Establishment of a novel microfluidic co-culture system for simultaneous analysis of multiple indicators of gefitinib sensitivity in colorectal cancer cells.

The therapeutic effect of gefitinib on colorectal cancer (CRC) is unclear, but it has been reported that stromal cells in the tumor microenvironment may have an impact on drug sensitivity. Herein, we established a microfluidic co-culture system and explored the sensitivity of CRC cells co-cultured with cancer-associated fibroblasts (CAFs) to gefitinib. The system consisted of a multichannel chip and a Petri dish. The chambers in the chip and dish were designed to continuously supply nutrients for long-term cell survival and create chemokine gradients for driving cell invasion without any external equipment. Using this system, the proliferation and invasiveness of cells were simultaneously evaluated by quantifying the area of cells and the migration distance of cells. In addition, the system combined with live cell workstation could evaluate the dynamic drug response of co-cultured cells and track individual cell trajectories in real-time. When CRC cells were co-cultured with CAFs, CAFs promoted CRC cell proliferation and invasion and reduced the sensitivity of cells to gefitinib through the exosomes secreted by CAFs. Furthermore, the cells that migrated out of the chip were collected, and EMT-related markers were determined by immunofluorescent and western blot assays. The results demonstrated that CAFs affected the response of CRC cells to gefitinib by inducing EMT, providing new ideas for further research on the resistance mechanism of gefitinib. This suggests that targeting CAFs or exosomes might be a new approach to enhance CRC sensitivity to gefitinib, and our system could be a novel platform for investigating the crosstalk between tumor cells and CAFs and understanding multiple biological changes of the tumor cells in the tumor microenvironment.

Photodynamic therapy for cutaneous inflammatory pseudotumour: A case report.

Inflammatory pseudotumour (IP) is a rare proliferative disease characterized by a dense infiltrate of plasma cells, lymphocytes, eosinophils and neutrophils in the fibrous stroma. It primarily affects the lungs of pediatric patients or young adults. Cutaneous IP is an extremely rare condition, with limited documentation in the English literature. In this case report, we presented an unusual instance of a 62-year-old male endured recalcitrant cutaneous IP for 8 years and exhibited poor response to topical glucocorticoid therapy, as well as intralesional injections of pingyangmycin and/or corticosteroid. Notably, after undergoing four sessions of 5-aminolevulinic acid photodynamic therapy (ALA-PDT), the patient experienced a significant reduction in erythema and nodules. This observation suggests that ALA-PDT may represent a promising and safe treatment option for cutaneous IP.

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning.

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists' confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

Identification of the target protein of the metastatic colorectal cancer-specific aptamer W3 as a biomarker by aptamer-based target cells sorting and functional characterization.

Metastasis is a leading cause of cancer-related deaths. Hence, the discovery of more reliable metastasis-related biomarkers is crucial to improve the survival rate of cancer patients. W3 is an aptamer previously produced by the subtractive cell-SELEX using metastatic colorectal cancer cells as target cells and non-metastatic cells as negative cells. In this study, we aimed to evaluate whether the target molecule of W3 can potentially act as a metastatic biomarker. First, we obtained two cell subpopulations with different expression levels of the target molecule by W3-based cell sorting. Subsequently, we demonstrated that W3high cells have a higher metastatic potential than W3low cells both in vitro and in vivo. Further, immunohistochemical analysis revealed that W3 target expression is positively associated with metastasis and poor prognosis of CRC patients. Using mass spectrometry (MS) combined with pull-down, we identified that Ephrin type-A receptor 2 (EphA2) is the target of W3. EphA2's potential as a metastatic predictor was demonstrated by capturing W3-positive circulating tumor cells from CRC patients using a W3 probe. Based on these results, we put forward a stratagem for cell-SELEX-based biomarker discovery: selecting an aptamer through subtractive cell-SELEX towards the phenotype of interest; evaluating the functional phenotype of the target molecule by aptamer-based target cell sorting and analysis of clinical samples; and identifying the aptamer's target molecule using MS and aptamer-based target enrichment. This stratagem not only shortens the time for the clinical application of aptamers but also enables a more targeted and efficient discovery of biomarkers.

Convolutional neural network assistance significantly improves dermatologists' diagnosis of cutaneous tumours using clinical images.

