今泉 和良

INTRODUCTION: Increasing numbers of cases of mild asymptomatic pulmonary alveolar proteinosis (PAP) are being reported with the recent increase in chest computed tomography (CT). Bronchoscopic diagnosis of mild PAP is challenging because of the patchy distribution of lesions, which makes it difficult to obtain sufficient biopsy samples. Additionally, the pathological findings of mild PAP, particularly those that differ from severe PAP, have not been fully elucidated. This study aimed to clarify the pathological findings of mild PAP and the usefulness of optical biopsy using probe-based confocal laser endomicroscopy (pCLE). METHODS: We performed bronchoscopic optical biopsy using pCLE and tissue biopsy in five consecutive patients with PAP (three with mild PAP and two with severe PAP). We compared the pCLE images of mild PAP with those of severe PAP by integrating clinical findings, tissue pathology, and chest computed tomography images. RESULTS: pCLE images of PAP showed giant cells with strong fluorescence, amorphous substances, and thin alveolar walls. Images of affected lesions in mild PAP were equivalent to those obtained in arbitrary lung lesions in severe cases. All three patients with mild PAP spontaneously improved or remained stable after ≥3 years of follow-up. Serum autoantibodies to granulocyte-macrophage colony-stimulating factor were detected in all five cases. CONCLUSION: Optical biopsy using pCLE can yield specific diagnostic findings, even in patients with mild PAP. pCLE images of affected areas in mild and severe PAP showed similar findings, indicating that the dysfunction level of pathogenic alveolar macrophages in affected areas is similar between both disease intensities.

BACKGROUND: Bronchial thermoplasty (BT) is a recently developed non-pharmacological therapy for refractory bronchial asthma. Although increasing evidence has suggested that BT is effective for various phenotypes of severe asthma, its safety and efficacy in patients with severe irreversible impaired lung function are unclear. OBJECTIVES: To assess the efficacy and safety of BT in patients with refractory asthma, including patients with a severely impaired forced expiratory volume in 1 second (FEV1). DESIGN: This was a single-center, retrospective, observational cohort study. METHODS: We retrospectively reviewed the medical records of 15 patients with refractory asthma (Global Initiative for Asthma step 4 or 5), including patients with severely impaired airflow limitation (% predicted pre-bronchodilator FEV1 <60%), who had undergone BT between June 2016 and January 2022. We analyzed the efficacy (change in asthma symptoms, exacerbation rate, pulmonary function, asthma medication, and serum inflammatory chemokine/cytokines before and after BT) and complications in all patients. We compared these data between patients with severe obstructive lung dysfunction [group 1(G1)] and patients with FEV1 ⩾ 60% [group 2 (G2)]. RESULTS: Six patients were in G1 and nine were in G2. Clinical characteristics, T2 inflammation, and concurrent treatment were equivalent in both groups. BT significantly improved asthma-related symptoms (measured using the Asthma Control Test and Asthma Quality of Life Questionnaire scores) in both groups. FEV1 was significantly improved in G1 but not in G2. Four patients in G2, but none in G1, experienced asthma exacerbation requiring additional systemic corticosteroids (including two requiring prolonged hospitalization) after BT. Long-term responders (patients who reduced systemic or inhaled corticosteroid without newly adding biologics in a follow-up > 2 years) of BT were identified in G1 and G2 (n = 2, 33.3% and n = 4, 44.4%, respectively). CONCLUSION: BT in patients with refractory asthma and severe airflow limitation is equally safe and efficacious as that in patients with moderate airflow limitation.

Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.

Abstract Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative adversarial networks (GANs). However, applying a GAN as a data augmentation technique has not been explored, owing to the quality and diversity of the generated images. To promote such applications by generating diverse images, this study aims to generate free-form lesion images from tumor sketches using a pix2pix-based model, which is an image-to-image translation model derived from GAN. As pix2pix, which assumes one-to-one image generation, is unsuitable for data augmentation, we propose StylePix2pix, which is independently improved to allow one-to-many image generation. The proposed model introduces a mapping network and style blocks from StyleGAN. Image generation results based on 20 tumor sketches created by a physician demonstrated that the proposed method can reproduce tumors with complex shapes. Additionally, the one-to-many image generation of StylePix2pix suggests effectiveness in data-augmentation applications.

