicinal plants are elaborated.The interspecific interaction in the aboveground part mainly introduces the distribution and utilization of space resources among plants. The interspecific interaction in the underground part mainly introduces the soil rhizosphere effect and related mediating factors, root exudates, soil microorganisms, root space structure and soil environmental factors. On the basis of understanding the mechanism of interspecific interaction, this paper further discusses the application of intercropping in traditional Chinese medicine ecological agriculture, taking the effective control of diseases and insect pests, the increase of medicinal material yield and the improvement of medicinal material quality as the benefit index, so as to seek better advantages of intercropping and provide ideas for the utilization of intercropping production mode in traditional Chinese medicine ecological agriculture.
Agriculture ; China ; Plants, Medicinal ; Rhizosphere ; Soil

Objective:Analysis of clustering characteristics of coronavirus disease 2019 (COVID-19) in Guangdong Province.Methods:The COVID-19 cases in Guangdong Province onset from January 1 to February 29, 2020 were collected from Chinese information system for disease control and prevention and Emergency Public Reporting System. Obtain the epidemiological survey data of the cluster epidemic situation, and clarify the scale of cluster epidemic situation, the characteristics of the index cases, family and non-family subsequent cases. Calculate serial interval according to the onset time of the index cases and subsequent cases, secondary attack rate based on the close contacts tracking results, the characteristics of different cases in the clustered epidemic were compared.Results:A total of 283 cluster were collected, including 633 index cases, 239 subsequent cases. Families are mainly clustered, the total number involved in each cluster is in the range of 2-27, M( P25, P75) are 2.0 (2.0, 4.0). During January 15 to February 29, the secondary attack rate is 2.86% (239/8 363) in Guangdong Province, the family secondary attack rate was 4.84% (276/3 697), and the non-family secondary attack rate was 1.32% (61/4 632). According to the reporting trend of the number of cases in Guangdong Province, it can be divided into four stages, the rising stage, the high platform stage, the descending stage and the low level fluctuation period. The secondary attack rate of the four stages were 3.5% (140/3 987), 2.3% (55/2 399), 2.6% (37/1 435), 1.3% (7/542), respectively. The difference was statistically significant ( P=0.003). Conclusion:COVID-19 cluster mainly occurs in families in Guangdong Province. The scale of the clustered epidemic was small; the serial interval was short; and the overall secondary attack rate was low.

Objective:Analysis of clustering characteristics of coronavirus disease 2019 (COVID-19) in Guangdong Province.Methods:The COVID-19 cases in Guangdong Province onset from January 1 to February 29, 2020 were collected from Chinese information system for disease control and prevention and Emergency Public Reporting System. Obtain the epidemiological survey data of the cluster epidemic situation, and clarify the scale of cluster epidemic situation, the characteristics of the index cases, family and non-family subsequent cases. Calculate serial interval according to the onset time of the index cases and subsequent cases, secondary attack rate based on the close contacts tracking results, the characteristics of different cases in the clustered epidemic were compared.Results:A total of 283 cluster were collected, including 633 index cases, 239 subsequent cases. Families are mainly clustered, the total number involved in each cluster is in the range of 2-27, M( P25, P75) are 2.0 (2.0, 4.0). During January 15 to February 29, the secondary attack rate is 2.86% (239/8 363) in Guangdong Province, the family secondary attack rate was 4.84% (276/3 697), and the non-family secondary attack rate was 1.32% (61/4 632). According to the reporting trend of the number of cases in Guangdong Province, it can be divided into four stages, the rising stage, the high platform stage, the descending stage and the low level fluctuation period. The secondary attack rate of the four stages were 3.5% (140/3 987), 2.3% (55/2 399), 2.6% (37/1 435), 1.3% (7/542), respectively. The difference was statistically significant ( P=0.003). Conclusion:COVID-19 cluster mainly occurs in families in Guangdong Province. The scale of the clustered epidemic was small; the serial interval was short; and the overall secondary attack rate was low.

Objective    To evaluate the involvement of renal artery in acute Stanford type A aortic dissection (TAAD) using CT angiography (CTA) and to analyze the difference of renal function among different types of renal artery involvement. Methods    From January 2016 to November 2017, 151 patients of acute TAAD with renal artery involvement were included in the study. There were 118 males and 33 females, with an average age of 47.93±10.53 years. All patients underwent aortic CTA to confirm the TAAD. According to CTA，involvement of one side of renal artery can be divided into four types: type A, large tear near renal artery orifice, difficult to distinguish true or false lumen; type B, the orifice of the renal artery originates entirely from the false lumen; type C, the orifice of the renal artery originates entirely from the true lumen; type D, renal artery dissection is observed, renal artery intima can be seen. The levels of serum creatinine (sCr) and creatinine clearance (CC) in all groups were analyzed and compared. Results    The results of one-way ANOVA analysis showed that there was no significant difference in sCr or CC among the groups (P>0.05). There was no significant difference in age, sex, proportion of hypertension history and onset time among the above groups (P>0.05). Conclusion    The three most common types of renal artery involvement were BC type, CC type, and AC type. The types of renal artery involvement do not affect renal function.