Moticeasyscan

Liver tissues from human liver cancer patients, Tg^TM4SF5 FVB/N mice, or C57BL/6 (WT or Tm4sf5^−/− KO) mice treated with or without DEN and in the absence or presence of TSAHC were processed for immunohistochemistry. Liver sections were fixed with 3.7% formaldehyde and embedded in paraffin. The fixed liver sections were deparaffinized and rehydrated. Antigen retrieval was performed with heat-induced epitope retrieval (HIER) using sodium citrate buffer (pH 6.0). Quenching and blocking were performed using 3% H₂O₂ in distilled water and 1% normal goat serum in phosphate-buffered saline (PBS). Antigens were stained using the avidin–biotin complex (ABC) method (VECTASTAIN Elite ABC HRP Kit, Vector) and were detected using 3,3′-diaminobenzidine (DAB) stain (Vector). Antibodies against TM4SF5 [17 (link)], collagen I (Acris Antibodies), pY⁷⁰⁵STAT3 (Cell Signaling Technology), normal rabbit or mouse IgG, α-SMA (Sigma-Aldrich), α-fetoprotein (AFP), CD34, Ki67, laminins (Abcam), α-l-fucosidase [FUCA (AFU)], and laminin γ2 (Santa Cruz Biotechnology) were used for immunostaining. Ten random images per slide were saved using a digital slide scanner (MoticEasyScan, Motic, British Columbia, Canada). The tissues were also processed with Masson’s trichrome as well as hematoxylin and eosin stains, as previously described [27 (link)].

We selected two representative autopsy cases of pulmonary TB as training data to develop AI-assisted detection of AFB from tissues. In these autopsy cases, we used one typical section where numerous AFBs were detected by Ziehl–Neelsen staining. Additionally, we randomly selected 40 cases who underwent biopsy and had no AFB on Ziehl–Neelsen staining to train other histopathological specimens: eight surgical lung biopsies, 20 TBLBs, six transbronchial mediastinal lymph node needle biopsies, and six bone marrow clots or bone marrow biopsies. Subsequently, 14 consecutive cases that were not used as training data were selected as validation data; these patients underwent bronchoscopy to diagnose mycobacteria. All TBLBs were performed with 2.0-mm forceps, and the number of biopsies was 3–5. We scanned the Ziehl–Neelsen stained tissues at 400× magnification using Motic EasyScan (Motic, Hong Kong, China), and processed them into WSI.
Additionally, patient information, interferon-gamma releasing assay (IGRA) results, bacteriological test results, clinical course, and mycobacteriosis onset were collected from the medical records of the 14 patients used for AI validation.

Thirty-six representative samples from C1 (N = 18) and C2 (N = 18) were selected for tissue microarray (TMA) construction, the TMA is prepared as previously described⁴³ . For immunohistochemistry staining, the sections were stained using anti-Vimentin antibody (Proteintech, 1:2000), anti-Ki67 antibody (Proteintech, 1:5000), anti-Cyclin D antibody (Proteintech, 1:1500), anti- E-cadherin antibody (Proteintech, 1:2000), anti- N-cadherin antibody (Proteintech, 1:200), anti-ADH1A antibody (Abcam, 1:500), anti-CYP3A4 antibody (Proteintech, 1:200), anti-CD34 antibody (Proteintech, 1:1000), anti-VEGFA antibody (Proteintech, 1:200), anti-CD8-alpha antibody (Abcam, 1:500), anti-CD163 antibody (Proteintech, 1:1000). Images were captured by MoticEasyScan (Motic).

Paraffin-embedded tissue samples were sectioned at a 6μm thickness and collected in 50μm increments across the defect site. Collected tissue sections were mounted on silane treated glass slides and subjected to Masson’s trichrome staining. Collagen at the defect site for each section was imaged (Motic Easy Scan version 1.0.5.134) and quantified via ImageJ, and the percent area of collagen deposition from each tissue section was averaged for each animal sample.

In response to increasing demands of timely and reliable histological diagnosis of malignant diseases, KCMC acquired a whole slide image scanning (WSI) equipment which was installed in November 2019. The system (Motic EasyScan, USA), is a website based tele-consultation platform (www.med3.motic.com), developed for capacity building on cancer diagnosis and management in resources limited setting (Figs 1 and 2). After successful installation and training of the KCMC Pathology Department staff, consultant and sub-specialized pathologists were registered and linked in the platform. Continuous communication between KCMC pathologists, Information Technology (IT) personnel and consultant telepathologists was made electronically to address any raised issues with regard to the quality of the images and troubleshooting challenges that hindered the smooth system workflow of the platform.