Halide Ion Mixing across Colloidal 2D Ruddlesden-Popper Perovskites: Implication of Spacer Ligand on Mixing Kinetics.

Halide ion exchange seen in metal halide perovskites provide a substantial opportunity to control their halide composition and corresponding optoelectronic properties. Halide ion mixing across colloidal 3D perovskite nanocrystals have been extensively studied while the mixing within colloidal 2D counterparts remain underexplored. In this study, the halide ion exchange kinetics across colloidally stable 2D Ruddlesden-Popper layered bromide (Br) and iodide (I) perovskites using two different spacer ligands such as aromatic phenethylammonium (PEA) versus linear butyammonium (BA) is demonstrated. The halide exchange kinetic rate constant (k), as determined by tracking time-dependent absorbance changes, indicates that Br/I halide mixing in 2D PEA-based perovskites (2.7 × 10-3 min-1 ) occurs at an order of magnitude slower than in 2D BA-based perovskites (3.3 × 10-2 min-1 ). Concentration (≈1 mM to 100 mM) and temperature-dependent (50 to 80 °C) kinetic studies further allow for the determination of activation barrier for halide ion mixing across the 2D layered perovskites with 75.2 ± 4.4 kJ mol-1 (2D PEA) and 57.8 ± 7.8 kJ mol-1 (2D BA), respectively. The activation energy reveals that the type of spacer cations plays a crucial role in controlling the halide ion mobility and halide stability due mainly to the internal ligand chemical interaction within 2D structures.

Deep learning-based natural language processing for detecting medical symptoms and histories in emergency patient triage.

OBJECTIVE: The manual recording of electronic health records (EHRs) by clinicians in the emergency department (ED) is time-consuming and challenging. In light of recent advancements in large language models (LLMs) such as GPT and BERT, this study aimed to design and validate LLMs for automatic clinical diagnoses. The models were designed to identify 12 medical symptoms and 2 patient histories from simulated clinician-patient conversations within 6 primary symptom scenarios in emergency triage rooms. MATERIALS AND METHOD: We developed classification models by fine-tuning BERT, a transformer-based pre-trained model. We subsequently analyzed these models using eXplainable artificial intelligence (XAI) and the Shapley additive explanation (SHAP) method. A Turing test was conducted to ascertain the reliability of the XAI results by comparing them to the outcomes of tasks performed and explained by medical workers. An emergency medicine specialist assessed the results of both XAI and the medical workers. RESULTS: We fine-tuned four pre-trained LLMs and compared their classification performance. The KLUE-RoBERTa-based model demonstrated the highest performance (F1-score: 0.965, AUROC: 0.893) on human-transcribed script data. The XAI results using SHAP showed an average Jaccard similarity of 0.722 when compared with explanations of medical workers for 15 samples. The Turing test results revealed a small 6% gap, with XAI and medical workers receiving the mean scores of 3.327 and 3.52, respectively. CONCLUSION: This paper highlights the potential of LLMs for automatic EHR recording in Korean EDs. The KLUE-RoBERTa-based model demonstrated superior classification performance. Furthermore, XAI using SHAP provided reliable explanations for model outputs. The reliability of these explanations was confirmed by a Turing test.

Oral Postoperative Antibiotic Prophylaxis for Outpatient Total Hip and Knee Arthroplasty: Describing Current Practices.

