Zingiber officinale promotes autophagy and apoptosis in human oral cancer through the C/EBP homologous protein.

The rhizome of Zingiber officinale (Z. officinale), commonly known as ginger, has been characterized as a potential drug candidate due to its antitumor effects. However, the chemotherapeutic effect of ginger on human oral cancer remains poorly understood. In this study, we examined the effects of an ethanol extract of Z. officinale rhizomes (ZOE) on oral cancer and identified the components responsible for its pharmacological activity. ZOE exerts its inhibitory activity in oral cancer by inducing both autophagy and apoptosis simultaneously. Mechanistically, ZOE-induced autophagy and apoptosis in oral cancer are attributed to the reactive oxygen species (ROS)-mediated endoplasmic reticulum stress response. Additionally, we identified two active components of ZOE, 1-dehydro-6-gingerdione and 8-shogaol, which were sufficient to stimulate autophagy initiation and apoptosis induction by enhancing CHOP expression. These results suggest that ZOE and its two active components induce ROS generation, upregulate CHOP, initiate autophagy and apoptosis, and hold promising therapeutics against human oral cancer.

Self-Management Using eHealth Technologies for Liver Transplant Recipients: Scoping Review.

BACKGROUND: Liver transplantation has become increasingly common as a last-resort treatment for end-stage liver diseases and liver cancer, with continually improving success rates and long-term survival rates. Nevertheless, liver transplant recipients face lifelong challenges in self-management, including immunosuppressant therapy, lifestyle adjustments, and navigating complex health care systems. eHealth technologies hold the potential to aid and optimize self-management outcomes, but their adoption has been slow in this population due to the complexity of post-liver transplant management. OBJECTIVE: This study aims to examine the use of eHealth technologies in supporting self-management for liver transplant recipients and identify their benefits and challenges to suggest areas for further research. METHODS: Following the Arksey and O'Malley methodology for scoping reviews, we conducted a systematic search of 5 electronic databases: PubMed, CINAHL, Embase, PsycINFO, and Web of Science. We included studies that (1) examined or implemented eHealth-based self-management, (2) included liver transplant recipients aged ≥18 years, and (3) were published in a peer-reviewed journal. We excluded studies that (1) were case reports, conference abstracts, editorials, or letters; (2) did not focus on the posttransplantation phase; (3) did not focus on self-management; and (4) did not incorporate the concept of eHealth or used technology solely for data collection. The quality of the selected eHealth interventions was evaluated using (1) the Template for Intervention Description and Replication guidelines and checklist and (2) the 5 core self-management skills identified by Lorig and Holman. RESULTS: Of 1461 articles, 15 (1.03%) studies were included in the final analysis. Our findings indicate that eHealth-based self-management strategies for adult liver transplant recipients primarily address lifestyle management, medication adherence, and remote monitoring, highlighting a notable gap in alcohol relapse interventions. The studies used diverse technologies, including mobile apps, videoconferencing, and telehealth platforms, but showed limited integration of decision-making or resource use skills essential for comprehensive self-management. The reviewed studies highlighted the potential of eHealth in enhancing individualized health care, but only a few included collaborative features such as 2-way communication or tailored goal setting. While adherence and feasibility were generally high in many interventions, their effectiveness varied due to diverse methodologies and outcome measures. CONCLUSIONS: This scoping review maps the current literature on eHealth-based self-management support for liver transplant recipients, assessing its potential and challenges. Future studies should focus on developing predictive models and personalized eHealth interventions rooted in patient-generated data, incorporating digital human-to-human interactions to effectively address the complex needs of liver transplant recipients. This review emphasizes the need for future eHealth self-management research to address the digital divide, especially with the aging liver transplant recipient population, and ensure more inclusive studies across diverse ethnicities and regions.

Age and Gender Differences in the Borrowing of Personal Stories.

