Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

BACKGROUND: To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. OBJECTIVE: This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. METHODS: We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. RESULTS: The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. CONCLUSIONS: Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Effects of Balanced Versus Saline-based Solutions on Acute Kidney Injury in Off-pump Coronary Artery Bypass Surgery: A Randomized Controlled Trial.

OBJECTIVES: To determine whether balanced solutions can reduce the incidence of acute kidney injury after off-pump coronary artery bypass surgery compared with saline. DESIGN: Randomized controlled trial. SETTING: Single tertiary care center. PARTICIPANTS: Patients who underwent off-pump coronary artery bypass surgery between June 2014 and July 2020. INTERVENTIONS: Balanced solution-based chloride-restrictive intravenous fluid strategy. MEASUREMENTS AND MAIN RESULTS: The primary outcome was acute kidney injury within 7 postoperative days, as defined by the 2012 Kidney Disease: Improving Global Outcomes Clinical Practice Guideline. The incidence of acute kidney injury was 4.4% (8/180) in the balanced group and 7.3% (13/178) in the saline group. The difference was not statistically significant (risk difference, -2.86%; 95% confidence interval [CI], -7.72% to 2.01%; risk ratio, 0.61, 95% CI, 0.26 to 1.43; p = 0.35). Compared with the balanced group, the saline group had higher levels of intraoperative serum chloride and lower base excess, which resulted in a lower pH. CONCLUSIONS: In patients undergoing off-pump bypass surgery with a normal estimated glomerular filtration rate, the intraoperative balanced solution-based chloride-restrictive intravenous fluid administration strategy did not decrease the rate of postoperative acute kidney injury compared with the saline-based chloride-liberal intravenous fluid administration strategy.

MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer.

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5'-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

Solar-Driven Sustainability: III-V Semiconductor for Green Energy Production Technologies.

Long-term societal prosperity depends on addressing the world's energy and environmental problems, and photocatalysis has emerged as a viable remedy. Improving the efficiency of photocatalytic processes is fundamentally achieved by optimizing the effective utilization of solar energy and enhancing the efficient separation of photogenerated charges. It has been demonstrated that the fabrication of III-V semiconductor-based photocatalysts is effective in increasing solar light absorption, long-term stability, large-scale production and promoting charge transfer. This focused review explores on the current developments in III-V semiconductor materials for solar-powered photocatalytic systems. The review explores on various subjects, including the advancement of III-V semiconductors, photocatalytic mechanisms, and their uses in H2 conversion, CO2 reduction, environmental remediation, and photocatalytic oxidation and reduction reactions. In order to design heterostructures, the review delves into basic concepts including solar light absorption and effective charge separation. It also highlights significant advancements in green energy systems for water splitting, emphasizing the significance of establishing eco-friendly systems for CO2 reduction and hydrogen production. The main purpose is to produce hydrogen through sustainable and ecologically friendly energy conversion. The review intends to foster the development of greener and more sustainable energy source by encouraging researchers and developers to focus on practical applications and advancements in solar-powered photocatalysis.

Unveiling the distinctive mechanical and thermal properties of γ-GeSe.

γ-GeSe is a newly identified polymorph among group-IV monochalcogenides, characterized by a distinctive interatomic bonding configuration. Despite its promising applications in electrical and thermal domains, the experimental verification of its mechanical and thermal properties remains unreported. Here, we experimentally characterize the in-plane Young's modulus (E) and thermal conductivity ([Formula: see text]) of γ-GeSe. The mechanical vibrational modes of freestanding γ-GeSe flakes are measured using optical interferometry. Nano-indentation via atomic force microscopy is also conducted to induce mechanical deformation and to extract the E. Comparison with finite-element simulations reveals that the E is 97.3[Formula: see text]7.5 GPa as determined by optical interferometry and 109.4[Formula: see text]13.5 GPa as established through the nano-indentation method. Additionally, optothermal Raman spectroscopy reveals that γ-GeSe has a lattice thermal conductivity of 2.3 [Formula: see text] 0.4 Wm-1K-1 and a total thermal conductivity of 7.5 [Formula: see text] 0.4 Wm-1K-1 in the in-plane direction at room temperature. The notably high [Formula: see text] ratio in γ-GeSe, compared to other layered materials, underscores its distinctive structural and dynamic characteristics.

