The application of small molecule targeted drugs for lung cancer has significantly improved the survival of lung cancer patients. However, these drugs have a wide variety of types, fast development and market launch of new drugs, complex adverse reactions, and are mostly used at home, which increases the risk of irrational drug use. At the same time, insufficient monitoring of efficacy and safety is also prone to occur, ultimately affecting treatment outcomes. This consensus focuses on 43 small molecule targeted drugs or combinations for lung cancer, providing standardized recommendations for rational drug use and monitoring of efficacy/adverse reactions in clinical practice. The recommendations are regarding drug selection, dosage adjustment, efficacy monitoring, adverse reaction monitoring, and improvement of patient compliance. This consensus aims to improve the rational use and efficacy/safety monitoring quality of small molecule targeted drugs for lung cancer, ensure the effectiveness and safety of drug treatment, prolong the survival of lung cancer patients and improve their quality of life.
.
Humans ; Lung Neoplasms/drug therapy* ; Antineoplastic Agents/adverse effects* ; Consensus ; Molecular Targeted Therapy ; Drug Monitoring ; Small Molecule Libraries/therapeutic use*

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

Neuronal reprogramming is an innovative technique for converting non-neuronal somatic cells into neurons that can be used to replace lost or damaged neurons, providing a potential effective therapeutic strategy for central nervous system (CNS) injuries or diseases. Transcription factors have been used to induce neuronal reprogramming, while their reprogramming efficiency is relatively low, and the introduction of exogenous genes may result in host gene instability or induce gene mutation. Therefore, their future clinical application may be hindered by these safety concerns. Compared with transcription factors, small-molecule compounds have unique advantages in the field of neuronal reprogramming, which can overcome many limitations of traditional transcription factor-induced neuronal reprogramming. Here, we review the recent progress in the research of small-molecule compound-mediated neuronal reprogramming and its application in CNS regeneration and repair.
Humans ; Cellular Reprogramming/drug effects* ; Neurons/cytology* ; Animals ; Transcription Factors ; Small Molecule Libraries/pharmacology* ; Nerve Regeneration

PURPOSE: To estimate the impact of a health information exchange (HIE) pilot project on ophthalmology department care. METHODS: Study sites included 4 ophthalmic clinics in three regions participating in the HIE pilot project (group A), and 12 clinics with similar distances and numbers of patient referrals as group A but who were not participating in the HIE pilot project (group B). The mean wait time, total medical costs, and ophthalmic examinations of referral patients were analyzed. RESULTS: The mean wait times were 8.4 ± 8.0 days in group A, which included 83 patients, and 11.7 ± 15.4 days in group B, which included 417 patients. The wait time was significantly shorter in group A (p = 0.005). Sensitivity analyses also indicated shorter wait times in group A. In 247 patients in group B who were referred to tertiary referral hospitals automatically through the conventional clinical cooperation center with group A, the wait times were 8.4 ± 8.0 and 7.7 ± 8.8 days, respectively, and the total cost of medical care was 260.6 ± 271.4 and 257.0 ± 251.7 thousand Won, respectively. No differences in these factors were found between the groups (p = 0.503, 0.913, respectively). There were no significant differences in participation in the HIE pilot project regarding ophthalmic examinations conducted within 2 weeks since patient referral (p > 0.050 for all). CONCLUSIONS: The HIE is advantageous because it results in shorter wait times to see an ophthalmologist, due to the automatic referral method based on medical records. However, there are no benefits in reducing total costs of medical care or the number of clinical examinations.
Health Communication ; Health Information Exchange ; Humans ; Medical Records ; Methods ; Ophthalmology ; Pilot Projects ; Referral and Consultation ; Tertiary Care Centers ; Tertiary Healthcare

PURPOSE: A systematic review and meta-analysis was conducted to evaluate the effects of smartphone-based mobile learning for nurses and nursing students. METHODS: Electronic literature search of PubMed, Cochrane Library, Embase, SCOPUS, Web of Science, ProQuest Central, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Educational Resource Information Center (ERIC) was conducted. Two authors independently reviewed empirical studies for inclusion and extracted the design, sample size, intervention method, outcome variables, and statistical values of them. Methodological quality was assessed using Cochrane collaboration risk of bias tool. To estimate the effect size, meta-analysis was performed using R meta program. RESULTS: Authors identified 11 randomized or nonrandomized controlled trials of a total of 3,419 studies. Overall effect sizes by random-effects model was large [Hedges'g (g) = 1.12, 95% confidence interval (CI) 0.72−1.52], with learning attitude (g = 1.69), skills (g = 1.41), knowledge (g = 1.47), and confidence in performance (g = 1.54). For heterogeneity, subgroup analyses using meta-analysis of variance were performed, but no significant difference was found. Finally, a funnel plot and Egger's regression test along with trim-and-fill analysis and fail-safe N were conducted to check for publication bias, but no significant bias was detected. CONCLUSION: Smartphone-based mobile learning had significantly positive influence on nursing students' knowledge, skills, confidence in performance, and learning attitude. Smartphone-based mobile learning may be an alternative or supportive method for better education in nursing fields.
Bias (Epidemiology) ; Cooperative Behavior ; Education ; Education, Nursing ; Humans ; Information Centers ; Learning ; Methods ; Nursing ; Population Characteristics ; Publication Bias ; Sample Size ; Smartphone ; Students, Nursing

