The Detection of Neoplastic Cells Using Objective Cytomorphologic Parameters in Malignant Lymphoma.

Pathologic evaluation is the most crucial method for diagnosing malignant lymphomas. However, there are no established diagnostic criteria for evaluating pathologic morphology. We manually circled cell nuclei in the lesions of 10 patients with diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, and reactive lymphadenitis. Seventeen parameters related to nuclear shape, color, and other characteristics were measured. We attempted to compare the statistical differences between these subtypes and extract distinctive disease-specific populations on the basis of these parameters. Statistically significant differences were observed between the different types of lymphoma for many of the 17 parameters. Through t-distributed stochastic neighbor embedding analysis, we extracted a cluster of cells that showed distinctive features of DLBCL and were not found in follicular lymphoma or reactive lymphadenitis. We created a decision tree to identify the characteristics of the cells within that cluster. Based on a 5-fold cross-validation study, the average sensitivity, specificity, and accuracy obtained were 84.1%, 98.4%, and 97.3%, respectively. A similar result was achieved using a validation experiment. Important parameters that indicate the features of DLBCL include Area, ConcaveCount, MaxGray, and ModeGray. By quantifying pathologic morphology, it was possible to objectively represent the cell morphology specific to each lymphoma subtype using quantitative indicators. The quantified morphologic information has the potential to serve as a reproducible and flexible diagnostic tool.

Structure-based development of novel substrate-type G9a inhibitors as epigenetic modulators for sickle cell disease treatment.

The discovery and development of structurally distinct lysine methyltransferase G9a inhibitors have been the subject of intense research in epigenetics. Structure-based optimization was conducted, starting with the previously reported seed compound 7a and lead to the identification of a highly potent G9a inhibitor, compound 7i (IC50 = 0.024 µM). X-ray crystallography for the ligand-protein interaction and kinetics study, along with surface plasmon resonance (SPR) analysis, revealed that compound 7i interacts with G9a in a unique binding mode. In addition, compound 7i caused attenuation of cellular H3K9me2 levels and induction of γ-globin mRNA expression in HUDEP-2 cells in a dose-dependent manner.

Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications.

In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework, the low-rank update, that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a the generalized low-rank update (GLRU) method, which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as support vector machines and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the number of data set changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods.

Deep learning based identification of pituitary adenoma on surgical endoscopic images: a pilot study.

Accurate tumor identification during surgical excision is necessary for neurosurgeons to determine the extent of resection without damaging the surrounding tissues. No conventional technologies have achieved reliable performance for pituitary adenomas. This study proposes a deep learning approach using intraoperative endoscopic images to discriminate pituitary adenomas from non-tumorous tissue inside the sella turcica. Static images were extracted from 50 intraoperative videos of patients with pituitary adenomas. All patients underwent endoscopic transsphenoidal surgery with a 4 K ultrahigh-definition endoscope. The tumor and non-tumorous tissue within the sella turcica were delineated on static images. Using intraoperative images, we developed and validated deep learning models to identify tumorous tissue. Model performance was evaluated using a fivefold per-patient methodology. As a proof-of-concept, the model's predictions were pathologically cross-referenced with a medical professional's diagnosis using the intraoperative images of a prospectively enrolled patient. In total, 605 static images were obtained. Among the cropped 117,223 patches, 58,088 were labeled as tumors, while the remaining 59,135 were labeled as non-tumorous tissues. The evaluation of the image dataset revealed that the wide-ResNet model had the highest accuracy of 0.768, with an F1 score of 0.766. A preliminary evaluation on one patient indicated alignment between the ground truth set by neurosurgeons, the model's predictions, and histopathological findings. Our deep learning algorithm has a positive tumor discrimination performance in intraoperative 4-K endoscopic images in patients with pituitary adenomas.

Metastatic ability and the epithelial-mesenchymal transition in induced cancer stem-like hepatoma cells.

