Coronavirus disease 2019 (COVID-19) is a multi-system disease that can lead to various severe complications. Acute limb ischemia (ALI) has been increasingly recognized as a COVID-19-associated complication that often predicts a poor prognosis. However, the pathophysiology and molecular mechanisms underlying COVID-19-associated ALI remain poorly understood. Hypercoagulability and thrombosis are considered important mechanisms, but we also emphasize the roles of vasospasm, hypoxia, and acidosis in the pathogenesis of the disease. The angiotensin-converting enzyme 2 (ACE2) pathway, inflammation, and platelet activation may be important molecular mechanisms underlying these pathological changes induced by COVID-19. Furthermore, we discuss the hypotheses of risk factors for COVID-19-associated ALI from genetic, age, and gender perspectives based on our analysis of molecular mechanisms. Additionally, we summarize therapeutic approaches such as use of the interleukin-6 (IL-6) blocker tocilizumab, calcium channel blockers, and angiotensin-converting enzyme inhibitors, providing insights for the future treatment of coronavirus-associated limb ischemic diseases.
Humans ; COVID-19/physiopathology* ; Ischemia/etiology* ; SARS-CoV-2 ; Extremities/blood supply* ; Risk Factors ; Interleukin-6/antagonists & inhibitors* ; Acute Disease ; Angiotensin-Converting Enzyme 2

Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.

Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.

Objective:To evaluate the characteristics of vaginal flora between normal and abnormal pregnant women throughout pregnancy.Methods:Vaginal swab specimens were collected from pregnant women in the first (<14 gestation weeks, GW), second (14~28 GW) and third trimester (>28 GW) in Anqing, Anhui Province from February 2018 to February 2020. Pregnant women were divided into normal and abnormal groups according to all clinical diagnosis. The sequences of 16S rRNA gene (V3-V4) from vaginal swabs were analyzed using QIIME2 platform. The differences in the dominance of Lactobacillus, community state type (CST) transition, Alpha diversity and Beta diversity were analyzed. Diversity data after log transition were used in the analysis of linear mixed model. Results:A total of 34 pregnant women (10 normal and 24 abnormal) with 102 samples were included for analysis. The composition of vaginal flora between two groups: the relative abundance of Lactobacillus was the highest at the genus level and Lactobacillus crispatus and Lactobacillus iners was the top two species with high relative abundance. The dominance of Lactobacillus, Alpha diversity and transition of CST were also similar. Both groups had a gradually decreased trend of Alpha diversity with GW, and the Chao1, Observed species and Faith′s PD indexes′ were different in different GW ( P<0.05). All Beta diversity metrics in normal group had descending trend, with lower value of the index of first distance which implied a higher microbiota stability, while Bray-Curtis, Weighted UniFrac distance had ascending trend in abnormal group, indicating lower stability. Jaccard distance′s first distance was statistically differed among GW and Unweighted UniFrac distance′s differed between normal and abnormal groups. Conclusions:The first distance of Unweighte UniFrac distance in abnormal pregnant women is higher than that of normal pregnant women and the vaginal flora in abnormal group has lower stability.

Laser tweezers Raman spectroscopy (LTRS) system is a combination of spectroscopy and laser tweezers; It is a new method of studying cells; It can trap single living cell and make Raman spectrum of single living cell. From the positions, intensities, and line widths of the Raman peaks in the spectra, we can get useful information about composition, structure and interactions of complexes inside the living cells. External agents may change cell's physiological state and this changed information can also be got from Raman spectra. This article is a study of Raman spectra of human red blood cell (RBC) affected by different intensity direct current (DC); from the result, distinct change of Raman spectra of RBC have been got. These changes characterize the changes of the internal information of the cells. This article give some academic reference of physical therapy using DC in the level of molecule.
Electricity ; Erythrocytes ; cytology ; physiology ; Humans ; Lasers ; Optical Tweezers ; Spectrum Analysis, Raman ; methods