BACKGROUND: Convolutional neural networks (CNNs) have demonstrated expert-level performance in cutaneous tumour classification using clinical images, but most previous studies have focused on dermatologist-versus-CNN comparisons rather than their combination. The objective of our study was to evaluate the potential impact of CNN assistance on dermatologists for clinical image interpretation. METHODS: A multi-class CNN was trained and validated using a dataset of 25,773 clinical images comprising 10 categories of cutaneous tumours. The CNN's performance was tested on an independent dataset of 2107 images. A total of 400 images (40 per category) were randomly selected from the test dataset. A fully crossed, self-control, multi-reader multi-case (MRMC) study was conducted to compare the performance of 18 board-certified dermatologists (experience: 13/18 ≤ 10 years; 5/18＞10 years) in interpreting the 400 clinical images with or without CNN assistance. RESULTS: The CNN achieved an overall accuracy of 78.45% and kappa of 0.73 in the classification of 10 types of cutaneous tumours on 2107 images. CNN-assisted dermatologists achieved a higher accuracy (76.60% vs. 62.78%, P < 0.001) and kappa (0.74 vs. 0.59, P < 0.001) than unassisted dermatologists in interpreting the 400 clinical images. Dermatologists with less experience benefited more from CNN assistance. At the binary classification level (malignant or benign), the sensitivity (89.56% vs. 83.21%, P < 0.001) and specificity (87.90% vs. 80.92%, P < 0.001) of dermatologists with CNN assistance were also significantly improved than those without. CONCLUSIONS: CNN assistance improved dermatologist accuracy in interpreting cutaneous tumours and could further boost the acceptance of this new technique.

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

Response of Tonsil Follicular Dendritic Cell Sarcoma to Multimodal Treatment Including Pembrolizumab: A Case Report and Literature Review.

Follicular dendritic cell sarcoma (FDCS) is a rare malignant neoplasm that was classified by the World Health Organization (WHO) under histiocytic and dendritic cell neoplasms in the 2016 revision. Considering the rarity of this tumor, there is no standardized treatment. It is usually treated by complete surgical resection. Adjuvant chemotherapy and radiotherapy are alternative methods. Immune checkpoint inhibitors (ICIs) represented by the programmed death receptor 1/programmed death ligand 1 (PD-1/PD-L1) antibody have achieved significant clinical benefits in a variety of solid tumors. However, reports on the treatment of FDCS with ICIs are rare. FDCS often expresses high levels of PD-L1, which provides a rationale to use immunotherapy in cases of FDCS. Here, we present a 51-year-old Filipino-Chinese man with FDCS who was treated with multimodal treatment, including the PD-1 inhibitor pembrolizumab and achieved a relatively long disease-free survival of 24 months. This case emphasizes that the application of ICIs under the guidance of NGS technology seems to be a meaningful treatment option for patients with FDCS.

CD8+ mycosis fungoides with ichthyosiform clinical presentation and angiocentric feature.

Mycosis fungoides (MF) is characterized by epidermotropic atypical lymphocytes infiltrate with α/ß T-helper memory immunophenotype (ßF1+, CD3+, CD4+, CD45Ro+, and CD8-). Angiocentricity is always associated with aggressive behavior or poor outcome in primary or secondary cutaneous lymphomas. Rare cases of angiocentric MF with a T-cytotoxic immunophenotype (CD3+, CD4-, CD8+, TIA-1+) have been described. Here, we report a 27-year-old man diagnosed with MF, clinically presenting with ichthyosiform lesions on his trunk and limbs. Biopsy demonstrated a CD3+ and CD8+ atypical lymphocytic infiltrate with marked epidermotropism and angiocentricity. Awareness of this rare MF variant with unusual clinicopathological characteristics is important to avoid misdiagnosis.

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm.

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images.

BACKGROUND: Deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs). METHODS: The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists. RESULTS: The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI]: 92.0%-98.2%) and 96.0% (95% CI: 94.2%-97.8%), respectively. At the operating point of sensitivity of 95.1%, the algorithm revealed a comparable specificity with 7 dermatopathologists (97.3% vs. 96.0%, P = 0.11). At the operating point of specificity of 96.0%, the algorithm also achieved a comparable sensitivity with 7 dermatopathologists (96.5% vs. 95.1%, P = 0.30). A more transparent and interpretable diagnosis could be generated by highlighting the regions of interest recognized by the algorithm in WSIs. CONCLUSION: The performance of the deep learning algorithm was on par with that of 7 expert dermatopathologists in interpreting WSIs with melanocytic lesions. By pre-screening the suspicious melanoma regions, it might serve as a supplemental diagnostic tool to improve working efficiency of pathologists.

Cutaneous involvement as initial presentation of multicentric plasmacytic Castleman disease.

Castleman disease (CD) is an unusual lymphoproliferative disorder characterized by multiple lymphadenopathy accompanied by marked systemic inflammatory symptoms. CD can be unicentric (UCD) or multicentric (MCD), and it can be classified into three types based on histopathology: hyaline vascular type, plasma cell type, and mixed hyaline vascular and plasma cell type. CD involving skin is an unusual clinical manifestation. Abnormalities including rash, hyperpigmentation, cherry hemangiomatosis, paraneoplastic pemphigus, and Kaposi sarcoma have been reported to occur in MCD. Here, we reported an unusual case of MCD which presented initially with disseminated dark brown papules, patches, and plaques, and pathologically demonstrated plasma cell type CD, a finding which is rarely reported. The peculiar clinicopathological features will be discussed.

