ObjectiveAcupuncture is emerging as a potential therapy for relieving pain, but the effectiveness of acupuncture for relieving low back and/or pelvic pain (LBPP) during the pregnancy remains controversial. This meta-analysis aims to investigate the effects of acupuncture on pain, functional status and quality of life for women with LBPP pain during the pregnancy.DesignSystematic review and meta-analysis.Data sourcesThe PubMed, EMBASE databases, Web of Science and Cochrane Library were searched for relevant randomised controlled trials (RCTs) from inception to 15 January 2022.Eligibility criteria for selecting studiesRCTs evaluating the effects of acupuncture on LBPP during the pregnancy were included.Data extraction and synthesisThe data extraction and study quality assessment were independently performed by three reviewers. The mean differences (MDs) with 95% CIs for pooled data were calculated. We assessed the confidence in the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework.Main outcomes and measuresThe primary outcomes were pain, functional status and quality of life. The secondary outcomes were overall effects (a questionnaire at a post-treatment visit within a week after the last treatment to determine the number of people who received good or excellent help), analgesic consumption, Apgar scores >7 at 5 min, adverse events, gestational age at birth, induction of labour and mode of birth.ResultsThis meta-analysis included 10 studies, reporting on a total of 1040 women. Overall, acupuncture significantly relieved pain during pregnancy (MD=1.70, 95% CI: (0.95 to 2.45), p<0.00001, I2=90%) and improved functional status (MD=12.44, 95% CI: (3.32 to 21.55), p=0.007, I2=94%) and quality of life (MD=−8.89, 95% CI: (−11.90 to –5.88), p<0.00001, I2= 57%). There was a significant difference for overall effects (OR=0.13, 95% CI: (0.07 to 0.23), p<0.00001, I2= 7%). However, there was no significant difference for analgesic consumption during the study period (OR=2.49, 95% CI: (0.08 to 80.25), p=0.61, I2=61%) and Apgar scores of newborns (OR=1.02, 95% CI: (0.37 to 2.83), p=0.97, I2= 0%). Preterm birth from acupuncture during he study period was reported in two studies. Although preterm contractions were reported in two studies, all infants were in good health at birth. In terms of gestational age at birth, induction of labour and mode of birth, only one study reported the gestational age at birth (mean gestation 40 weeks). Thus, prospective randomised clinical studies or clinical follow-up studies were hence desirable to further evaluate these outcomes.ConclusionsAcupuncture significantly improved pain, functional status and quality of life in women with LBPP during the pregnancy. Additionally, acupuncture had no observable severe adverse influences on the newborns. More large-scale and well-designed RCTs are still needed to further confirm these results.PROSPERO registration numberCRD42021241771.
Background: Gastric cancer (GC) is the second leading cause of cancer-related mortality and the fifth most common cancer worldwide. However, the underlying mechanisms of competitive endogenous RNAs (ceRNAs) in GC are unclear. This study aimed to construct a ceRNA regulation network in correlation with prognosis and explore a prognostic model associated with GC.Methods: In this study, 1,040 cases of GC were obtained from TCGA and GEO datasets. To identify potential prognostic signature associated with GC, Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were employed. The prognostic value of the signature was validated in the GEO84437 training set, GEO84437 test set, GEO15459 set, and TCGA-STAD. Based on the public databases, TargetScan and starBase, an mRNA-miRNA-lncRNA regulatory network was constructed, and hub genes were identified using the CytoHubba plugin. Furthermore, the clinical outcomes, immune cell infiltration, genetic variants, methylation, and somatic copy number alteration (sCNA) associated with the ceRNA network were derived using bioinformatics methods.Results: A total of 234 prognostic genes were identified. GO and GSEA revealed that the biological pathways and modules related to immune response and fibroblasts were considerably enriched in GC. A nomogram was generated to provide accurate prognostic outcomes and individualized risk estimates, which were validated in the training, test dataset, and two independent validation datasets. Thereafter, an mRNA-miRNA-lncRNA regulatory network containing 4 mRNAs, 22 miRNAs, 201 lncRNAs was constructed. The KCNQ1OT1/hsa-miR-378a-3p/RBMS1 ceRNA network associated with the prognosis was obtained by hub gene analysis and correlation analysis. Importantly, we found that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may play a vital role in the diagnosis and prognosis of GC patients based on Cox regression analyses. Furthermore, our findings demonstrated that mutations and sCNA of the KCNQ1OT1/miR-378a-3p/RBMS1 axis were associated with increased immune infiltration, while the abnormal upregulation of the axis was primarily a result of hypomethylation.Conclusion: Our findings suggest that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may be a potential prognostic biomarker and therapeutic target for GC. Moreover, such findings provide insights into the molecular mechanisms of GC pathogenesis.