Cancer stem cells (CSCs) are thought to play important roles in cancer malignancy. Previously, we successfully induced sphere cancer stem-like cells (CSLCs) from several cell lines and observed the property of chemoresistance. In the present study, we examined the metastatic potential of these induced CSLCs. Sphere cancer stem-like cells were induced from a human hepatoma cell line (SK-HEP-1) in a unique medium containing neural survival factor-1. Splenic injection of cells into immune-deficient mice was used to assess hematogenous liver metastasis. Transcriptomic strand-specific RNA-sequencing analysis, quantitative real-time PCR, and flow cytometry were carried out to examine the expression of epithelial-mesenchymal transition (EMT)-related genes. Splenic injection of CSLCs resulted in a significantly increased frequency of liver metastasis compared to parental cancer cells (P < .05). In CSLCs, a mesenchymal marker, Vimentin, and EMT-promoting transcription factors, Snail and Twist1, were upregulated compared to parental cells. Correspondingly, significant enrichment of the molecular signature of the EMT in CSLCs relative to parental cancer cells was shown (q < 0.01) by RNA-sequencing analysis. This analysis also revealed differential expression of CD44 isoforms between CSLCs and parental cancer cells. Increasing CD44 isoforms containing an extra exon were observed, and the standard CD44 isoform decreased in CSLCs compared to parental cells. Interestingly, another CD44 variant isoform encoding a short cytoplasmic tail was also upregulated in CSLCs (11.7-fold). Our induced CSLCs possess an increased liver metastatic potential in which promotion of the EMT and upregulation of CD44 variant isoforms, especially short-tail, were observed.

Mixed HCV Infection of Genotype 1B and Other Genotypes Influences Non-response during Daclatasvir + Asunaprevir Combination Therapy.

Daclatasvir (DCV) + asunaprevir (ASV) combination therapy has become available for patients with hepatitis C virus (HCV) serogroup 1 infection. We studied the efficacy of this therapy by focusing on the factors associated with sustained virological responses (SVR) including resistance-associated variants (RAVs) and mixed infection of different HCV genotypes. We enrolled 951 HCV serogroup 1-positive patients who received this combination therapy at our hospital or affiliated hospitals. The presence of RAVs in non-structural (NS) regions 3 and 5A was analyzed by direct sequencing. HCV genotypes were determined by PCR with genotype-specific primers targeting HCV core and NS5B regions. SVR was achieved in 91.1% of patients. Female sex, age > 70 years, and RAVs were significantly associated with non-SVR (p<0.01 for all). Propensity score-matching results among the patients without RAVs regarding sex, age, and fibrosis revealed that mixed HCV infection determined by HCV NS5B genotyping showed significantly lower SVR rates than 1B-mono infection (p=0.02). Female sex and RAVs were significant factors associated with treatment failure of this combination therapy for patients with HCV serogroup 1 infection. Mixed HCV infection other than 1B-mono infection would be useful for predicting treatment failure.

Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments.

To assist in the structural optimization of hit/lead compounds during drug discovery, various computational approaches to identify potentially useful bioisosteric conversions have been reported. Here, the preference of chemical fragments to hydrogen bonds with specific amino acid residues was used to identify potential bioisosteric conversions. We first compiled a data set of chemical fragments frequently occurring in complex structures contained in the Protein Data Bank. We then used a computational approach to determine the amino acids to which these chemical fragments most frequently hydrogen bonded. The results of the frequency analysis were used to hierarchically cluster chemical fragments according to their amino acid preferences. The Euclid distance between amino acid preferences of chemical fragments for hydrogen bonding was then compared to MMP information in the ChEMBL database. To demonstrate the applicability of the approach for compound optimization, the similarity of amino acid preferences was used to identify known bioisosteric conversions of the epidermal growth factor receptor inhibitor gefitinib. The amino acid preference distance successfully detected bioisosteric fragments corresponding to the morpholine ring in gefitinib with a higher ROC score compared to those based on topological similarity of substituents and frequency of MMP in the ChEMBL database.

Development of a 2D Image Reconstruction and Viewing System for Histological Images from Multiple Tissue Blocks: Towards High-Resolution Whole-Organ 3D Histological Images.

