Study limitations include referral population of acne patients and the restriction of outcome measures to facial acne.

Relative to laparoscopic sigmoid colovaginoplasty, laparoscopic peritoneal vaginoplasty provides good anatomic and functional results and excellent patient satisfaction.

To explore the technique and clinical value of transvaginal natural orifice transluminal endoscopic surgery (vNOTES) in hysterectomy and sentinel lymph node (SLN) mapping for endometrial cancer by comparing its perioperative outcomes with those of laparoscopic staging. Design: Retrospective cohort study. Setting: Department of gynecology at a tertiary medical center. Patients: All women diagnosed with endometrial cancer who underwent minimally invasive surgery at our center between August 2017 and May 2020. Interventions: Both vNOTES and laparoscopic approaches were used for hysterectomy and SLN mapping. The success of SLN detection as well as perioperative outcomes were subsequently analyzed. Measurements and Main Results: This study included 74 patients; 23 patients underwent vNOTES surgery, whereas 51 underwent standard laparoscopic surgery. The total successful SLN detection was 95.7% in the vNOTES group and 92.2% in the laparoscopy group (p >.05), whereas the bilateral success rates were 87.0% and 90.2%, respectively. No difference in SLN detection was observed between the 2 groups in terms of the side-specific mapping efficacy quotient (91.3% vs 91.2%, p = .47). The number of harvested SLNs, operative time, estimated blood loss, and intraoperative complications in the 2 groups were similar. One (4.3%) postoperative complication occurred in the vNOTES group vs 4 (7.9%) in the laparoscopy group (p = .029), and the median postoperative hospital stay was 3 days vs 4 days (p = .003). Conclusion:This study suggests that the vNOTES procedure is feasible, with a potentially decreased postoperative hospital stay, faster recovery, and better cosmetic results. However, prospective research is needed to validate its broader clinical application.

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Background: Neo-tetraploid rice (NTR) is a new tetraploid rice germplasm that developed from the crossing and directional selection of different autotetraploid rice lines, which showed high fertility and promising yield potential. However, systematic yield assessment, genome composition and functional variations associated with fertility and yield remain elusive. Results: Two season's field trials of 15 NTRs and 27 autotetraploid rice (ATR) lines revealed that the improvement of YPP (yield per plant, 4.45 g increase) were significantly associated with the increase of SS (seed setting, 29.44% increase), and yield and seed setting of NTRs improved significantly compared to parental lines. Whole genome resequencing of 13 NTR sister lines and their parents at about 48.63 depth were conducted and genome compositions were illustrated using inherited chromosomal blocks. Interestingly, 222 non-parental genes were detected between NTRs and their low fertility parental lines, which were conserved in 13 NTRs. These genes were overlapped with yield and fertility QTLs, and RNA-Seq analysis revealed that 81 of them were enriched in reproductive tissues. CRISPR/Cas9 gene knockout was conducted for 9 non-parental genes to validate their function. Knockout mutants showed on an average 25.63% and 4.88 g decrease in SS and YPP, respectively. Notably, some mutants showed interesting phenotypes, e.g., kin7l (kinesin motor gene) and kin14m (kinesin motor gene), bzr3 (BES1/BZR1 homolog) and nrfg4 (neo-tetraploid rice fertility related gene) exhibited 44.65%, 24.30%, 24.42% and 28.33% decrease in SS and 8.81 g, 4.71 g, 5.90 g, 6.22 g reduction in YPP, respectively. Conclusion: Comparative genomics provides insights into genome composition of neo-tetraploid rice and the genes associated with fertility and yield will play important role to reveal molecular mechanisms for the improvement of tetraploid rice.

Background Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. Methods Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. Results The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. Conclusions We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.