A systems biology approach for defining the potential molecular framework of idiopathic hypereosinophilic syndrome with cutaneous involvement.

Hypereosinophilic syndrome (HES) is a rare multisystem disease that predominantly includes skin with severe and persistent itching. A lack of understanding about the pathological condition and mechanism of dermatosis caused by HES hinders its treatment. In the present study, we applied a quantitative proteomics approach to characterize the cellular responses of skin tissue to idiopathic HES (IHES) at the proteome level. We identified hundreds of skin tissue proteins that were differentially expressed between IHES patients and healthy individuals. IHES patients display severely damaged microenvironment, including extracellular matrix (ECM) organization and disassembly, immune disorders, decreased metabolic capacity, and susceptibility to microbial infection. Moreover, there was abnormal proliferation of basal epidermal stem cells, which was closely related to high expression of the epigenetic regulator, histone deacetylase 2, providing mechanistic insight into the abnormal epidermal thickening of IHES skin tissues. Overall, our study provides a comprehensive framework for a system-level understanding of IHES-induced dermatosis (IHESiD) tissues at the protein and cell pathway levels. Our findings may facilitate a new approach to diagnosis and treatment to alleviate skin clinical symptoms, monitor the activity of IHES, and determine therapeutic effects.

Lymphomatoid papulosis type E with a CD56+ immunophenotype presenting with purpura-like lesions.

Lymphomatoid papulosis (LyP) type E is a recently described variant characterized by the occurrence of large necrotic eschar-like lesions displaying microscopically angioinvasive and angiodestructive infiltrates of CD30+ lymphocytes, frequently coexpressing CD8. Rare cases of LyP type E with a CD56+ immunophenotype have been described. Herein, we describe a 36-year-old woman with LyP type E, characterized by purpura-like lesions on her left ankle. Initially, she presented with left ankle swelling, petechiae and ecchymosis, and rapidly developing necrotic papules, all of which resolved spontaneously over a period of a few months without intentional therapy. Biopsy revealed CD30 and CD56 positive atypical cell infiltrates with marked angiocentricity and angiodestruction. Awareness of this rare LyP variant and its correct recognition, even if the clinical presentation is unusual, is important to avoid aggressive treatment.

Photodynamic Therapy for Genital Warts Causes Activation of Local Immunity.

BACKGROUND: 5-aminolevulinic acid photodynamic therapy (PDT) for genital warts is effective, safe, and can prevent recurrence. It is believed that PDT can induce immune responses, but the mechanism is not completely understood. OBJECTIVES: The objectives of this article are to confirm the effect of PDT for genital warts on local immunity and to investigate the recruitment and significance of immune cells in tissues. METHODS: Local immune changes in T lymphocytes (CD3+, CD4+, CD8+), plasmacytoid dendritic cells (pDCs) (CD123+), and myeloid dendritic cells (CD1a+) after PDT in patients were evaluated by immunohistochemistry staining. Changes in mRNA levels of IFN-γ, IFN-α, IFN-ß, interferon-stimulated gene 15 kDa (ISG-15), Mx2, Toll-like receptor 9 (TLR9), and interferon regulatory factor 7 (IRF7) were analyzed by real-time quantitative polymerase chain reaction. RESULTS: At 4 hours after PDT, CD4+ increased, accompanied by increased levels of mRNA expression of IFN-γ, but CD4+ and mRNA expression levels of IFN-γ were decreased at 24 hours after PDT. CD123+ pDCs showed an increasing trend. CD1a+ LCs in the epidermis gradually decreased, and DCs in the epidermis gradually increased. CD3+ infiltrated and migrated to the superficial dermis, but CD8+ did not change significantly after PDT. The mRNA expression levels of IFN-α, IFN-ß, ISG-15, Mx2, TLR9, and IRF7 showed an increasing trend after PDT. As compared with the patients without significantly increased IFN-α and IFN-ß after PDT sessions, patients with significant increases needed fewer sessions of PDT for remission. CONCLUSIONS: PDT for genital warts can activate T lymphocyte-mediated, DC-related, and pDC-related immunity. The clinical efficacy of PDT for genital warts may be related to the increased levels of IFN-α and IFN-ß after treatment.

Metformin inhibits pro-inflammatory responses via targeting nuclear factor-κB in HaCaT cells.