BACKGROUND: Despite an increase in outpatient total hip arthroplasty (THA) and total knee arthroplasty (TKA), large-scale data are lacking on current practice for antibiotic prophylaxis prescribing. We aimed to describe current oral antibiotic prophylaxis practices nationally for outpatient THA and TKA. METHODS: This nationwide retrospective cohort study included primary outpatient THA or TKA procedures in patients aged 18 to 64 years from 2018 to 2021 using a national claims database. Oral antibiotic prescriptions filled perioperatively (defined as 5 days before to 3 days after surgery) were extracted; these were categorized and assumed to represent postoperative prophylaxis. Multivariable logistic regression measured associations between patient and surgery characteristics and perioperative oral antibiotic prophylaxis. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) are reported. RESULTS: Oral antibiotic prescriptions were filled in 16.5% of 73,015 outpatient THA and TKA (18.4% of 24,857 THAs, 15.5% of 48,158 TKAs) procedures. Prescriptions were most often for cephalosporins (74.3%), with cephalexin (52.8%), and cefadroxil (19.1%) being the most common. Non-cephalosporin antibiotics prescribed were mainly clindamycin (6.8%), sulfamethoxazole-trimethoprim (6.7%), and doxycycline (6.2%). The odds of receiving oral antibiotic prophylaxis were higher for THA compared to TKA (OR 1.13, 95% CI 1.09 to 1.18, P < .001) and in the presence of obesity, diabetes, and autoimmune conditions (OR 1.08 to 1.13, P < .001 to .01). Ambulatory surgery center procedures also had significantly increased odds of prophylaxis compared to hospital-based outpatient surgeries (OR 2.62, 95% CI 2.51 to 2.73, P < .001). Additionally, regional and time-based variations were noted. CONCLUSIONS: Perioperative oral antibiotic prophylaxis prescriptions were filled in only 16.5% of outpatient THA and TKA cases, with variation in the type of antibiotic prescribed. The receipt of any prophylaxis and specific medications was associated with demographic, clinical, and procedure-related characteristics. Follow-up research will evaluate associations with infection risk reduction.

Transplanting Gold Active Sites into Non-Precious-Metal Nanoclusters for Efficient CO2-to-CO Electroreduction.

Electrocatalytic CO2 reduction reaction (CO2RR) is greatly facilitated by Au surfaces. However, large fractions of underlying Au atoms are generally unused during the catalytic reaction, which limits mass activity. Herein, we report a strategy for preparing efficient electrocatalysts with high mass activities by the atomic-level transplantation of Au active sites into a Ni4 nanocluster (NC). While the Ni4 NC exclusively produces H2, the Au-transplanted NC selectively produces CO over H2. The origin of the contrasting selectivity observed for this NC is investigated by combining operando and theoretical studies, which reveal that while the Ni sites are almost completely blocked by the CO intermediate in both NCs, the Au sites act as active sites for CO2-to-CO electroreduction. The Au-transplanted NC exhibits a remarkable turnover frequency and mass activity for CO production (206 molCO/molNC/s and 25,228 A/gAu, respectively, at an overpotential of 0.32 V) and high durability toward the CO2RR over 25 h.

Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes.

Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis.

Pancreatin-induced liberation of starch/cyanidin 3-O-glucoside complexes from rice cooked with black soybean that exhibit slow hydrolysis.

Cyanidin 3-O-glucoside (C3G), which has various health-promoting functions, is contained in black soybean (BSB). In Japan and Korea, BSB is cooked with rice and the cooked rice appears purplish in colour. In this study, BSB was cooked with glutinous rice, non-glutinous rice, and high-amylose rice. The amount of C3G detected in high-amylose rice was greater than that detected in glutinous rice, suggesting that C3G combined more efficiently with amylose than with amylopectin. Pancreatin induced the liberation of starch/C3G complexes from the purplish cooked rice, and rate of the liberation was in the following order; glutinous rice < non-glutinous rice < high-amylose rice. The amylose/C3G complexes liberated from high-amylose rice was hydrolysed slowly, while the amylopectin/C3G complexes liberated from glutinous rice were hydrolysed into smaller amylopectin/C3G complexes that were difficult to further hydrolysis. Thus, C3G may be useful for preparing foods whose starch hydrolysis is slow.

Prognostic Value of Coronary Artery Disease-Reporting and Data System Score for Major Adverse Cardiac Events in Patients Attending the Emergency Department With Acute Chest Pain.