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets.

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

Multiplex analysis for the identification of plasma protein biomarkers for predicting lung cancer immunotherapy response.

Background: Programmed death-ligand (PD-L1) expression serves as a predictive biomarker for immune checkpoint inhibitor (ICI) sensitivity in non-small cell lung cancer (NSCLC). Nevertheless, the development of biomarkers that reliably predict ICI response remains an ongoing endeavor due to imperfections in existing methodologies. Objectives: ICIs have led to a new paradigm in the treatment of NSCLC. The current companion PD-L1 diagnostics are insufficient in predicting ICI response. Therefore, we sought whether the Olink platform could be applied to predict response to ICIs in NSCLC. Design: We collected blood samples from patients with NSCLC before ICI treatment and retrospectively analyzed proteomes based on their response to ICI. Methods: Overall, 76 NSCLC patients' samples were analyzed. Proteomic plasma analysis was performed using the Olink platform. Intraplate reproducibility, validation, and statistical analyses using elastic net regression and generalized linear models with clinical parameters were evaluated. Results: Intraplate coefficient of variation (CV) assays ranged from 3% to 6%, and the interplate CV was 14%. In addition, the Pearson correlation coefficient of the Olink Normalized Protein eXpression data was validated. No statistical differences were observed in the analyses of progressive disease and response to ICIs. Furthermore, no single proteome showed prognostic value in terms of progression-free survival. Conclusion: In this study, the proximity extension assay-based approach of the Olink panel could not predict the patient's response to ICIs. Our proteomic analysis failed to achieve predictive value in both response or progression to ICIs and progression-free survival (PFS).

Numerical investigation of the effect of air layer on drag reduction in channel flow over a superhydrophobic surface.

This study investigates the effects of an air layer on drag reduction and turbulence dynamics in channel flow over a superhydrophobic surface (SHS). Employing the OpenFOAM platform, direct numerical simulation was conducted to investigate turbulent channel flow with an air layer over an SHS. The simulations, which take into account the interaction between water and air, analyze various parameters such as velocity distribution, drag reduction (DR), Reynolds stress, turbulent kinetic energy (TKE), and coherent structures near the water-air interface. The presence of an air layer significantly alters the velocity distribution, leading to higher velocities at the interface compared to simulations without the air layer. Notably, the thickness of the air layer emerges as an important factor, with larger thicknesses resulting in increased velocities and drag reduction. This study underscores the substantial impact of the air layer on TKE near the superhydrophobic surface, emphasizing its role in understanding and optimizing drag reduction. Furthermore, the nonlinear relationship between slip velocity, Q contours, and coherent structures near the SHS are investigated.

Clinical outcomes according to the timing of the first tracheostomy tube change.

Purpose: The first tracheostomy tube replacement is a critical procedure that can cause various complications, but there are few studies on the optimal timing of tracheostomy tube replacement in adult patients. This study aimed to evaluate the appropriate timing to replace the first tracheostomy tube to improve outcomes in adult patients. Materials and methods: This study was a retrospective cohort study that included 3957 patients aged ≥18 years who underwent the first tracheostomy tube change from January 2010 to February 2021. The primary outcome was all-cause mortality after the first tracheostomy tube change. Results: The all-cause mortality was statistically significantly lower in group changing the first tracheostomy tube between 7 and 9 days than in other groups (42.1%, P = 0.001). After adjustments in the multivariable analyses, early first tracheostomy tube change within 6 days was independently associated with increased all-cause mortality. The hospital stay, ICU stay, and post-procedural pulmonary complications seemed to increase as the replacement time was delayed. Conclusions: The timing of the first tracheostomy tube change between 7 and 9 days after tracheostomy was associated with improved clinical outcomes, including all-cause mortality. Further prospective investigations are needed to determine whether the optimal timing of the first tracheostomy tube change can reduce mortality.

