Formaldehyde was classified as a Group I Carcinogen by the International Agency for Research on Cancer (IARC) in 2006. While the IARC has stated that there is a lack of evidence that formaldehyde causes brain cancer, three meta-analyses have consistently reported a significantly higher risk of brain cancer in workers exposed to high levels of formaldehyde. Therefore, we report a case of a worker who was diagnosed with glioblastoma after being exposed to high concentrations of formaldehyde while working with formaldehyde resin in the paper industry.

Purpose: To investigate the prevalence of lower urinary tract symptoms/benign prostatic hyperplasia in a Korean population. Materials and Methods: The Korean Prostate & Voiding Health Association provided free prostate-related community health care and conducted surveys in all regions of Korea from 2001 to 2022 with the cooperation of local government public health centers. A total of 72,068 males older than 50 were surveyed and analyzed. History taking, International Prostate Symptom Score (IPSS), transrectal ultrasonography, prostate-specific antigen (PSA) testing, uroflowmetry, and urine volume testing were performed. Results: The mean prostate volumes in males in their 50s, 60s, 70s, and 80s or above were 24.7 g, 27.7 g, 31 g, and 33.7 g, respectively. The proportion of males with high PSA greater than 3 ng/mL was 3.8% among males in their 50s, 7.7% among males in their 60s, 13.1% among males in their 70s, and 17.9% among males 80 years of age or older. The mean IPSS total scores in males in their 50s, 60s, 70s, and 80s or above were 10.7, 12.7, 14.5, and 16, respectively. Severe symptoms were reported by 27.3% of males, whereas 51.7% reported moderate symptoms. The mean Qmax in males in their 50s, 60s, 70s, and 80s or above were 20 mL/s, 17.4 mL/s, 15.4 mL/s, and 13.8 mL/s, respectively. Conclusions In this population-based study, mean prostate volume, IPSS, PSA, and Qmax were 30.6±15.1 g, 14.8±8.2, 1.9±4.7 ng/mL, and 15.6±6.5 mL/s, respectively. Aging was significantly associated with increased prostate volume, PSA levels, and IPSS scores, and with decreased Qmax and urine volume.

Formaldehyde was classified as a Group I Carcinogen by the International Agency for Research on Cancer (IARC) in 2006. While the IARC has stated that there is a lack of evidence that formaldehyde causes brain cancer, three meta-analyses have consistently reported a significantly higher risk of brain cancer in workers exposed to high levels of formaldehyde. Therefore, we report a case of a worker who was diagnosed with glioblastoma after being exposed to high concentrations of formaldehyde while working with formaldehyde resin in the paper industry.

Formaldehyde was classified as a Group I Carcinogen by the International Agency for Research on Cancer (IARC) in 2006. While the IARC has stated that there is a lack of evidence that formaldehyde causes brain cancer, three meta-analyses have consistently reported a significantly higher risk of brain cancer in workers exposed to high levels of formaldehyde. Therefore, we report a case of a worker who was diagnosed with glioblastoma after being exposed to high concentrations of formaldehyde while working with formaldehyde resin in the paper industry.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Objective: The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods: One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results: The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions.However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations.However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world’s population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Macrophages are important for the first line of defense against microbial pathogens. Integrin CD11b, which is encoded by Itgam, is expressed on the surface of macrophages and has been implicated in adhesion, migration, and cell-mediated cytotoxicity. However, the functional impact of CD11b on the inflammatory responses of macrophages upon microbial infection remains unclear. Here, we show that CD11b deficiency resulted in increased susceptibility to sepsis induced by methicillin-resistant Staphylococcus aureus (MRSA) infection by enhancing the pro-inflammatory activities of macrophages. Upon infection with MRSA, the mortality of Itgam, knockout mice was significantly higher than that of control mice, which is associated with increased production of TNF-α and IL-6. In response to MRSA, both bone marrowderived macrophages and peritoneal macrophages lacking CD11b produced elevated amounts of pro-inflammatory cytokines and nitric oxide. Moreover, CD11b deficiency upregulated IL-4-induced expression of anti-inflammatory mediators such as IL-10 and arginase-1, and an immunomodulatory function of macrophages to restrain T cell activation. Biochemical and confocal microscopy data revealed that CD11b deficiency augmented the activation of NF-κB signaling and phosphorylation of Akt, which promotes the functional activation of macrophages with pro-inflammatory and immunoregulatory phenotypes, respectively. Overall, our experimental evidence suggests that CD11b is a critical modulator of macrophages in response to microbial infection.