OBJECTIVE: This study aimed to compare the prognostic performance of Coronary Artery Disease (CAD)-Reporting and Data System (CAD-RADS) score with those of clinical risk factors and the extent of CAD classification for predicting major adverse cardiac events in emergency department patients. METHODS: A total of 779 patients with acute chest pain at low to intermediate risk for CAD underwent cardiac computed tomography angiography. The primary end point was early and late major adverse cardiac events. We developed the following models: model 1, clinical risk factors; model 2, clinical risk factors and CAD-RADS scores; model 3, clinical risk factors and extent of CAD. RESULTS: The C-statistics revealed that both CAD-RADS score and CAD extent improved risk stratification over the clinical risk factors (C-index for early events: C-index: 0.901 vs 0.814 and 0.911 vs 0.814; C-index for late events: 0.897 vs 0.808 and 0.905 vs 0.808; all P < 0.05). CONCLUSIONS: The CAD-RADS score had additional risk prediction benefits over clinical risk factors for emergency department patients.

Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study.

BACKGROUND: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea. For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. METHODS: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. RESULTS: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81-0.9), KNN (AUROC, 0.89; 95% CI, 0.85-0.93), RF (AUROC, 0.86; 95% CI, 0.82-0.9) and BERT (AUROC, 0.82; 95% CI, 0.75-0.87) achieved excellent classification performance. Based on SHAP, we found "stress", "pain score point", "fever", "breath", "head" and "chest" were the important vocabularies for determining KTAS and symptoms. CONCLUSION: We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Greenness and kidney? A review of epidemiological studies on the association between green space and kidney disease.

Recent accumulating epidemiological evidence underlines the important role of environmental exposures on kidney diseases. Among environmental exposures, this study addresses "Green space," which has been recognized as one of the major environmental exposures at the population level. We review a total of seven epidemiological studies currently published on greenness and kidney disease. We also discuss knowledge gaps in the epidemiological evidence in relation to study design, greenness exposure index, emerging kidney outcomes, and inequalities. With an increase in public attention regarding environmental risks and climate change, an improved understanding of the beneficial effects of green space can play an important role in promoting kidney health.

Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.

Development of Indocyanine Green/Methyl-ß-cyclodextrin Complex-Loaded Liposomes for Enhanced Photothermal Cancer Therapy.

Photothermal therapy (PTT) is a promising cancer therapeutic approach due to its spatial selectivity and high potency. Indocyanine green (ICG) has been considered a biocompatible PTT agent. However, ICG has several challenges to hinder its clinical use including rapid blood clearance and instability to heat, light, and solvent, leading to a loss of photoactivation property and PTT efficacy. Herein, we leveraged stabilizing components, methyl-ß-cyclodextrin and liposomes, in one nanoplatform (ICD lipo) to enhance ICG stability and the photothermal therapeutic effect against cancer. Compared to ICG, ICD lipo displayed a 4.8-fold reduction in degradation in PBS solvent after 30 days and a 3.4-fold reduction in photobleaching after near-infrared laser irradiation. Moreover, in tumor-bearing mice, ICD lipo presented a 2.7-fold increase in tumor targetability and inhibited tumor growth 9.6 times more effectively than did ICG without any serious toxicity. We believe that ICD lipo could be a potential PTT agent for cancer therapeutics.

Systematic Selection of High-Affinity ssDNA Sequences to Carbon Nanotubes.

Single-walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single-stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high-throughput systematic selection, enriching high-affinity ssDNA sequences from a vast random library. Specific base compositions and patterns are identified that govern the binding affinity between ssDNA and SWCNTs. Molecular dynamics simulations validate the stability of ssDNA conformations on SWCNTs and reveal the pivotal role of hydrogen bonds in this interaction. Additionally, it is demonstrated that machine learning could accurately distinguish high-affinity ssDNA sequences, providing an accessible model on a dedicated webpage (http://service.k-medai.com/ssdna4cnt). These findings open new avenues for high-affinity ssDNA-SWCNT constructs for stable and sensitive molecular detection across diverse scientific disciplines.