PIBF1 regulates trophoblast syncytialization and promotes cardiovascular development.

Proper placental development in early pregnancy ensures a positive outcome later on. The developmental relationship between the placenta and embryonic organs, such as the heart, is crucial for a normal pregnancy. However, the mechanism through which the placenta influences the development of embryonic organs remains unclear. Trophoblasts fuse to form multinucleated syncytiotrophoblasts (SynT), which primarily make up the placental materno-fetal interface. We discovered that endogenous progesterone immunomodulatory binding factor 1 (PIBF1) is vital for trophoblast differentiation and fusion into SynT in humans and mice. PIBF1 facilitates communication between SynT and adjacent vascular cells, promoting vascular network development in the primary placenta. This process affected the early development of the embryonic cardiovascular system in mice. Moreover, in vitro experiments showed that PIBF1 promotes the development of cardiovascular characteristics in heart organoids. Our findings show how SynTs organize the barrier and imply their possible roles in supporting embryogenesis, including cardiovascular development. SynT-derived factors and SynT within the placenta may play critical roles in ensuring proper organogenesis of other organs in the embryo.

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis.

Techniques such as using an optical microscope and Raman spectroscopy are common methods for detecting single-layer graphene. Instead of relying on these laborious and expensive methods, we suggest a novel approach inspired by skilled human researchers who can detect single-layer graphene by simply observing color differences between graphene flakes and the background substrate in optical microscope images. This approach implemented the human cognitive process by emulating it through our data extraction process and machine learning algorithm. We obtained approximately 300,000 pixel-level color difference data from 140 graphene flakes from 45 optical microscope images. We utilized the average and standard deviation of the color difference data for each flake for machine learning. As a result, we achieved F1-Scores of over 0.90 and 0.92 in identifying 60 and 50 flakes from green and pink substrate images, respectively. Our machine learning-assisted computing system offers a cost-effective and universal solution for detecting the number of graphene layers in diverse experimental environments, saving both time and resources. We anticipate that this approach can be extended to classify the properties of other 2D materials.

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

PURPOSE: To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed. MATERIALS AND METHODS: A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed. RESULTS: Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively. CONCLUSIONS: Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

Preoperative low muscle mass and early postoperative outcomes in children undergoing living donor liver transplantation: A retrospective study.

Low skeletal muscle mass may develop in children with end-stage liver disease, affecting postoperative outcomes. We retrospectively investigated whether preoperative low muscle mass was associated with early postoperative outcomes in pediatric patients undergoing living donor liver transplantation (LDLT). Electronic medical records of children (age below 12 y) who underwent LDLT between February 1, 2007, and January 31, 2018, were reviewed. The cross-sectional areas of psoas, quadratus lumborum, and erector spinae muscles at the level of fourth-fifth lumbar intervertebral disks were measured using abdominal CT images, divided by the square of the height and were added to obtain the total skeletal muscle index (TSMI). The patients were divided into two groups according to the median TSMI in the second quintile (1859.1 mm 2 /m 2 ). Complications in the early postoperative period (within 30 d after surgery) classified as Clavien-Dindo grade 3 or higher were considered major complications. Logistic regression analyses were performed to determine the association between preoperative low muscle mass and early postoperative outcomes. In the study population of 123 patients (median age, 14 mo; range, 8-38 mo) who underwent LDLT, 29% and 71% were classified in the low (mean TSMI, 1642.5 ± 187.0 mm 2 /m 2 ) and high (mean TSMI 2188.1 ± 273.5 mm 2 /m 2 ) muscle mass groups, respectively. The rates of major complications, mechanical ventilation >96 hours, intensive care unit stay >14 days, hospital stay >30 days, and in-hospital mortality were not significantly different between the 2 groups. Additionally, adverse outcomes according to pediatric end-stage liver disease scores and sex were not significantly different between the 2 groups. In conclusion, preoperative low muscle mass defined by TSMI was not associated with early postoperative outcomes in pediatric patients undergoing LDLT.

Inhalation of Origanum majorana L. essential oil while working reduces perceived stress and anxiety levels of nurses in a COVID-19 intensive care unit: a randomized controlled trial.