Syphilis can cause a wide range of systemic manifestations, such as papular rash, malaise, weight loss, muscle aches, generalized lymphadenopathy, and meningitis. However, pulmonary involvement in patients with secondary syphilis is thought to be relatively rare. Moreover, bone involvement in patients with secondary syphilis is also considered rare, and only a few cases of involvement of lung and bone in such patients have been reported. In this paper, we report a case of secondary syphilis with pulmonary involvement in the form of multiple nodules with low attenuation areas, lymphadenopathy and multiple bone lesions detected on computed tomography and 18F fluorodeoxyglucose-positron emission tomography/computed tomography.

Induced pluripotent stem (iPS) cells are widely used as a research tool in regenerative medicine and embryology. In studies related to lens regeneration in the eye, iPS cells have been reported to differentiate into lens epithelial cells (LECs); however, to the best of our knowledge, no study to date has described their formation of three-dimensional cell aggregates. Notably, in vivo studies in newts have revealed that iris cells in the eye can dedifferentiate into LECs and regenerate a new lens. Thus, as basic research on lens regeneration, the present study investigated the differentiation of human iris tissue-derived cells and human iris tissue-derived iPS cells into LECs and their formation of three-dimensional cell aggregates using a combination of two-dimensional culture, static suspension culture and rotational suspension culture. The results revealed that three-dimensional cell aggregates were formed and differentiated into LECs expressing αA-crystallin, a specific marker protein for LECs, suggesting that the cell-cell interaction facilitated by cell aggregation may have a critical role in enabling highly efficient differentiation of LECs. However, the present study was unable to achieve transparency in the cell aggregates; therefore, we aim to continue to investigate the degradation of organelles and other materials necessary to make the interior of the formed cell aggregates transparent. Furthermore, we aim to expand on our current work to study the regeneration of the lens and ciliary body as a whole in vitro, with the aim of being able to restore focusing function after cataract surgery.

OBJECTIVE: It is essential to accurately diagnose and classify histological subtypes into adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung carcinoma (SCLC) for the appropriate treatment of lung cancer patients. However, improving the accuracy and stability of diagnosis is challenging, especially for non-small cell carcinomas. The purpose of this study was to compare multiple deep convolutional neural network (DCNN) technique with subsequent additional classifiers in terms of accuracy and characteristics in each histology. METHODS: Lung cancer cytological images were classified into ADC, SCC, and SCLC with four fine-tuned DCNN models consisting of AlexNet, GoogLeNet (Inception V3), VGG16 and ResNet50 pretrained by natural images in ImageNet database. For more precise classification, the figures of 3 histological probabilities were further applied to subsequent machine learning classifiers using Naïve Bayes (NB), Support vector machine (SVM), Random forest (RF), and Neural network (NN). RESULTS: The classification accuracies of the AlexNet, GoogLeNet, VGG16 and ResNet50 were 74.0%, 66.8%, 76.8% and 74.0%, respectively. Well differentiated typical morphologies were tended to be correctly judged by all four architectures. However, poorly differentiated non-small cell carcinomas lacking typical structures were inclined to be misrecognized in some DCNNs. Regarding the histological types, ADC were best judged by AlexNet and SCC by VGG16. Subsequent machine learning classifiers of NB, SVV, RF, and NN improved overall accuracies to 75.1%, 77.5%, 78.2%, and 78.9%, respectively. CONCLUSION: Fine-tuning DCNNs in combination with additional classifiers improved classification of cytological diagnosis of lung cancer, although classification bias could be indicated among DCNN architectures.

Secondary fungal infections are a critical problem that accompany immunosuppressive therapy for severe coronavirus disease 2019 (COVID-19). We report a fatal case of COVID-19 with disseminated mucormycosis diagnosed during autopsy. A 58-year-old man with diabetes was hospitalized for severe COVID-19 and treated with remdesivir, systemic steroids and tocilizumab. Following treatment, he was provided extracorporeal membrane oxygenation support. However, he died of multiple organ failure accompanied by pulmonary and kidney infarction, as revealed by computed tomography. Autopsy revealed that the infarction was caused by thromboangiitis due to mucormycosis in the brain, lungs, heart, liver and kidneys. Therefore, the diagnosis of disseminated mucormycosis was established. Disseminated mucormycosis is a rare complication of COVID-19. Although its early diagnosis is difficult, the disease progresses rapidly. Hence, we propose that immunosuppressive treatment for COVID-19 should be administered with caution considering the risk of developing severe opportunistic infections, such as mucormycosis.