Phloroglucinol Derivatives Exert Anti-Inflammatory Effects and Attenuate Cognitive Impairment in LPS-Induced Mouse Model.

Neuroinflammation is an inflammatory immune response that arises in the central nervous system. It is one of the primary causes of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Phloroglucinol (PG) is a natural product contained in extracts of plant, algae and microbe and has been reported to have antioxidant and anti-inflammatory properties. In this study, we synthesized PG derivatives to enhance antioxidant and anti-inflammatory activity. Among PG derivatives, 6 a suppressed pro-oxidative and inflammatory molecule nitric oxide (NO) production more effectively than PG. Moreover, 6 a dose-dependently reduced the expression of proinflammatory cytokines such as IL-6, IL-1ß, TNF-α, and NO producing enzyme iNOS in lipopolysaccharide (LPS)-stimulated BV-2 microglial cells. Additionally, we confirmed that 6 a alleviated cognitive impairment and glial activation in mouse model of LPS-induced neuroinflammation. These findings suggest that novel PG derivative, 6 a, is a potential treatment for neurodegenerative diseases.

Influence of Age on Associations of Occlusal Status and Number of Present Teeth with Dementia in Community-Dwelling Older People in Japan: Cross-Sectional Study.

While occlusal status has been reported to be related to cognitive function, little is known about the influence of age on that relationship. The present study examined the associations of tooth loss and occlusal status with dementia in the older people, as well as the effects of age on those relationships. A total of 196 older participants (median age: 84 years) were enrolled. Occlusal status was assessed using functional tooth units (FTU), calculated based on the number of paired natural or artificial teeth. Logistic regression analysis was then performed using dementia as the objective variable, and FTU or number of teeth as explanatory variables. The results showed that higher FTU was associated with lower risk of dementia. Furthermore, when stratified by median age, the association was greater for those aged less than 84 years. On the other hand, there was no significant association of number of present teeth with dementia. These results suggest that the risk of dementia is lower for individuals with better occlusion and that occlusal factor may have a greater effect on dementia onset in younger older people. It is thus recommended that both occlusal function and age be incorporated as factors in programs developed for dementia prevention.

Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.

In this prospective, multi-reader, multi-vendor study, we evaluated the performance of a commercially available deep neural network (DNN)-based MR image reconstruction in enabling accelerated 2D fast spin-echo (FSE) knee imaging. Forty-five subjects were prospectively enrolled and randomly divided into three 3T MRIs. Conventional 2D FSE and accelerated 2D FSE sequences were acquired for each subject, and the accelerated FSE images were reconstructed and enhanced with DNN-based reconstruction software (FSE-DNN). Quantitative assessments and diagnostic performances were independently evaluated by three musculoskeletal radiologists. For statistical analyses, paired t-tests, and Pearson's correlation were used for image quality comparison and inter-reader agreements. Accelerated FSE-DNN reduced scan times by 41.0% on average. FSE-DNN showed better SNR and CNR (p < 0.001). Overall image quality of FSE-DNN was comparable (p > 0.05), and diagnostic performances of FSE-DNN showed comparable lesion detection. Two of cartilage lesions were under-graded or over-graded (n = 2) while there was no significant difference in other image sets (n = 43). Overall inter-reader agreement between FSE-conventional and FSE-DNN showed good agreement (R2 = 0.76; p < 0.001). In conclusion, DNN-based reconstruction can be applied to accelerated knee imaging in multi-vendor MRI scanners, with reduced scan time and comparable image quality. This study suggests the potential for DNN-accelerated knee MRI in clinical practice.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

BACKGROUND: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. RESULTS: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). CONCLUSIONS: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Development of Spleen Targeting H2S Donor Loaded Liposome for the Effective Systemic Immunomodulation and Treatment of Inflammatory Bowel Disease.