Background: Nurses caring for patients with coronavirus disease 2019 (COVID-19) experience higher psychosocial distress than other healthcare workers, and this can adversely affect the quality of patient care. There is evidence that inhalation of essential oil from marjoram (Origanum majorana L.) has calming effects, suggesting this intervention may help to reduce the stress and anxiety of nurses working in a COVID-19 intensive care unit (ICU). This study aimed to investigate the effect of inhalation of marjoram essential oil at work on the stress and anxiety levels of nurses in a COVID-19 ICU. Methods: Nurses (n = 57) working in a single COVID-19 ICU were randomly assigned to inhale 3% marjoram essential oil (marjoram group, n = 29) or almond oil (control group, n = 28) for 2 h while at work. Mean arterial pressure (MAP), heart rate, state anxiety score, and score on a visual analog scale for anxiety (VAS-anxiety) and stress (VAS-stress) were measured before and after the intervention. Results: The two groups had similar baseline variables. MAP did not have within-group or between-group differences. Heart rate increased significantly in the marjoram group after the intervention (p = 0.031), but it remained within the normal range and the increase was not clinically meaningful. There was no significant between-group difference in the state-anxiety or VAS-anxiety score after the intervention, but the marjoram group had a significantly lower state-anxiety (p = 0.001) and VAS-anxiety (p = 0.037) score at posttest vs. pretest. The VAS-stress score was significantly lower in the marjoram group at the posttest vs. the pretest (p = 0.026). Conclusion: Nurses caring for patients in a COVID-19 ICU experience significant stress, and strategies are needed to address this important issue. Inhalation of 3% marjoram essential oil while caring for patients in a COVID-19 ICU may be a simple and effective intervention that reduces perceived stress and anxiety in nurses.Clinical Trial Registration: https://cris.nih.go.kr/, KCT0007543.

Uncertainty quantification of computational fluid dynamics-based predictions for fractional flow reserve and wall shear stress of the idealized stenotic coronary.

Introduction: In clinical practice, hemodynamics-based functional indices, such as fractional flow reserve (FFR) and wall shear stress (WSS), are useful in assessing the severity and risk of rupture of atherosclerotic lesions. Computational fluid dynamics (CFD) is widely used to predict these indices noninvasively. Method: In this study, uncertainty quantification and sensitivity analysis are performed for the computational prediction of WSS and FFR directly from 3D-0D coupled CFD simulations on idealized stenotic coronary models. Five geometric parameters (proximal, mid, and distal lengths of stenosis; reference lumen diameter; and stenosis severity) and two physiological parameters (mean aortic pressure and microcirculation resistance) are considered as uncertain input variables. Results: When employing the true values of stenosis severity and mean aortic pressure, a discernible reduction of 25% and 9.5% in the uncertainty of the computed proximal WSS, respectively. In addition, degree of stenosis, reference lumen diameter, and coronary resistance contributed the uncertainty of computed FFR, accounting for 41.2%, 31.9%, and 24.6%, respectively. Conclusion: This study demonstrated that accurate measurement of the degree of stenosis and mean aortic pressure is crucial for improving the computational prediction of WSS. In contrast, the reference lumen diameter, degree of stenosis, and coronary resistance are the most impactful parameters for FFR.

HDAC5-mediated exosomal Maspin and miR-151a-3p as biomarkers for enhancing radiation treatment sensitivity in hepatocellular carcinoma.