Psoriasis is a prevalent, chronic inflammatory skin disease that arises from rapid and excessive growth of keratinocytes induced by abnormal inflammatory responses. Metformin is the first-line drug in type 2 diabetes and has been proven to possess significant anti-inflammatory effects in various diseases. In the present study, we examined the role of metformin in nuclear factor kappa B (NF-κB)-mediated inflammatory responses in HaCaT cells, a cell line for the keratinocyte. Our results demonstrated that metformin significantly decreased the mRNA and protein levels of tumour necrosis factor-α (TNFα), interleukin (IL)-6, IL-8, and IL-1ß induced by TNFα. Immunofluorescence staining and western blot analysis showed that metformin inhibited the nuclear localization of p65, a subunit of nuclear factor NF-κB. In addition, metformin suppressed the transcription activity of NF-κB by inhibiting the degradation of IκBα. The inhibitory effect of metformin on NF-κB signalling is comparable with a specific IKKß inhibitor BI605906. Collectively, our data suggest that metformin may be a potential therapeutic agent in inflammatory skin diseases like psoriasis.

A Neuronal Hub Binding Sleep Initiation and Body Cooling in Response to a Warm External Stimulus.

Mammals, including humans, prepare for sleep by nesting and/or curling up, creating microclimates of skin warmth. To address whether external warmth induces sleep through defined circuitry, we used c-Fos-dependent activity tagging, which captures populations of activated cells and allows them to be reactivated to test their physiological role. External warming tagged two principal groups of neurons in the median preoptic (MnPO)/medial preoptic (MPO) hypothalamic area. GABA neurons located mainly in MPO produced non-rapid eye movement (NREM) sleep but no body temperature decrease. Nitrergic-glutamatergic neurons in MnPO-MPO induced both body cooling and NREM sleep. This circuitry explains how skin warming induces sleep and why the maximal rate of core body cooling positively correlates with sleep onset. Thus, the pathways that promote NREM sleep, reduced energy expenditure, and body cooling are inextricably linked, commanded by the same neurons. This implies that one function of NREM sleep is to lower brain temperature and/or conserve energy.

Lymphomatoid papulosis with folliculotropism, eccrinotropism and neurotropism.

The histopathological characteristics of lymphomatoid papulosis (LyP) vary. Currently, 6 subtypes have been reported, including a new subtype with perifollicular infiltration and different degrees of folliculotropism of CD30+ atypical lymphocytes, known as follicular LyP. However, LyP pathologically manifesting with folliculotropism, eccrinotropism and neurotropism has been rarely reported. We present a case of LyP showing CD30+ atypical lymphocytes around the hair follicle, eccrine gland and nerve fiber, with varying degrees of infiltrates. The pathological characteristics of folliculotropism and eccrinotropism are often associated with mycosis fungoides (MF). This case suggests that differential diagnosis is necessary when atypical lymphocytes infiltrate the follicle and eccrine gland. As folliculotropism and eccrinotropism can occur in both MF and LyP, it may represent a conceptual intersection between the 2 disease processes.

Correction to: A novel protein derived from lamprey supraneural body tissue with efficient cytocidal actions against tumor cells.

CORRECTION: Unfortunately, following publication of this article [1], it was noticed that the key in Figure 5c incorrectly showed '0 h', '5 h' and '10 h'. The corrected version, showing '0 h', '12 h' and '24 h', can be seen below and the original article has been updated to reflect this.

A novel protein derived from lamprey supraneural body tissue with efficient cytocidal actions against tumor cells.

BACKGROUND: In previous research, we found that cell secretion from the adult lamprey supraneural body tissues possesses cytocidal activity against tumor cells, but the protein with cytocidal activity was unidentified. METHODS: A novel lamprey immune protein (LIP) as defense molecule was first purified and identified in jawless vertebrates (cyclostomes) using hydroxyapatite column and Q Sepharose Fast Flow column. After LIP stimulation, morphological changes of tumor cells were analysed and measured whether in vivo or in vitro. RESULTS: LIP induces remarkable morphological changes in tumor cells, including cell blebbing, cytoskeletal alterations, mitochondrial fragmentation and endoplasmic reticulum vacuolation, and most of the cytoplasmic and organelle proteins are released following treatment with LIP. LIP evokes an elevation of intracellular calcium and inflammatory molecule levels. Our analysis of the cytotoxic mechanism suggests that LIP can upregulate the expression of caspase 1, RIPK1, RIP3 to trigger pyroptosis and necroptosis. To examine the effect of LIP in vivo, tumor xenograft experiments were performed, and the results indicated that LIP inhibits tumor growth without damage to mice. In addition, the cytotoxic action of LIP depended on the phosphatidylserine (PS) content of the cell membrane. CONCLUSIONS: These observations suggest that LIP plays a crucial role in tumor cell survival and growth. The findings will also help to elucidate the mechanisms of host defense in lamprey.