diff --git a/docs/images/hovernet_diagram.png b/docs/images/hovernet_diagram.png
new file mode 100644
index 0000000000..aa7adcbdcf
Binary files /dev/null and b/docs/images/hovernet_diagram.png differ
diff --git a/docs/source/whatsnew_1_1.md b/docs/source/whatsnew_1_1.md
index 2c16a417ad..261af460fc 100644
--- a/docs/source/whatsnew_1_1.md
+++ b/docs/source/whatsnew_1_1.md
@@ -8,6 +8,8 @@
 
 ## Digital pathology workflows
 
+![hovernet](../images/hovernet_diagram.png)
+
 Hover-Net is a model for simultaneous segmentation and classification of nuclei in multi-tissue histology images (Graham et al. Medical Image Analysis, 2019).
 We have added support for this model in MONAI by implementing several new components, enhancing existing ones and providing pipelines and examples for training, validation and inference.
 
@@ -22,6 +24,8 @@ and [inference](https://github.com/Project-MONAI/tutorials/blob/main/pathology/n
 
 ## Experiment management for MONAI bundle
 
+![exp_mgmt](../images/exp_mgmt.png)
+
 In this release, experiment management features are integrated with MONAI bundle.
 It provides essential APIs for managing the end-to-end model bundle lifecycle.
 Users can start tracking experiments by, for example, appending `--tracking "mlflow"` to the training or inference commands to enable the MLFlow-based management.
@@ -38,6 +42,8 @@ CT and PET images of various resolutions and sizes. A tutorial example of
 running Auto3DSeg on the HECKTOR22 challenge dataset is available in MONAI
 Tutorials. The tutorial is based on [the HECKTOR22 challenge 1st place solution](https://arxiv.org/abs/2209.10809).
 - A new improved version of `Segresnet` Algo is now available in `AutoRunner`.
+In this version, data caching is more efficient and the preprocessing transforms are more flexible.
+The workflow progresses including the timings of steps are written to console output as well as a YAML file.
 - Automatic customization and optimization of the model training configuration
 can be achieved according to the GPU devices used. The feature
 focuses on determining parameters including batch size of model
@@ -52,8 +58,10 @@ Notably,
 - The `mednist_reg` model demonstrates how to build image registration workflows in MONAI bundle
 format. The model uses a ResNet and spatial transformer for hand X-ray image registration based on
 [the registration_mednist tutorial](https://github.com/Project-MONAI/tutorials/blob/main/2d_registration/registration_mednist.ipynb),
-- `pathology_nuclei_segmentation_and_classification`, `pathology_nuclick_annotation`, and
-`pathology_nuclei_classification` bundles are built for digital pathology image analysis.
+- `pathology_nuclei_segmentation_and_classification`,
+  `pathology_nuclick_annotation`, and `pathology_nuclei_classification` bundles
+  are built for [digital pathology image
+  analysis](https://github.com/Project-MONAI/model-zoo/tree/dev/models/pathology_nuclei_segmentation_classification).
 
 For more details about how to use the models, please see [the tutorials](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo).
 
diff --git a/monai/bundle/scripts.py b/monai/bundle/scripts.py
index dc1fc2f2d7..f81905a961 100644
--- a/monai/bundle/scripts.py
+++ b/monai/bundle/scripts.py
@@ -166,7 +166,7 @@ def _download_from_ngc(download_path: Path, filename: str, version: str, remove_
     url = _get_ngc_bundle_url(model_name=filename, version=version)
     filepath = download_path / f"{filename}_v{version}.zip"
     if remove_prefix:
-        filename = _remove_ngc_prefix(filename)
+        filename = _remove_ngc_prefix(filename, prefix=remove_prefix)
     extract_path = download_path / f"{filename}"
     download_url(url=url, filepath=filepath, hash_val=None, progress=progress)
     extractall(filepath=filepath, output_dir=extract_path, has_base=True)