Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: Although the combination of androgen deprivation therapy with docetaxel or abiraterone acetate and prednisone has become a standard treatment for metastatic hormone-sensitive prostate cancer (mHSPC) and has shown improved overall survival, a subset of patients still progress to castration-resistant disease. However, the underlying molecular features in these patients remain poorly understood. Materials and Methods: We performed single-cell RNA sequencing (scRNA-seq) on 12 tissue samples, including 2 mHSPC samples, 7 primary prostate cancer (PCa) samples, and 3 matched normal samples from 7 patients. Raw sequencing data was processed by Cell Ranger software (10X Genomics) and aligned to the human reference genome (GRCh38). A comprehensive analysis of samples from patients with mHSPC was also conducted and validated using a cohort of 52 patients with mHSPC. Results: Our results identified distinct subpopulations within luminal and mononuclear phagocyte (MNP) clusters characterized by proliferative activation associated with unfavorable clinical outcomes. Furthermore, we observed that the MNP cluster exhibited significant proliferation activity. To understand the underlying mechanisms associated with aggressiveness, we conducted cell-cell interaction, copy number variation and pseudotime analysis. Utilizing 87 network genes from scRNA-seq, we classified 52 mHSPC patients into 2 molecular subtypes and demonstrated their correlation with distinct transcriptomic profiles and survival outcomes. Importantly, a 14-gene signature derived from 3 distinct gene sets exhibited a strong association with patient survival and drug response. The prognostic value of our findings was further validated in large-scale cohorts comprising localized PCa, metastatic PCa, mHSPC, and metastatic castration-resistant PCa patients. Conclusion This study provides valuable insights into the identification of high-risk patients, novel biomarkers, and potential therapeutic targets for individuals with mHSPC. Furthermore, the results in this study can serve as a basis for future investigations aimed at refining prognostic strategies and developing targeted therapies for patients with mHSPC.

Non-fluent variant primary progressive aphasia (nfvPPA), a subtype of frontotemporal lobar dementia syndrome, has been proven to have various pathological diagnoses. A 63-year-old woman and 71-year-old man separately visited our clinic for language dysfunction. Both patients showed non-fluent speech. The female patient showed personality change accompanied by language dysfunction, while the male patient had parkinsonian symptoms such as bradykinesia and cogwheel rigidity. Both patients were clinically diagnosed with nfvPPA. Several years after the first visit, the patients died, and a brain autopsy was performed. On postmortem examination, the female patient was pathologically diagnosed with Pick’s disease, while the male patient was diagnosed with progressive supranuclear palsy. Our report suggests that nfvPPA patients might show distinct clinical features depending on underlying pathologies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is a positive-sense singlestranded RNA (+ssRNA) that causes coronavirus disease 2019 (COVID-19). The viral genome encodes twelve genes for viral replication and infection. The third open reading frame is the spike (S) gene that encodes for the spike glycoprotein interacting with specific cell surface receptor – angiotensin converting enzyme 2 (ACE2) – on the host cell membrane. Most recent studies identified a single point mutation in S gene. A single point mutation in S gene leading to an amino acid substitution at codon 614 from an aspartic acid 614 into glycine (D614G) resulted in greater infectivity compared to the wild type SARS-CoV2. We were interested in investigating the mutation region of S gene of SARS-CoV2 from Korean COVID-19 patients. New mutation sites were found in the critical receptor binding domain (RBD) of S gene, which is adjacent to the aforementioned D614G mutation residue. This specific sequence data demonstrated the active progression of SARS-CoV2 by mutations in the RBD of S gene.The sequence information of new mutations is critical to the development of recombinant SARS-CoV2 spike antigens, which may be required to improve and advance the strategy against a wide range of possible SARS-CoV2 mutations.

