By 22 June 2022, 521 cases of monkeypox were notified in Germany. The median age was 38 years (IQR: 32–44); all cases were men. In Berlin, where 69% of all cases occurred, almost all were men who have sex with men. Monkeypox virus likely circulated unrecognised in Berlin before early May. Since mid-May, we observed a shift from travel-associated infections to mainly autochthonous transmission that predominantly took place in Berlin, often in association with visits to clubs and parties.

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.

Prevalence and age distribution of tumors is largely unknown in pet rabbits. Currently available studies focused on specific organ systems or specific tumor types and never covered a comparative examination of all tumor types. Previous studies on laboratory rabbits suggested a low tumor prevalence but were mostly limited to young adult animals. In the present study, all tumor types and several tumor-like lesions of all organ systems were analyzed retrospectively in archived pet rabbit samples of all ages. Cases included necropsy cases (n = 2,014) or postmortem tissue samples (n = 102) as well as surgical biopsies (n = 854). All lesions suspicious of neoplasia were reevaluated by histopathology and, when indicated, by immunohistochemistry. Necropsy cases had a tumor prevalence of 14.4% in both sexes or 19.8% in female intact rabbits of all age groups, and up to 47.2% or 66.7%, respectively, in rabbits older than 6 years. Overall, the most common tumor types were uterine adenocarcinoma (prevalence in female intact animals: 13.1%), lymphoma (prevalence: 2.8%), and thymoma (prevalence: 2.1%). Lymphoma, the most common tumor of rabbits ≤24 months of age, were of B-cell immunophenotype in 96% of cases and most commonly located in the lymph nodes (57%), gastrointestinal tract (54%), kidneys (48%), spleen (42%), and liver (41%). Tumors accounted for 81.1% of surgical biopsies and mostly comprised cutaneous, mammary, and uterine tumors. In conclusion, tumor types and prevalence varied significantly with respect to age, revealing some differences from previous studies on laboratory rabbits.

Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.