High-resolution 3D histology image reconstruction of the whole brain organ starts from reconstructing the high-resolution 2D histology images of a brain slice. In this paper, we introduced a method to automatically align the histology images of thin tissue sections cut from the multiple paraffin-embedded tissue blocks of a brain slice. For this method, we employed template matching and incorporated an optimization technique to further improve the accuracy of the 2D reconstructed image. In the template matching, we used the gross image of the brain slice as a reference to the reconstructed 2D histology image of the slice, while in the optimization procedure, we utilized the Jaccard index as the metric of the reconstruction accuracy. The results of our experiment on the initial 3 different whole-brain tissue slices showed that while the method works, it is also constrained by tissue deformations introduced during the tissue processing and slicing. The size of the reconstructed high-resolution 2D histology image of a brain slice is huge, and designing an image viewer that makes particularly efficient use of the computing power of a standard computer used in our laboratories is of interest. We also present the initial implementation of our 2D image viewer system in this paper.

Dendritic cells transfected with heat-shock protein 70 messenger RNA for patients with hepatitis C virus-related hepatocellular carcinoma: a phase 1 dose escalation clinical trial.

BACKGROUND: We previously reported overexpression of heat-shock protein (HSP) 70 in hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) using proteomic profiling and immunohistochemical staining (IHS). This suggested that HSP70 could be a molecular target for treatment of HCC. METHODS: Twelve patients with HCV-related HCC were enrolled in a phase 1 clinical trial. Dendritic cells (DCs) transfected with HSP70 mRNA (HSP70-DCs) induced by electroporation were injected intradermally. Patients were treated three times every 3 weeks. The number of HSP70-DCs injected was 1 × 10(7) as the lowest dose, then 2 × 10(7) as the medium dose, and then 3 × 10(7) as the highest dose. Immunological analyses were performed. FINDINGS: No adverse effects of grade III/IV, except one grade III liver abscess at the 3 × 10(7) dose, were observed. Thus, we added three more patients to confirm whether 3 × 10(7) is an appropriate dose. Eventually, we chose 3 × 10(7) as the recommended dose of DCs. Complete response (CR) without any recurrence occurred in two patients, stable disease in five, and progression of disease in five. The two patients with CR have had no recurrence for 44 and 33 months, respectively. IHS in one patient who underwent partial hepatectomy showed infiltration of CD8+ T cells and granzyme B in tumors, indicating that the dominant immune effector cells were cytotoxic T lymphocytes with tumor-killing activity. INTERPRETATION: This study demonstrated that HSP70-DCs therapy is both safe and feasible in patients with HCV-related HCC. Further clinical trials should be considered.

Randomized trial of low-dose peginterferon α-2b plus low and escalating doses of ribavirin in older patients with chronic hepatitis C with high viral load genotype 1.

Elderly patients with chronic hepatitis C cannot tolerate standard combination therapy of peginterferon and ribavirin, which remains the backbone of therapy in many countries, including Japan. The efficacy and safety of low-dose peginterferon α-2b in combination with low and escalating doses of ribavirin in older patients with high viral load genotype 1 were investigated in this randomized controlled trial. Thirty-two patients (age ≥ 60 years) were randomized into standard (group 1) or low (group 2) doses of peginterferon α-2b in combination with low and escalating doses of ribavirin. Patients were evaluated for safety and efficacy of treatment. There was a higher virological response rate in group 1 than in group 2. However, the response in men was higher than in women in the early treatment phase and 24 weeks after treatment (P = 0.008). There was no significant difference between the two groups in the virological response rate in men and women. Completion of therapy was higher in group 2 than in group 1 (31% vs. 13%, P = 0.200). Dose modification of ribavirin was less frequent in group 2 than in group 1 (69% vs. 88%, P = 0.200). These data suggest that combination therapy with low-dose peginterferon plus low and escalating doses of ribavirin may be safer in older patients than that with standard dose peginterferon, without impairing the treatment response.

Effectiveness of extending treatment duration in therapy with pegylated interferon and ribavirin for genotype 2 hepatitis C virus infection.