Background: Previous studies have demonstrated that Foot Posture Index (FPI-6) is a valid and moderately reliable tool to evaluate foot posture. However, data about reliability and validity of FPI-6 in the assessment of foot posture in people with low back pain (LBP) is lacking.Objectives: To investigate reliability and validity of FPI-6 in the assessment of foot posture in people with LBP.Methods: Thirty volunteers with LBP, aged 20 - 64 years, were recruited for the research and assessed by two raters. The data measured by different raters on the same day were used to calculate the inter-rater reliability. The data measured by the same rater on different dates were used to calculate the test-retest reliability. The reliability of FPI-6 was tested with intraclass correlation coefficient (ICC), and absolute reliability with standard error of measurement (SEM), minimal detectable change (MDC) and Bland–Altman analysis. The validity of FPI-6 was tested with Exploratory Factor Analysis (EFA) and Spearman's correlation coefficients. Results: The FPI-6 indicated excellent inter-rater and test-retest reliability in the evaluation of foot posture in people with LBP (ICC = 0.97 and 0.95). The agreement for inter-rater and test-retest was excellent based on the SEM (SEM = 0.12) and MDC value (MDC = 0.33). Bland–Altman plots showed that there was no significant systematic bias for the agreement on the ground of low mean difference (< 1). The EFA suggested that the fit indices were considered acceptable according to the Kaiser-Meyer-Olkin (KMO) value (KMO = 0.620) and Bartlett's sphericity test (P < 0.01). There was a statistically significant positive correlation between each item and total score of FPI-6 because the Spearman’s correlation coefficient of six items were all > 0.3 (P < 0.01).Conclusions: The inter-rater and test-retest reliability and validity of FPI-6 on people with LBP were proved reliable. It might be considered a reliable and valid adjunctive tool to detect possible changes of foot posture after interventions in patients with LBP.
Previous studies have demonstrated that Foot Posture Index (FPI-6) is a valid and moderately reliable tool to evaluate foot posture. However, data about reliability and validity of FPI-6 in the assessment of foot posture in people with low back pain (LBP) is lacking. To investigate reliability and validity of FPI-6 in the assessment of foot posture in people with LBP. Thirty volunteers with LBP, aged 20–64 years, were recruited for the research and assessed by two raters. The data measured by different raters on the same day were used to calculate the inter-rater reliability. The data measured by the same rater on different dates were used to calculate the test–retest reliability. The reliability of FPI-6 was tested with intraclass correlation coefficient (ICC), and absolute reliability with standard error of measurement (SEM), minimal detectable change (MDC) and Bland–Altman analysis. The validity of FPI-6 was tested with Exploratory Factor Analysis (EFA) and Spearman's correlation coefficients. The FPI-6 indicated excellent inter-rater and test–retest reliability in the evaluation of foot posture in people with LBP (ICC = 0.97 and 0.95). The agreement for inter-rater and test–retest was excellent based on the SEM (SEM = 0.12) and MDC value (MDC = 0.33). Bland–Altman plots showed that there was no significant systematic bias for the agreement on the ground of low mean difference (< 1). The EFA suggested that the fit indices were considered acceptable according to the Kaiser–Meyer–Olkin (KMO) value (KMO = 0.620) and Bartlett's sphericity test (P < 0.01). There was a statistically significant positive correlation between each item and total score of FPI-6 because the Spearman’s correlation coefficient of six items were all > 0.3 (P < 0.01). The inter-rater and test–retest reliability and validity of FPI-6 on people with LBP were proved reliable. It might be considered a reliable and valid adjunctive tool to detect possible changes of foot posture after interventions in patients with LBP.
The COVID-19 pandemic has resulted in nosocomial transmission of COVID-19 within hospitals and other healthcare settings such as residential homes and primary care settings. Here, we discuss how a 5G network can be used to reduce such infections.Nosocomial infections are those that are acquired in a healthcare setting and they have been commonly reported during the coronavirus disease 2019 (COVID-19) pandemic. For example, 48 COVID-19 cases, including 28 healthcare workers (HCWs), 13 patients, and 7 accompanying persons, were associated with a nosocomial outbreak in the pediatric dialysis unit of the University Hospital of Münster 1 . Although the percentage of the at-risk population who contract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; attack rate) is variable during a nosocomial outbreak, it can be as high as 60%, resulting in high mortality 2 . In addition to the impact on patients, HCWs have been reported to have at least a tenfold increased risk of being infected with SARS-CoV-2 compared with the general population 3 . Infection of HCWs and the associated mortality have detrimental effects on healthcare service as well as the morale of frontline staff 4 . There are also increased risks associated with spreading the virus to patients with comorbidities who are more vulnerable to the effects of the virus 5 .The Guangdong Second Provincial General Hospital (GD2H, Guangzhou, Guangdong Province, China) recently built the first 5G-powered smart hospital in China in conjunction with Huawei 6 . The 5G technology included in the hospital features a computer network able to process a very high volume of wireless data with minimal delay, resulting in a fast and efficient transfer of information. Several features of the technology have the potential to reduce the occurrence of nosocomial infections (Table 1). In this article, we highlight examples of how the 5G technology has enabled practices and behavior within the hospital to be modified to reduce the likelihood of nosocomial infections. Reduced in-person visitsDuring the pandemic, a large number of people have been to the hospital, resulting in significant logistical challenges for hospitals and healthcare professionals. GD2H uses artificial intelligence (AI) and 5G technology to provide disease diagnosis under the supervision of clinicians (AI doctors). The high bandwidth and wireless nature of 5G also enable high-definition real-time online consultations, psychological counseling, and other online services to be provided 7 . Electronic prescriptions can be issued and AI can also be used for epidemiological investigations, intelligent screening of COVID-19 symptoms, and pre-hospital triage. These AI systems use automated screening algorithms that were designed using a decision tree that classified patients according to their symptoms, travel history, and
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