Three-dimensional measurement of radiographic bone–implant contact lengths of zygomatic implants in zygomatic bone: a retrospective study of 66 implants in 28 patients

Zygomatic implant treatment is widely applied for severe maxillary atrophy to help rehabilitate the maxillary dentition. This retrospective study was performed to evaluate the actual radiographic bone–implant contact (rBIC) lengths of zygomatic implants. The records of 28 patients who underwent zygomatic implant surgery and subsequent follow-up examinations between August 2013 and September 2018 in the Department of Oral and Maxillofacial Surgery, Taipei Tzu Chi Hospital were reviewed. The surgeries were performed by a single surgeon using the same treatment protocol. All patients had a computed tomography scan at 1 year after the surgery. Using three-dimensional imaging software, an investigator measured the rBIC lengths of 66 implants and documented their clinical status. The implant survival rate was 100%. The mean rBIC length was significantly longer in male patients than in female patients (20.80 ± 5.88 mm versus 17.79 ± 6.34 mm; P = 0.028). The mean rBIC length of double zygomatic implants was significantly longer when compared to that of single implants (21.11 ± 6.23 mm versus 17.75 ± 5.85 mm; P = 0.027). This article is novel in reporting the exact rBIC lengths of zygomatic implants in a clinical setting. The results showed that zygomatic implants are a viable treatment modality for full-mouth rehabilitation.     

Serological Responses of Raccoons and Striped Skunks to Ontario Rabies Vaccine Bait in West Virginia during 2012–2016

Since the 1990s, oral rabies vaccination (ORV) has been used successfully to halt the westward spread of the raccoon rabies virus (RV) variant from the eastern continental USA. Elimination of raccoon RV from the eastern USA has proven challenging across targeted raccoon (Procyon lotor) and striped skunk (Mephitis mephitis) populations impacted by raccoon RV. Field trial evaluations of the Ontario Rabies Vaccine Bait (ONRAB) were initiated to expand ORV products available to meet the rabies management goal of raccoon RV elimination. This study describes the continuation of a 2011 trial in West Virginia. Our objective was to evaluate raccoon and skunk response to ORV occurring in West Virginia for an additional two years (2012–2013) at 75 baits/km² followed by three years (2014–2016) of evaluation at 300 baits/km². We measured the change in rabies virus-neutralizing antibody (RVNA) seroprevalence in targeted wildlife populations by comparing levels pre- and post-ORV during each year of study. The increase in bait density from 75/km² to 300/km² corresponded to an increase in average post-ORV seroprevalence for raccoon and skunk populations. Raccoon population RVNA levels increased from 53% (300/565, 95% CI: 50–57%) to 82.0% (596/727, 95% CI: 79–85%) during this study, and skunk population RVNA levels increased from 11% (8/72, 95% CI: 6–20%) to 39% (51/130, 95% CI: 31–48%). The RVNA seroprevalence pre-ORV demonstrated an increasing trend across study years for both bait densities and species, indicating that multiple years of ORV may be necessary to achieve and maintain RVNA seroprevalence in target wildlife populations for the control and elimination of raccoon RV in the eastern USA.     

生化汤加味促进剖宫产后子宫复旧随机对照试验的系统评价及Meta分析＊

目的 对生化汤加味促进剖宫产后子宫复旧的有效性与安全性进行系统评价，为其在临床应用提供循证医学证据。方法 检索中国生物医学文献数据库（CBM）、中国知网（CNKI）、万方资源数据库（WanFang）、维普期刊数据库（VIP）、Cochrane Library、PubMed、Web of Science等，检索时限为建库至2020年7月，选择生化汤加味促进剖宫产后子宫复旧的随机对照试验（RCT），采用Cochrane系统评价员手册提供的偏倚风险评估工具进行文献质量评价，应用RevMan5.3软件进行Meta分析。结果 共纳入12篇RCT文献，共计1804例剖宫产产妇；Meta分析结果显示，生化汤加味联合缩宫素组在产后第1、3、5天子宫底高度［MD = -0.75，95%CI（-1.35， -0.15），P < 0.00001；MD = -1.92，95%CI（-3.13， -0.72），P < 0.00001；MD = -1.92，95%CI（-3.79， -0.06），P < 0.00001］低于单用缩宫素组，联合用药组血性恶露时间［MD = -1.52，95%CI（-2.71，-0.34），P < 0.00001］和产后恶露持续时间［MD = -4.47，95%CI（-6.20，-2.75），P < 0.00001］均短于单用缩宫素组。结论 生化汤加味联合缩宫素与单用缩宫素比较，更能促进产后子宫复旧；不良反应少，报道的仅有4例出现轻微腹泻，安全性较高；但由于纳入文献质量较低，上述结论尚需更多的随机对照临床研究加以证实。     

Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE:Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS:We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS:The random forest methodology estimated cellular density with R² = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R² = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS:Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Increased cellular density (CD) is a hallmark of cancer and a key feature in histologic glioma analysis.¹ Mapping cellular density throughout a tumor would be a valuable tool to probe how tumors infiltrate and analyze the transition between diseased and healthy brain. However, measuring CD requires tissue, which entails additional risks and is expensive to obtain. There is no currently accepted clinical algorithm to translate imaging data into quantitative assessments of CD.There is great need for a method to estimate CD noninvasively in human patients with gliomas. In this article, we describe the development of such a method using MR imaging data inputs by correlating with multiple biopsy specimens acquired during a prospective human clinical trial. We obtained comprehensive MR imaging, including conventional, diffusion, perfusion, and permeability imaging sequences. We used machine learning approaches to correlate imaging findings with CD measurements from pathology, devised an algorithm to estimate CD from MR imaging inputs, and generated CD maps for the visual display of the predictions. We identified the most informative imaging data subset. This work has multiple applications in the diagnosis and treatment of patients with gliomas: For example, the method can be used to guide biopsy, resection, and surgery and delineate tumor borderzones both pre- and postoperatively.²