Microbiota in adult perianal abscess revealed by metagenomic next-generation sequencing.

The microbiota of perianal abscesses is scarcely investigated. Identifying causative bacteria is essential to develop antibiotic therapy. However, culture-based methods and molecular diagnostics through 16S PCR technology are often hampered by the polymicrobial nature of perianal abscesses. We sought to characterize the microbiota composition of perianal abscesses via metagenomic next-generation sequencing (mNGS). Fourteen patients suffering from perianal abscesses between March 2023 and August 2023 underwent retrospective assessment. Information from medical records was used, including clinical information, laboratory data, and culture and mNGS results. Forty bacterial taxa were identified from perianal abscesses through mNGS, with Bilophila wadsworthia (71.4%), Bacteroides fragilis (57.1%), and Escherichia coli (50.0%) representing the most prevalent species. mNGS identified an increased number of bacterial taxa, with an average of 6.1 compared to a traditional culture-based method which only detected an average of 1.1 in culture-positive perianal abscess patients, predominantly E. coli (75.0%), revealing the polymicrobial nature of perianal abscesses. Our study demonstrates that a more diverse bacterial profile is detected by mNGS in perianal abscesses, and that Bilophila wadsworthia is the most prevalent microorganism, potentially serving as a potential biomarker for perianal abscess.IMPORTANCEAccurately, identifying the bacteria causing perianal abscesses is crucial for effective antibiotic therapy. However, traditional culture-based methods and 16S PCR technology often struggle with the polymicrobial nature of these abscesses. This study employed metagenomic next-generation sequencing (mNGS) to comprehensively analyze the microbiota composition. Results revealed 40 bacterial taxa, with Bilophila wadsworthia (71.4%), Bacteroides fragilis (57.1%), and Escherichia coli (50.0%) being the most prevalent species. Compared to the culture-based approach, mNGS detected a significantly higher number of bacterial taxa (average 6.1 vs 1.1), highlighting the complex nature of perianal abscesses. Notably, Bilophila wadsworthia emerged as a potential biomarker for these abscesses. This research emphasizes the importance of mNGS in understanding perianal abscesses and suggests its potential for improving diagnostic accuracy and guiding targeted antibiotic therapy in the future.

The allometric relationship between carbon emission and economic development in Yangtze River Delta: fusion of multi-source remote sensing nighttime light data.

Exploring the allometric relationship between carbon emission and economic development can provide guidance for policy-makers who hope to accelerate carbon emission reduction and achieve high-quality development. First, based on the established DMSP/OLS and NPP/VIIRS nighttime light datasets, this study simulated the carbon emissions of the Yangtze River Delta from 2000 to 2020. Second, our research analyzed the spatiotemporal evolution characteristics of carbon emissions. Third, adopting allometric growth model, we explored the allometric relationship between economic development and carbon emissions in Yangtze River Delta. The main conclusions are as follows. First, four prediction models, namely, linear fitting, support vector machine, random forest, and CNN-BiLSTM deep learning, were compared to simulate the accuracy of carbon emissions. Consequently, the CNN-BiLSTM deep learning estimation model presented the best accuracy. Second, both the carbon emissions in YRD as a whole showed an increasing trend, with the largest growth rate appearing in Shanghai and the smallest growth rate occurring in Lishui. Moreover, the high-carbon emission areas were mainly distributed in the core city cluster, which are enclosed by Shanghai, Nanjing, and Hangzhou. Finally, the allometric relationship between economic development and carbon emissions was dominated by one-level negative during the sample period, and the relative growth rate of carbon emissions is lower than that of the economic development, which made the YRD at a basic coordinate stage of weak expansion of economy.

[Efficacy comparison of tissue selecting therapy stapler and procedure for prolapse and hemorrhoids in the treatment of severe hemorrhoids].

OBJECTIVE: To compare the efficacy and complication of tissue selecting therapy stapler (TST) and procedure for prolapse and hemorrhoids (PPH) in the treatment of severe hemorrhoids. METHODS: Clinical data of 542 cases of severe hemorrhoids undergoing TST (258 cases) or PPH (284 cases) in The First Affiliated Hospital of Fujian Medical University from November 2010 to January 2012 were analyzed retrospectively. Operative parameters, efficacy and complication 3 months after operation were assessed and compared. RESULTS: No significant difference in cure rate between TST and PPH (96.5% vs. 95.4%) was found, while the operation time and hospital stay after operation in TST group were significantly shorter urgency [(20.6±4.7) vs. (26.4±6.3) min, (2.9±0.5) vs. (3.5±0.7) d, both P<0.05]. Incidences of postoperative pain, bleeding, anal urgency and urinary retention in TST group were significantly lower than those in PPH group (all P<0.01). No anal stenosis was observed in TST group, and 5 cases developed anal stenosis in PPH group (P<0.05). Hemorrhoid recurrence did not differ significantly between the two groups. CONCLUSIONS: The efficacy of TST and PPH is comparable for severe hemorrhoids patients, while TST is associated with faster postoperative recovery and less complications.

[Comparison of two kinds of procedures used in the removal of horizontal impacted mandibular third molars].

OBJECTIVE: To compare the effect of lingual split bone technique and high speed turbine bodkin boneless technique in the removal of horizontal impacted mandibular third molars. METHODS: 150 horizontal impacted mandibular third molars were randomly divided into two groups, 75 teeth in each group. One group received lingual split bone technique, while the other group received high speed turbine bodkin boneless technique. Both the operation time and operation complication were compared. RESULTS: The operating time with lingual split bone technique and high speed turbine bodkin boneless technique were (35.85 +/- 6.05) min and (43.52 +/- 7.70) min (P < 0.05), respectively. There was significant difference of intraoperative fracture lingual film removal and facial swelling between lingual split bone technique and high speed turbine bodkin boneless technique (P < 0.05). While there was no significant difference of gingival laceration, postoperative pain, restriction of mouth opening and postoperative bleed between lingual split bone technique and high speed turbine bodkin boneless technique (P > 0.05). There was no lingual nerve injury or numbness of lower lip occurred in two kinds of procedures. Dry socket occurred in a case of high speed turbine bodkin boneless technique. CONCLUSION: Lingual split bone technique is better in the removal of horizontal impacted mandibular third molars. It could shorten operation time and reduce the intraoperative and postoperative complications.

A Sparsification Approach for Temporal Graphical Model Decomposition.

Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct temporal causal graphical models. However, how to best understand and conceptualize these complicated causal relationships is still an open problem. In this paper, we propose a decomposition approach to simplify the temporal graphical model. Our method clusters time series variables into groups such that strong interactions appear among the variables within each group and weak (or no) interactions exist for cross-group variable pairs. Specifically, we formulate the clustering problem for temporal graphical models as a regression-coefficient sparsification problem and define an interesting objective function which balances the model prediction power and its cluster structure. We introduce an iterative optimization approach utilizing the Quasi-Newton method and generalized ridge regression to minimize the objective function and to produce a clustered temporal graphical model. We also present a novel optimization procedure utilizing a graph theoretical tool based on the maximum weight independent set problem to speed up the Quasi-Newton method for a large number of variables. Finally, our detailed experimental study on both synthetic and real datasets demonstrates the effectiveness of our methods.