Archives ; Breast Neoplasms ; Breast ; Clone Cells ; Drug Therapy ; Ectopic Gene Expression ; Gene Expression ; Humans ; Microarray Analysis ; Neoplasm Metastasis ; Neoplastic Stem Cells ; Oligonucleotide Array Sequence Analysis ; Prognosis ; Survival Rate

OBJECTIVES: This study aimed to determine the prevalence of odontogenic cysts, tumors, and other lesions among reports in the archives of the Department of Oral and Maxillofacial Surgery at the Faculty of Dentistry affiliated with Kocaeli University collected over a four-year period.MATERIALS AND METHODS: In this retrospective study, patient records from the archive of the Department of Oral and Maxillofacial Surgery from 2014 to 2018 were reviewed. Patient demographic information (age and sex) and lesion location were recorded and analyzed.RESULTS: From a total of 475 files reviewed, odontogenic cyst was confirmed in 340 cases (71.6%), and odontogenic tumor was confirmed in 52 cases (10.9%). Regarding odontogenic cyst type, the most common was radicular cyst (216 cases), followed by dentigerous cyst (77 cases) and odontogenic keratocyst (23 cases). Among odontogenic tumors, the most frequent was odontoma (19 cases), followed by ossifying fibroma (18 cases) and ameloblastoma (9 cases). Giant cell granuloma was also reported in 35 cases.CONCLUSION: The distribution pattern of odontogenic cysts and tumors in our retrospective study is relatively similar to that reported in the literature. Complete clinical reports for final diagnosis of these lesions and routine follow-up examinations are very important for treatment.
Ameloblastoma ; Archives ; Biopsy ; Dentigerous Cyst ; Dentistry ; Diagnosis ; Fibroma, Ossifying ; Follow-Up Studies ; Granuloma, Giant Cell ; Humans ; Jaw Neoplasms ; Odontogenic Cysts ; Odontogenic Tumors ; Odontoma ; Prevalence ; Radicular Cyst ; Retrospective Studies ; Surgery, Oral

OBJECTIVE: To compare the diagnostic accuracy of dynamic computed tomography (CT) and gadoxetate-enhanced magnetic resonance imaging (MRI) for characterization of hepatic lesions by using the Liver Imaging Reporting and Data System (LI-RADS) in a multicenter, off-site evaluation.MATERIALS AND METHODS: In this retrospective multicenter study, we evaluated 231 hepatic lesions (114 hepatocellular carcinomas [HCCs], 58 non-HCC malignancies, and 59 benign lesions) confirmed histologically in 217 patients with chronic liver disease who underwent both gadoxetate-enhanced MRI and dynamic CT at one of five tertiary hospitals. Four radiologists at different institutes independently reviewed all MR images first and the CT images 4 weeks later. They evaluated the major and ancillary imaging features and categorized each hepatic lesion according to the LI-RADS v2014. Diagnostic performance was calculated and compared using generalized estimating equations.RESULTS: MRI showed higher sensitivity and accuracy than CT for diagnosing hepatic malignancies; the pooled sensitivities, specificities, and accuracies for categorizing LR-5/5V/M were 59.0% vs. 72.4% (CT vs. MRI; p < 0.001), 83.5% vs. 83.9% (p = 0.906), and 65.3% vs. 75.3% (p < 0.001), respectively. CT and MRI showed comparable capabilities for differentiating between HCC and other malignancies, with pooled accuracies of 79.9% and 82.4% for categorizing LR-M, respectively (p = 0.139).CONCLUSION: Gadoxetate-enhanced MRI showed superior accuracy for categorizing LR-5/5V/M in hepatic malignancies in comparison with dynamic CT. Both modalities had comparable accuracies for distinguishing other malignancies from HCC.
Academies and Institutes ; Carcinoma, Hepatocellular ; Contrast Media ; Humans ; Information Systems ; Liver ; Liver Diseases ; Magnetic Resonance Imaging ; Retrospective Studies ; Tertiary Care Centers

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.
Asian Continental Ancestry Group ; Humans ; Licensure ; Medical Subject Headings ; Methods ; National Library of Medicine (U.S.) ; Natural Language Processing ; Vocabulary, Controlled

Korea is undergoing a rapid environmental change in health and welfare. Therefore, the law mandates the establishment and implementation of plans in accordance with the changes. A total of 49 long-term plans related to health and welfare were specified by the National Law Information Center, the Korean representative legal information website managed by the Korea Ministry of Government Legislation. Of the 49 long-term plans, 10 plans (20.4%) were not yet fully constructed. Eight out of 10 non-constructive plans have been put into force for more than a year, but these plans still require further systematic planning and development. The complete construction of long-term plans is substantial to account for the changes in South Korean health and welfare. In addition, a systematic plan with solidarity and continuity between the mutual plans should be established in planning.
Information Centers ; Jurisprudence ; Korea ; Social Welfare