OBJECTIVE: The prevalence of obstructive sleep apnea (OSA) in Japan is 9% among males and 3% among females. Up to 2.5 million patients are estimated to suffer from the disease, but limited number of facilities are capable of carrying out polysomnography (PSG), leaving more than 80% of these individuals are undiagnosed. In recent years, the development of new portable sleep monitoring (PMs) devices has been remarkable. We evaluate the correlation between the results of the LS-140 PMs device (Fukuda Denshi Tech Co. Ltd.), released in 2017, and those of PSG. METHODS: We obtained contemporaneous data from the same patients by equipping 58 patients with PMs (LS-140) devices while they underwent PSG. Our primary outcome was Case 2 of the intraclass correlation coefficient (ICC), i.e., the ICC (2.1). And we used a Bland-Altman analysis to compare the apnea-hypopnea index (AHI) given by PSG and the respiratory event index (REI) given by LS-140 and examined the sensitivity and specificity of the REI relative to the AHI in the diagnosis of OSA. We also carried out the same comparison but in terms of the presence or absence of periodic limb movements (PLMs). RESULTS: The ICC (2.1) between The REI and the AHI was 0.944, a rather high value (p<0.0001). The mean difference between AHI and REI values was -3.6 (p<0.0001), indicating a negative fixed bias. Sensitivity may decrease in groups with PLMs. CONCLUSION: The REI and the AHI are highly correlated, giving LS-140 sufficient diagnostic sensitivity and specificity to screen for OSA.

<title>Abstract</title>In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.

We herein report a case of acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) triggered by COVID-19. An 87-year-old woman tested positive for COVID-19 on a polymerase chain reaction test, and computed tomography revealed ground-glass opacity (GGO) superimposed on a background pattern consistent with usual interstitial pneumonia. Considering these data, we diagnosed her with AE-IPF. She experienced worsening of dyspnea and expansion of the GGO. Therefore, we introduced high-dose steroids (methylprednisolone 250 mg/day for 3 days). After the treatment, the pulmonary infiltrates improved. She was discharged from our hospital without severe disability. High-dose steroids can be a viable treatment option for AE-IPF triggered by COVID-19.

Erlotinib is used to treat advanced non-small-cell lung cancer (NSCLC), the common serious adverse events are skin disorders. The dose intensity of erlotinib should be maintained as much as possible by an appropriate control of adverse events in order to maintain its efficacy. Therefore, the management of these adverse events related to skin disorders would enable a continuous erlotinib treatment without interruption and dose reduction. This study assessed the effect of pharmaceutical consultation in outpatients who received erlotinib. Participants included patients with NSCLC who received erlotinib therapy for more than 6 months between December 2007 and March 2019. The participants were divided into two groups: the intervention group that included patients who received pharmaceutical consultation targeting outpatients by a pharmacist and the nonintervention group that included patients who did not. We retrospectively investigated patient characteristics, treatment regimens, and treatment efficacy. We included a total of 33 patients (18 and 15 patients in the nonintervention and intervention groups, respectively) in this study. The intervention group had a significantly higher median relative dose intensity (RDI) of erlotinib than the nonintervention group (p = 0.0437). In addition, the pharmaceutical consultation targeting outpatients was identified as a factor contributing to the maintenance of RDI ≥90% (p = 0.0269). The present study indicated that there was improvement in RDI with pharmaceutical consultation targeting outpatients with advanced NSCLC.

The incidence rate of post-cataract surgery posterior capsule opacification (PCO) and lens turbidity is about 20% in 5 years. Soemmering's ring, which is a type of PCO also called a regenerated lens with similar tissue structure to that of a human lens, is an important proxy for elucidating the mechanism of lens regeneration and maintenance of transparency. The authors created new human immortalized crystalline lens epithelial cells (iHLEC-NY1s) with excellent differentiation potential, and as a result of culturing the cells by static and rotation-floating methods, succeeded in producing a three-dimensional cell structure model (3D-iHLEC-NY1s) which is similar to Soemmering's ring in tissue structure and expression characteristics of αA-crystalline, βB2-crystalline, vimentin proteins. 3D-iHLEC-NY1s is expected to be a proxy in vitro experimental model of Soemmering's ring to enable evaluation of drug effects on suppression of cell aggregate formation and transparency. By further improving the culture conditions, we aim to control the cell sequence and elucidate the mechanism underlying the maintenance of lens transparency.