Nanoparticles are primarily taken up by immune cells after systemic administration. Thus, they are considered an ideal drug delivery vehicle for immunomodulation. Because the spleen is the largest lymphatic organ and regulates the systemic immune system, there have been studies to develop spleen targeting nanoparticles for immunomodulation of cancer and immunological disorders. Inflammatory bowel disease (IBD) includes disorders involving chronic inflammation in the gastrointestinal tract and is considered incurable despite a variety of treatment options. Hydrogen sulfide (H2S) is one of the gasotransmitters that carries out anti-inflammatory functions and has shown promising immunomodulatory effects in various inflammatory diseases including IBD. Herein, we developed a delicately tuned H2S donor delivering liposome for spleen targeting (ST-H2S lipo) and studied its therapeutic effects in a dextran sulfate sodium (DSS) induced colitis model. We identified the ideal PEG type and ratio of liposome for a high stability, loading efficiency, and spleen targeting effect. In the treatment of the DSS-induced colitis model, we found that ST-H2S lipo and conventional long-circulating liposomes loaded with H2S donors (LC-H2S lipo) reduced the severity of colitis, whereas unloaded H2S donors did not. Furthermore, the therapeutic effect of ST-H2S lipo was superior to that of LC-H2S lipo due to its better systemic immunomodulatory effect than that of LC-H2S lipo. Our findings demonstrate that spleen targeting H2S lipo may have therapeutic potential for IBD.

Dual-Emissive Mn-Doped Lead Halide Perovskite Nanocrystals as Background-Suppressed Latent Fingerprint Detection Probes.

Diverse strategies have been developed to visualize latent fingerprints (LFPs) that are undetectable by the naked eye. Among them, fluorescence-based approaches have emerged as an attractive method for enabling high-resolution LFP imaging. However, the use of fluorescent probes for LFP detection remains challenging due to cumbersome processing, low selectivity, and high background interference. Here, we demonstrate highly efficient, sensitive, and background-free LFP detection with dual-color emission arising from manganese (Mn)-doped lead halide perovskite (CsPb(Cl1-yBry)3) nanocrystals (NCs). The resulting bright, fluorescent, solid-state nanopowder (NP) permits the visualization of LFP ridge structures and the resolution of level 1-3 LFP features. The dual-color emission of the Mn-doped perovskite NP provides a simple, robust, and effective route to overcome background interference, thereby increasing the resolution and sensitivity of the LFP detection. The combination of the high quantum efficiency and dual emission of Mn-doped perovskite NP offers great potential for forensic science.

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach.

Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

Effects of Both Japanese-Style Dietary Patterns and Nutrition on Falling Incidents among Community-Dwelling Elderly Individuals: A Cross-Sectional Study.

Approximately 20% of the community-dwelling Japanese elderly (≥65 years) experience falling annually, with injury frequency rising with age. Increased nursing home admission/hospitalization risk influences healthy aging and QOL. Nutrition for musculoskeletal health is necessary, though the relationship of falling with nutritional status in the elderly is largely unknown. We investigated falling incidents and nutritional status, including a Japanese-style diet in a community-dwelling cohort. Using a cross-sectional design, 186 subjects (median age 83.0 years, males/females 67/119) were analyzed. Oral and systemic health conditions were assessed. A brief-type self-administered diet history questionnaire (BDHQ) was given for nutritional status. Analysis of covariance (adjusted for gender, age, BMI, articular disease/osteoporosis history, present tooth number, educational level) and the Japanese-Mediterranean diet (jMD) score adapted for Japan were used. The jMD score and falling incidents were significantly associated, with point increases related to a significantly decreased falling risk of 28% (OR: 0.72; 95%CI: 0.57−0.91). Of the 13 jMD food components, fish, eggs, and potatoes had a significant relationship with reduced falling, while significant associations of intake of animal protein, potassium, magnesium, zinc, and cholesterol (p < 0.05) were also observed. The results suggest that the jMD dietary pattern is an important factor for the prevention of falling incidents in elderly individuals.