The effectiveness of extending treatment duration as response guided therapy was previously reported for chronic hepatitis C (CHC) genotype 1, but is still controversial for genotype 2. The present study is a retrospective cohort study to investigate the effectiveness of extending treatment duration in therapy with pegylated interferon and ribavirin for patients with CHC genotype 2 by focusing on the timing at which patients obtained undetectable HCV RNA. A total of 306 patients who obtained undetectable HCV RNA by week 24 of treatment and completed 24 weeks of treatment were enrolled. Rapid virological response (RVR) to standard therapy was achieved by 122 patients (51%), and 89% of them obtained sustained virological response (SVR), while 69% of non-RVR patients achieved SVR. Non-RVR patients with undetectable HCV RNA at week 8, and insufficient adherence＜80% pegylated interferon and ribavirin during the first 24 weeks, significantly improved their SVR rate by extended therapy. Among patients receiving extended therapy, drug adherences did not differ between SVR and non-SVR patients, indicating that extending treatment duration might compensate for insufficient antiviral effects due to insufficient drug adherences. This finding might be useful in creating a guideline for extending treatment duration for patients with CHC genotype 2.

Cancer stem-like sphere cells induced from de-differentiated hepatocellular carcinoma-derived cell lines possess the resistance to anti-cancer drugs.

BACKGROUND: Cancer stem cells (CSCs) are thought to play important roles in therapy-resistance. In this study, we induced cancer stem-like cells from hepatocellular carcinoma (HCC) cell lines using a unique medium, and examined their potential for resistance to anti-cancer drugs. METHODS: The human HCC cell lines SK-HEP-1 (SK), HLE, Hep 3B, and HuH-7 were used to induce cancer stem-like cells with our sphere induction medium supplemented with neural survival factor-1. NANOG and LIN28A were examined as stemness markers. Several surface markers for CSC such as CD24, CD44, CD44 variant, and CD90 were analyzed by flow-cytometry. To assess the resistance to anti-cancer drugs, the MTS assay, cell cycle analysis, and reactive oxygen species (ROS) activity assay were performed. RESULTS: Poorly differentiated HCC derived SK and undifferentiated HCC derived HLE cell lines efficiently formed spheres of cells (SK-sphere and HLE-sphere), but well-differentiated HCC-derived HuH-7 and Hep 3B cells did not. SK-spheres showed increased NANOG, LIN28A, and ALDH1A1 mRNA levels compared to parental cells. We observed more CD44 variant-positive cells in SK-spheres than in parental cells. The cell viability of SK-spheres was significantly higher than that of SK cells in the presence of several anti-cancer drugs except sorafenib (1.7- to 7.3-fold, each P < 0.05). The cell cycle of SK-spheres was arrested at the G0/G1 phase compared to SK cells. SK-spheres showed higher ABCG2 and HIF1A mRNA expression and lower ROS production compared to parental cells. CONCLUSION: Our novel method successfully induced cancer stem-like cells, which possessed chemoresistance that was related to the cell cycle, drug efflux, and ROS.

[Clinical benefit of thoracoscopic approach for liver tumors located under the diaphragm].

UNLABELLED: It is important to ensure restricted operation fields and operation maneuvers in the surgical resection of liver tumors located under the diaphragm, especially those near the hepatic vein and IVC. Here we describe resection and ablation using thoracoscopy for tumors under the diaphragm after preoperative three-dimensional (3D) simulation. METHOD: Preoperative 3D images were reformatted preoperatively using a 3D software tool (Virtual Place; AZE, Japan). These images simulate the thoracoscopic view on the surface of the diaphragm, enabling us to confirm the tumor location and choose optimal port position. RESULT: We performed thoracoscopic surgery in 5 patients (4 with HCC and 1 with a metastatic tumor) after the simulation. In all cases, we were able to safely confirm the tumor locations and perform the surgeries. CONCLUSION: Preoperative 3D simulation makes it easy to determine the optimal port position for the thoracoscope and confirm the tumor location. The approach through the diaphragm using thoracoscpy is useful since it does not require liver mobilization.

Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma.

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.

A study of criteria for grading follicular lymphoma using a cell type classifier from pathology images based on complementary-label learning.

We propose a criterion for grading follicular lymphoma that is consistent with the intuitive evaluation, which is conducted by experienced pathologists. A criterion for grading follicular lymphoma is defined by the World Health Organization (WHO) based on the number of centroblasts and centrocytes within the field of view. However, the WHO criterion is not often used in clinical practice because it is impractical for pathologists to visually identify the cell type of each cell and count the number of centroblasts and centrocytes. Hence, based on the widespread use of digital pathology, we make it practical to identify and count the cell type by using image processing and then construct a criterion for grading based on the number of cells. Here, the problem is that labeling the cell type is not easy even for experienced pathologists. To alleviate this problem, we build a new dataset for cell type classification, which contains the pathologists' confusion records during labeling, and we construct the cell type classifier using complementary-label learning from this dataset. Then we propose a criterion based on the composition ratio of cell types that is consistent with the pathologists' grading. Our experiments demonstrate that the classifier can accurately identify cell types and the proposed criterion is more consistent with the pathologists' grading than the current WHO criterion.

