Forest plot for TNF- by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Bias risk assessment of included studies. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for hs-CRP. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for IL-6. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

The incidence rate of adverse reactions. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

The PRISMA study flowchart. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for FEV1/FVC. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for clinical efficacy. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for FEV1. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

The excluded and included studies were listed. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for PaCO₂. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Forest plot for PaO₂. by Xuqin Du (8803772)

“…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO₂ (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO₂ (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”

Enteropathogen detection and fCal levels. by Muhammad Rehan (406524)

Flavonoid biosynthesis pathway and the key genes along it. by Gustavo Roberto Fonseca de Oliveira (10739500)

Land use intensity classes standard. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Land use transfer matrix 1990-2020 (km²). by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Study area habitat quality LISA clustering map. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Spato-temporal changes in land use types. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Pattern indices of landscape levels. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Type level landscape index changes in 1990-2020. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”