BACKGROUND: Using analysis of air samples from the workplace, we report on one case of pneumoconiosis in an individual who has been working in a polytetrafluoroethylene (PTFE) spraying process for 28 years. CASE PRESENTATION: The patient was diagnosed with granulomatous lung disease caused by PTFE using computed tomography (CT), lung biopsy and electron microscopy. To assess the qualitative and quantitative exposure to PTFE in workplace, Fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX) and thermogravimetric analysis (TGA) were performed on air samples from the workplace. The presence of PTFE particles was confirmed, and the airborne concentration of PTFE was estimated to be 0.75 mg/m3. CONCLUSIONS: This case demonstrates that long-term exposure to PTFE spraying can cause granulomatous lung lesions such as pneumoconiosis; such lesions appear to be caused not by the degradation products of PTFE from high temperatures but by spraying the particles of PTFE. Along with air-sampling analysis, we suggest monitoring the concentration of airborne PTFE particles related to chronic lung disease.
Biopsy ; Humans ; Hygiene ; Lung ; Lung Diseases ; Microscopy, Electron ; Occupational Diseases ; Pneumoconiosis ; Polytetrafluoroethylene ; Spectroscopy, Fourier Transform Infrared ; Spectrum Analysis

BACKGROUND: We reviewed a series of 188 resected pulmonary mucinous adenocarcinomas (MAs) to clarify the prognostic significance of lepidic and non-lepidic patterns. METHODS: Non-lepidic patterns were divided into bland, non-distorted acini with uncertain invasiveness (pattern 1), unequivocal invasion into stroma (pattern 2), or invasion into alveolar spaces (pattern 3). RESULTS: The mean proportion of invasive patterns (patterns 2 and 3) was lowest in small (≤ 3 cm) tumors, and gradually increased in intermediate (> 3 cm and ≤ 7 cm) and large (> 7 cm) tumors (8.4%, 34.3%, and 50.1%, respectively). Adjusted T (aT) stage, as determined by the size of invasive patterns, was positively correlated with adverse histologic and clinical features including older age, male sex, and ever smokers. aTis tumors, which were exclusively composed of lepidic pattern (n = 9), or a mixture of lepidic and pattern 1 (n = 40) without any invasive patterns, showed 100% disease- free survival (DFS). The aT1mi tumors, with minimal (≤ 5 mm) invasive patterns (n = 63), showed a 95.2% 5-year DFS, with recurrences (n = 2) limited to tumors greater than 3 cm in total size (n = 23). Both T and aT stage were significantly associated with DFS; however, survival within the separate T-stage subgroups was stratified according to the aT stage, most notably in the intermediatestage subgroups. In multivariate analysis, the size of invasive patterns (p = .020), pleural invasion (p < .001), and vascular invasion (p = .048) were independent predictors of recurrence, whereas total size failed to achieve statistical significance (p = .121). CONCLUSIONS: This study provides a rationale for histologic risk stratification in pulmonary MA based on the extent of invasive growth patterns with refined criteria for invasion.
Adenocarcinoma in Situ ; Adenocarcinoma, Mucinous* ; Disease-Free Survival ; Humans ; Lung ; Male ; Mucins* ; Multivariate Analysis ; Recurrence*

Human studies of brain stimulation have demonstrated modulatory effects on the perception of pain. However, whether the primary somatosensory cortical activity is associated with antinociceptive responses remains unknown. Therefore, we examined the antinociceptive effects of neuronal activity evoked by optogenetic stimulation of primary somatosensory cortex. Optogenetic transgenic mice were subjected to continuous or pulse-train optogenetic stimulation of the primary somatosensory cortex at frequencies of 15, 30, and 40 Hz, during a tail clip test. Reaction time was measured using a digital high-speed video camera. Pulse-train optogenetic stimulation of primary somatosensory cortex showed a delayed pain response with respect to a tail clip, whereas no significant change in reaction time was observed with continuous stimulation. In response to the pulse-train stimulation, video monitoring and local field potential recording revealed associated paw movement and sensorimotor rhythms, respectively. Our results show that optogenetic stimulation of primary somatosensory cortex at beta and gamma frequencies blocks transmission of pain signals in tail clip test.
Animals ; Brain ; Humans ; Mice ; Mice, Transgenic ; Neurons ; Optogenetics ; Pain Perception* ; Reaction Time ; Somatosensory Cortex* ; Tail*

The complement system is a part of the innate immune system that potentiates the ability of antibodies and phagocytic cells to clear microbes and damaged cells. The complement system consists of a number of proteins circulating as inactive precursors. It is stimulated mainly by three pathways: the classical pathway, the alternative pathway, and the lectin pathway. There are many genetic polymorphisms in this system, which can over-activate the immune system. In this study, we collected the polymorphisms reported to over-activate complement cascades that affect the immune system and induce autoimmune diseases.
Antibodies ; Autoimmune Diseases ; Complement System Proteins* ; Immune System ; Phagocytes ; Polymorphism, Genetic*