Discovery of Novel Bicyclic Pyrazoles as Potent PIP5K1C Inhibitors.

Phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) is generated by phosphatidylinositol 4-phosphate 5-kinases (PIP5Ks) from phosphatidylinositol 4-phosphate (PI4P). Structurally diverse and selective inhibitors against PIP5Ks are required to further elucidate the therapeutic potential for PIP5K inhibition, although the effects of PIP5K inhibition on various diseases and their symptoms, such as cancer and chronic pain, have been reported. Our medicinal chemistry efforts led to novel and potent PIP5K1C inhibitors. Compounds 30 and 33 not only showed potent activity but also demonstrated low total clearance in mice and high levels of kinase selectivity. These compounds might serve as tools to further elucidate the complex biology and therapeutic potential of PIP5K inhibition.

[A case of hepatectomy for liver metastases of advanced colon cancer after conversion therapy].

Curative resection can be achieved in some cases of multiple liver metastases that are initially unresectable by multistage hepatectomy. We report the case of a patient who underwent 2 hepatectomy procedures for liver metastases of advanced colon cancer after conversion chemotherapy and 2-stage hepatectomy; this treatment resulted in long-term survival.

[A resected case of hilar cholangiocarcinoma treated with neoadjuvant chemoradiation therapy].

The patient was a 69-year-old woman with elevated levels of hepatobiliary enzymes. An abdominal computed tomography (CT) scan revealed an enhanced mass in the liver hilum with dilatation of the intrahepatic bile duct. We diagnosed hilar cholangiocarcinoma and administered neoadjuvant chemoradiation therapy because of the possibility of tumor cells remaining at the surgical margins. Radical surgery was performed and pathological examination showed the tumor to be Grade 2b according to the Oboshi-Shimosato classification. Although postoperative bile leakage and intra-abdominal abscess were observed, the patient was discharged on day 82 after surgery. The patient is still alive without recurrence at 17 months after the surgery. Neoadjuvant chemoradiation therapy has the potential to obtain a negative surgical margin in patients with hilar cholangiocarcinoma, which is likely to be positive for cancer cells at the surgical margin in preoperative diagnosis.

[A case of hepatocellular carcinoma with Vp4 tumor thrombus treated by radiation therapy before hepatectomy].

A 61-year-old man who complained of right hypochondralgia was diagnosed as hepatocellular carcinoma (HCC) associated with hepatitis B virus (HBV) infection. The tumor was located in the right lobe and S3, and the tumor thrombus extended into the main portal and left portal veins. Preoperatively, real-time tumor-tracking radiation therapy was performed on the tumor thrombus (20 Gy/4 Fr),after vessel coils were placed at the anterior hepatic artery as a marker for the radiation. Ten days after radiation therapy, extended right hepatectomy with thrombectomy and S3 partial hepatectomy were performed. There were no postsurgical complications, and intrahepatic artery infusion chemotherapy was performed. The patient was alive with no recurrences 20 months after surgery. Radiation therapy before hepatectomy is an effective treatment for portal venous tumor thrombus in HCC.

Transformer-based personalized attention mechanism for medical images with clinical records.

In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based on both the images and various clinical records. Consequently, pathologists examine medical images with prior knowledge of the patients and the attention regions may change depending on the clinical records. In this study, we propose a method, called the Personalized Attention Mechanism (PersAM) method, by which the attention regions in medical images according to the clinical records. The primary idea underlying the PersAM method is the encoding of the relationships between medical images and clinical records using a variant of the Transformer architecture. To demonstrate the effectiveness of the PersAM method, we applied it to a large-scale digital pathology problem involving identifying the subtypes of 842 malignant lymphoma patients based on their gigapixel whole-slide images and clinical records.