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.

BACKGROUND AND OBJECTIVES: It takes considerable time and effort to make decisions about management and follow up for the thyroid cancer. Also there are risks of incorrectness or confusion on the part of thyroid specialists. We developed a thyroid cancer calculator that does automatic thyroid cancer staging, prognosis predicting and suggesting follow-up strategies in order to reduce the burden of thyroid specialists who have to memorize a lot of guidelines and statistics, and to give consistency to the treatment plan. MATERIALS AND METHOD: An automatic thyroid cancer calculator was developed by using a computer program called 'Qt 5.2 version', based on patient demographics, diagnosis, treatment, and follow-up status. This partly cited the history of prior thyroid cancer or other cancer registration, and focused on the specification of differentiated thyroid cancer. RESULTS: The program consisted of survival, recurrence and, dynamic re-stratification with follow-up. The patient registration form consisted of identification number, name and operation date, and patients needed to enter their thyroid cancer status, including clinical and pathologic information after registration. The entered information could be easily accessed in a few seconds. The program helped to update patient's current status, promptly collect data for clinical studies of thyroid cancers and provide better patient care. This program was simple, convenient and time-saving for users as it specifically contained important thyroid cancer items. CONCLUSION: Although this program is still in its primitive stage, the Kosin thyroid calculator reduces the workload of thyroid specialists and prevents the loss of clinical data. Furthermore, it could be a useful tool for the management and research of thyroid cancer.
Demography ; Diagnosis ; Follow-Up Studies* ; Humans ; Neoplasm Staging ; Patient Care ; Prognosis* ; Recurrence ; Specialization ; Thyroid Gland ; Thyroid Neoplasms*

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.

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.

Cigarette smoke is a major risk factor for several diseases, including chronic obstructive pulmonary and cardiovascular diseases. The toxicity of the cigarette smoke can be determined in vitro. The cytotoxicity test of the cigarette smoke is commonly conducted using the cigarette smoke condensate (CSC) and cigarette smoke extract (CSE). The CSC and CSE methods are well known for sampling of the particles and water-soluble compounds in the cigarette smoke, respectively. In this study, the CSC and CSE were analyzed by using a gas chromatography-mass spectrometry (GC-MS) system equipped with a wax column for separation of the volatile organic compounds. The cytotoxic effect of the CSC and CSE were evaluated thoroughly by comparing the analytical results of the CSC and CSE samples. The total concentration of the volatile organic compounds detected in the CSC sample was similar to that in the CSE sample based on the peak area. Except for the dimethyl sulfoxide solvent, nicotine had the highest concentration in the CSC sample, while acetonitrile had the highest concentration in the CSE sample. The compositions were as follows: (1) CSC sample: 55.8% nicotine, 18.0% nicotyrine, 3.20% 1,2,3-propanetriol, triacetate, 1.28% ethyl chloride, 1.22% phenol, etc. and (2) CSE sample: 18.7% acetonitrile, 18.0% acetone, 12.5% 2-hydroxy-2-methyl-propanenitrile, 8.98% nicotine, 5.86% nicotyrine, etc. In this manner, to accurately examine the cytotoxicity of the cigarette smoke using CSC or CSE, the components and their concentrations in the CSC and CSE samples should be considered.
Acetone ; Cardiovascular Diseases ; Dimethyl Sulfoxide ; Ethyl Chloride ; Gas Chromatography-Mass Spectrometry ; In Vitro Techniques ; Nicotine ; Phenol ; Risk Factors ; Smoke ; Tobacco Products ; Volatile Organic Compounds

Cigarette smoke is a major risk factor for several diseases, including chronic obstructive pulmonary and cardiovascular diseases. The toxicity of the cigarette smoke can be determined in vitro. The cytotoxicity test of the cigarette smoke is commonly conducted using the cigarette smoke condensate (CSC) and cigarette smoke extract (CSE). The CSC and CSE methods are well known for sampling of the particles and water-soluble compounds in the cigarette smoke, respectively. In this study, the CSC and CSE were analyzed by using a gas chromatography-mass spectrometry (GC-MS) system equipped with a wax column for separation of the volatile organic compounds. The cytotoxic effect of the CSC and CSE were evaluated thoroughly by comparing the analytical results of the CSC and CSE samples. The total concentration of the volatile organic compounds detected in the CSC sample was similar to that in the CSE sample based on the peak area. Except for the dimethyl sulfoxide solvent, nicotine had the highest concentration in the CSC sample, while acetonitrile had the highest concentration in the CSE sample. The compositions were as follows: (1) CSC sample: 55.8% nicotine, 18.0% nicotyrine, 3.20% 1,2,3-propanetriol, triacetate, 1.28% ethyl chloride, 1.22% phenol, etc. and (2) CSE sample: 18.7% acetonitrile, 18.0% acetone, 12.5% 2-hydroxy-2-methyl-propanenitrile, 8.98% nicotine, 5.86% nicotyrine, etc. In this manner, to accurately examine the cytotoxicity of the cigarette smoke using CSC or CSE, the components and their concentrations in the CSC and CSE samples should be considered.
Acetone ; Cardiovascular Diseases ; Dimethyl Sulfoxide ; Ethyl Chloride ; Gas Chromatography-Mass Spectrometry ; In Vitro Techniques ; Nicotine ; Phenol ; Risk Factors ; Smoke* ; Tobacco Products* ; Volatile Organic Compounds*

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.

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.