P-Wave Duration/Amplitude Ratio Quantifies Atrial Low-Voltage Area and Predicts Atrial Arrhythmia Recurrence After Pulmonary Vein Isolation.

BACKGROUND: Atrial low-voltage areas (LVAs) in patients with atrial fibrillation increase the risk of atrial arrhythmia (AA) recurrence after pulmonary vein isolation (PVI). Contemporary LVA prediction scores (DR-FLASH, APPLE) do not include P-wave metrics. We aimed to evaluate the utility of P-wave duration/amplitude ratio (PWR) in quantifying LVA and predicting AA recurrence after PVI. METHODS: In 65 patients undergoing first-time PVI, 12-lead ECGs were recorded during sinus rhythm. PWR was calculated as the ratio between the longest P-wave duration and P-wave amplitude in lead I. High-resolution biatrial voltage maps were collected and LVAs included bipolar electrogram amplitudes < 0.5 mV or < 1.0 mV. An LVA quantification model was created with the use of clinical variables and PWR, and then validated in a separate cohort of 24 patients. Seventy-eight patients were followed for 12 months to evaluate AA recurrence. RESULTS: PWR strongly correlated with left atrial (LA) (< 0.5 mV: r = 0.60; < 1.0 mV: r = 0.68; P < 0.001) and biatrial LVA (< 0.5 mV: r = 0.63; < 1.0 mV: r = 0.70; P < 0.001). Addition of PWR to clinical variables improved model quantification of LA LVA at the < 0.5 mV (adjusted R2 = 0.59 to 0.68) and < 1.0 mV (adjusted R2 = 0.59 to 0.74) cutoffs. In the validation cohort, PWR model-predicted LVA correlated strongly with measured LVA (< 0.5 mV: r = 0.78; < 1.0 mV: r = 0.81; P < 0.001). PWR model was superior to DR-FLASH (area under the receiver operating characteristic curve [AUC] 0.90 vs 0.78; P = 0.030) and APPLE (AUC 0.90 vs 0.67; P = 0.003) at detecting LA LVA and similar at predicting AA recurrence after PVI (AUC 0.67 vs 0.65 and 0.60). CONCLUSION: Our novel PWR model accurately quantifies LVA and predicts AA recurrence after PVI. PWR model-predicted LVA may help guide patient selection for PVI.

Study of Correlation between Intestinal Microbiota and Traditional Chinese Medicine Syndrome of Patients with Colon Cancer.

Objective: This research aims to study the material basis of the formation and specific bacteria of traditional Chinese medicine (TCM) syndrome from the characteristics of the intestinal microbiota of patients with colon cancer (CC) before and after the operation. Methods: A cross-sectional study was conducted on 84 patients with CC and 24 healthy controls. A total of 168 and 24 stool samples were collected from CC patients before and after the operation and healthy controls. DNA was extracted from 192 stool samples and then amplified using PCR. The V3-V4 high variable areas were analyzed by 16s rDNA sequencing. Results: The community diversity, in descending order, was the healthy control group and postoperative and preoperative groups of CC patients. The abundance of beneficial bacteria was postoperative group of CC patients > healthy control group > preoperative group of CC patients. Among the comparisons of the intestinal microbiota of preoperative groups of CC patients with different TCM syndromes, the community diversity in descending order was damp heat accumulation (DHA), spleen deficiency and dampness (SDD), spleen and kidney yang deficiency (SKYD), liver and kidney yin deficiency (LKYD), and deficiency of qi and blood (QBD), respectively. Specific microbiome analysis showed that the differences in the abundance of 42 taxons were statistically significant among the preoperative groups of CC patients with the five TCM syndromes and the healthy control group. While comparing the intestinal microbiota of postoperative groups with the five TCM syndromes, the community diversity in descending order is DHA, SDD, LKYD, SKYD, and QBD. Specific microbiome analysis showed that the differences in the abundance of 46 taxons were statistically significant among the postoperative groups of CC patients with the five TCM syndromes and the healthy control group. Streptococcus and Streptococcus mutans showed no statistical significance between the preoperative group and postoperative groups of CC with DHA syndrome (P > 0.05). Bacteroides at phylum and genus levels showed that there was no statistical significance between the preoperative group and the postoperative group of CC with SKYD syndrome (P > 0.05). Conclusions: Before and after surgery, with the deterioration of TCM syndrome: DHA ⟶ SDD ⟶ SKYD ⟶ LKYD ⟶ QBD, the number of beneficial bacteria in CC patients' intestines decreased while the number of pathogenic bacteria increased, and the community structure of intestinal microbiota tends to be unitized, indicating a serious intestinal microbiological disorder. After radical surgery and perioperative intervention, the intestinal microbiota diversity and community structure of postoperative CC patients were closer to those of healthy people than preoperative. However, they were still imbalanced. The intestinal microbiota of CC patients with different TCM syndromes differs significantly, which is important for understanding the pathogenesis of CC in TCM. The DHA and SKYD syndromes in CC patients before and after surgery showed significant differences in the microbial structure. Streptococcus and Streptococcus mutans were the specific species with a significant difference in CC patients with DHA syndrome, while bacteroides were the specific species in CC patients with SKYD syndrome.

Expression and clinical significance of periostin in oral lichen planus.

Oral lichen planus (OLP) is a chronic inflammatory lesion involving the oral mucosa, which has a high likelihood of progressing to cancer. The present study investigated the periostin expression in the mucosa and serum of patients with OLP and its correlation with serum cytokines. A total of 117 patients with OLP and 110 healthy controls were included in the study. The protein expression of periostin were measured in the OLP and normal oral mucosa by immunohistochemistry. ELISA was performed to measure the serum levels of periostin, tumour necrosis factor-α (TNF-α), interleukin (IL)-6, interferon-γ (IFN-γ), IL-4 and thymic stromal lymphopoietin (TSLP). Compared with the control group the OLP group had a significantly increased number of cases with high periostin expression in the oral mucosa and a significantly increased serum periostin level (P<0.05). Among all 117 OLP subjects, high periostin expression was associated with higher serum IL-6, TNF-α, TSLP and tissue mast cell density. High periostin expression was also significantly associated with a lower IFN-γ/IL-4 ratio (P<0.05). The present study concluded that periostin expression was increased in the oral mucosa and serum of patients with OLP and was associated with inflammatory response, T helper 2 cytokine-predominant immune imbalance, increased mast cell count and TSLP. The modulation of periostin may represent an attractive novel therapeutic target.