Interphase (Xiangji) Economic Principle and Targeted Poverty Alleviation: Strategic Breakthrough of Non-economic Factors

The theoretical model of the “interphase (xiangji) economic principle” proposes that in economic activities, if the complex problems caused by cross-ethnic and cross-culture contact are ignored, the restrictive effects of various “non-economic factors” must follow. Therefore, in the practice of the “targeted poverty alleviation program”, it is necessary to fully understand and grasp the restrictive roles of “non-economic factors”, assess the situation, maintain rational behaviors of cultural traditions, and effectively avoid and overcome its unfavorable factors on economic activities. Therefore, the poverty alleviation action can achieve twice the result with half the effort

Research and Experiment on Optimal Separation Distance of Adjacent Buildings Based on Performance

Firstly, constitutive models of two unequal height adjacent ten-story and six-story reinforced concrete frame structures were established based on OpenSees software in which a series of separation distances are set for incremental dynamic analysis (IDA), respectively. Secondly, the seismic vulnerability curves by postprocessing programming in Matlab software were obtained based on IDA datum, and the optimal separation distances of adjacent buildings with and without connecting dampers were obtained by comparing the seismic vulnerability of adjacent building at different separation distance and the seismic hazard analysis. Finally, a scaled model shaking table test of adjacent steel frame structures was performed. The conclusions are obtained by comparing the measured results of the test with those obtained by the OpenSees analysis, such as acceleration and displacement. The conclusions show that when two adjacent buildings are not connected with a damper, the distance of adjacent structures is suggested to be 0.3 m under moderate and strong earthquakes and the distance of adjacent structures is suggested to be a specified value of 0.24 m under rare earthquakes. When two adjacent buildings are connected with dampers, the separation distance is suggested to be 0.1 m under various performance conditions

BCMA/CD47-directed universal CAR-T cells exhibit excellent antitumor activity in multiple myeloma

Abstract Background BCMA-directed autologous chimeric antigen receptor T (CAR-T) cells have shown excellent clinical efficacy in relapsed or refractory multiple myeloma (RRMM), however, the current preparation process for autologous CAR-T cells is complicated and costly. Moreover, the upregulation of CD47 expression has been observed in multiple myeloma, and anti-CD47 antibodies have shown remarkable results in clinical trials. Therefore, we focus on the development of BCMA/CD47-directed universal CAR-T (UCAR-T) cells to improve these limitations. Methods In this study, we employed phage display technology to screen nanobodies against BCMA and CD47 protein, and determined the characterization of nanobodies. Furthermore, we simultaneously disrupted the endogenous TRAC and B2M genes of T cells using CRISPR/Cas9 system to generate TCR and HLA double knock-out T cells, and developed BCMA/CD47-directed UCAR-T cells and detected the antitumor activity in vitro and in vivo. Results We obtained fourteen and one specific nanobodies against BCMA and CD47 protein from the immunized VHH library, respectively. BCMA/CD47-directed UCAR-T cells exhibited superior CAR expression (89.13-98.03%), and effectively killing primary human MM cells and MM cell lines. BCMA/CD47-directed UCAR-T cells demonstrated excellent antitumor activity against MM and prolonged the survival of tumor-engrafted NCG mice in vivo. Conclusions This work demonstrated that BCMA/CD47-directed UCAR-T cells exhibited potent antitumor activity against MM in vitro and in vivo, which provides a potential strategy for the development of a novel “off-the-shelf” cellular immunotherapies for the treatment of multiple myeloma. Graphic Abstrac

Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Image_1_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.png

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Molecular epidemiology and risk factors of Anaplasma spp., Babesia spp. and Theileria spp. infection in cattle in Chongqing, China.

Tick-borne pathogens (TBPs) seriously affect cattle production and can be economically damaging. The epidemiology of these organisms in the Chongqing municipality of China is not well described. This study aimed to investigate the prevalence and risk factors of TBPs including Anaplasma spp., Babesia spp. and Theileria spp. in cattle in Chongqing municipality. The results showed that 43.48% (150/345) of cattle were infected with at least one TBP, of which single infections were detected in 104 (30.14%), double infections in 34 cattle (9.86%) and triple infections in 12 (3.48%) of the cattle. The overall prevalence of Anaplasma spp., Theileria spp. and B. bigemina were 22.32%, 23.19% and 7.24%, respectively. Among these, the prevalence of A. bovis, A. central, A. phagocytophilum, A. platys, A. marginale, T. sinensisi and T. orientalis were 8.41%, 7.83%, 4.93%, 4.35%, 2.61%, 22.32% and 2.60%, respectively. We could not detect B. bovis, T. annulata, T. luwenshuni or T. uilenbergi in cattle. Cattle ≥1-year-old were more likely to be infected with Theileria spp. [adjusted odd ratio (AOR) = 2.70, 95% CI = 1.12-6.56)] compared with younger cattle, while cattle ≥1-year-old had reduced susceptibility to B. bigemina (AOR = 0.14, 95% CI = 0.03-0.60). Cattle living at higher altitude (≥500 m) were more susceptible to B. bigemina (AOR = 6.97, 95% CI = 2.08-23.35) and Theileria spp. infection (AOR = 1.87, 95% CI = 1.06-3.32). The prevalence of Theileria spp. on farms with cats was significantly higher than that without cats (AOR = 2.56, 95% CI = 1.12-5.88). Infection with A. bovis and A. central were significantly associated with A. phagocytophilum infection. Furthermore, there were significant associations between A. bovis and A. central infection, T. sinensisi and A. marginale infection, and B. bigemina and T. orientalis infection. This study provides new data on the prevalence of Anaplasma spp., Babesia spp. and Theileria spp. in cattle in Chongqing, and for the first time we reveal a possible relationship between the afore-mentioned pathogens, which will help in formulating appropriate control strategies for these pathogens in this area

Image_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.tif

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p