BACKGROUND: Tumor-derived exosomes are critical elements of the cell-cell communication response to various stimuli. This study aims to reveal that the histone deacetylase 5 (HDAC5) and p53 interaction upon radiation in hepatocellular carcinoma intricately regulates the secretion and composition of exosomes. METHODS: We observed that HDAC5 and p53 expression were significantly increased by 2 Gy and 4 Gy radiation exposure in HCC. Normal- and radiation-derived exosomes released by HepG2 were purified to investigate the exosomal components. RESULTS: We found that in the radiation-derived exosome, exosomal Maspin was notably increased. Maspin is known as an anti-angiogenic gene. The expression of Maspin was regulated at the cellular level by HDAC5, and it was elaborately regulated and released in the exosome. Radiation-derived exosome treatment caused significant inhibition of angiogenesis in HUVECs and mouse aortic tissues. Meanwhile, we confirmed that miR-151a-3p was significantly reduced in the radiation-derived exosome through exosomal miRNA sequencing, and three HCC-specific exosomal miRNAs were also decreased. In particular, miR-151a-3p induced an anti-apoptotic response by inhibiting p53, and it was shown to induce EMT and promote tumor growth by regulating p53-related tumor progression genes. In the HCC xenograft model, radiation-induced exosome injection significantly reduced angiogenesis and tumor size. CONCLUSIONS: Our present findings demonstrated HDAC5 is a vital gene of the p53-mediated release of exosomes resulting in tumor suppression through anti-cancer exosomal components in response to radiation. Finally, we highlight the important role of exosomal Maspin and mi-151a-3p as a biomarker in enhancing radiation treatment sensitivity. Therapeutic potential of HDAC5 through p53-mediated exosome modulation in radiation treatment of hepatocellular carcinoma.

Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive review of opportunities and limitations.

Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future.

Impact of unilateral and bilateral impairments on bimanual force production following stroke.

Large bilateral asymmetry and task deficits are typically observed during bimanual actions of stroke survivors. Do these abnormalities originate from unilateral impairments affecting their more-impaired limb, such as weakness and abnormal synergy, or from bilateral impairments such as incoordination of two limbs? To answer this question, 23 subjects including 10 chronic stroke survivors and 13 neurologically intact subjects participated in an experiment where they produced bimanual forces at different hand locations. The force magnitude and directional deviation of the more-impaired arm were measured for unilateral impairments and bimanual coordination across locations for bilateral impairments. Force asymmetry and task error were used to define task performance. Significant unilateral impairments were observed in subjects with stroke; the maximal force capacity of their more-impaired arm was significantly lower than that of their less-impaired arm, with a higher degree of force deviation. However, its force contribution during submaximal tasks was greater than its relative force capacity. Significant bilateral impairments were also observed, as stroke survivors modulated two forces to a larger degree across hand locations but in a less coordinated manner than control subjects did. But only unilateral, not bilateral, impairments explained a significant amount of between-subject variability in force asymmetry across subjects with stroke. Task error, in contrast, was correlated with neither unilateral nor bilateral impairments. Our results suggest that unilateral impairments of the more-impaired arm of stroke survivors mainly contribute to its reduced recruitment, but that the degree of its participation in bimanual task may be greater than their capacity as they attempt to achieve symmetry.NEW & NOTEWORTHY We studied how unilateral and bilateral impairments in stroke survivors affect their bimanual task performance. Unilateral impairments of the more-impaired limb, both weakness and loss of directional control, mainly contribute to bimanual asymmetry, but stroke survivors generally produce higher force with their more-impaired limb than their relative capacity. Bilateral force coordination was significantly impaired in stroke survivors, but its degree of impairment was not related to their unilateral impairments.

Prognostic significance of 25-hydroxyvitamin D levels in patients with peripheral T-cell lymphoma.

Vitamin D insufficiency has been linked to unfavourable outcomes in diverse malignancies. However, the prognostic significance of vitamin D levels in peripheral T-cell lymphoma (PTCL) remains unclear. In this study, we thus aimed to assess the prognostic relevance of 25-hydroxyvitamin D [25(OH)D] levels in patients newly diagnosed with PTCL. The analysis included 144 patients with PTCL treated from March 2015 to May 2020. The median 25(OH)D level was 12.2 (1.7-48.8) ng/mL, and 59 (41%) patients had vitamin D deficiency. Patients with vitamin D deficiency demonstrated significantly worse event-free survival (EFS) and overall survival (OS). In the multivariate analysis, vitamin D was independently associated with OS, with a hazard ratio of 1.66 (95% confidence interval, 1.05-2.63, p = 0.030). These findings suggest that vitamin D deficiency significantly correlates with poor survival outcomes in patients with PTCL.