BACKGROUND: In prior research, about half of undergraduate students claimed to have "borrowed" a story, by telling someone else's autobiographical memory as if it was their own. Given that borrowing stories often involves intentional fabrication, and given that there are age-related declines in lying, we hypothesized that reports of intentionally borrowing stories should decline with age. METHODS: We recruited participants who ranged in age from 18 to 86 and asked them to complete an online retrospective survey about borrowing stories. RESULTS: Consistent with our hypothesis, older age was associated with lower reports of borrowing stories. Furthermore, among people who did report borrowing a story, older age was associated with less frequent story borrowing and less recent story borrowing. CONCLUSION: These findings highlight the importance of using age-diverse samples when examining social memory phenomena. Findings based upon undergraduate students do not always replicate in other age groups.

The comprehensive expression of BCL2 family genes determines the prognosis of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a prevalent and aggressive non-Hodgkin's lymphoma, and 40% of patients succumb to death. Despite numerous clinical trials aimed at developing treatment strategies beyond the conventional R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) regimen, there have been no positive results thus far. Although the selective BCL2 inhibitor venetoclax has shown remarkable efficacy in chronic lymphocytic leukemia, its therapeutic effect in DLBCL was limited. We hypothesized that the limited therapeutic effect of venetoclax in DLBCL may be attributed to the complex expression and interactions of BCL2 family members, including BCL2. Therefore, we aimed to comprehensively analyze the expression patterns of BCL2 family members in DLBCL. We analyzed 157 patients with de novo DLBCL diagnosed at Asan Medical Center and Ajou University Hospital. The mRNA expression levels of BCL2 family members were quantified using the NanoString technology. BCL2 family members showed distinct heterogeneous expression patterns both intra- and inter-patient. Using unsupervised hierarchical cluster analysis, we were able to classify patients with similar BCL2 family expression pattern and select groups with clear prognostic features, C1 and C6. In the group with the best prognosis, C1, the expression of pro-apoptotic and pro-apoptotic BH3-only group gene expressions were increased, while anti-apoptotic group expression was significantly increased in both C1 and C6. Based on this, we generated the BCL2 signature score using the expression of pro-apoptotic genes BOK and BCL2L15, and anti-apoptotic gene BCL2. The BCL2 signature score 0 had the best prognosis, score 1/2 had intermediate prognosis, and score 3 had the worst prognosis (EFS, p = 0.0054; OS, p = 0.0011). Multivariate analysis, including COO and IPI, showed that increase in the BCL2 signature score was significantly associated with poor prognosis for EFS, independent of COO and IPI. The BCL2 signature score we proposed in this study provides information on BCL2 family deregulation based on the equilibrium of pro-versus anti-apoptotic BCL2 family, which can aid in the development of new treatment strategies for DLBCL in the future.

Distinct features of B cell receptors in neuromyelitis optica spectrum disorder among CNS inflammatory demyelinating diseases.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) stands out among CNS inflammatory demyelinating diseases (CIDDs) due to its unique disease characteristics, including severe clinical attacks with extensive lesions and its association with systemic autoimmune diseases. We aimed to investigate whether characteristics of B cell receptors (BCRs) differ between NMOSD and other CIDDs using high-throughput sequencing. METHODS: From a prospective cohort, we recruited patients with CIDDs and categorized them based on the presence and type of autoantibodies: NMOSD with anti-aquaporin-4 antibodies, myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) with anti-myelin oligodendrocyte glycoprotein antibodies, double-seronegative demyelinating disease (DSN), and healthy controls (HCs). The BCR features, including isotype class, clonality, somatic hypermutation (SHM), and the third complementarity-determining region (CDR3) length, were analyzed and compared among the different disease groups. RESULTS: Blood samples from 33 patients with CIDDs (13 NMOSD, 12 MOGAD, and 8 DSN) and 34 HCs were investigated for BCR sequencing. Patients with NMOSD tended to have more activated BCR features compare to the other disease groups. They showed a lower proportion of unswitched isotypes (IgM and IgD) and a higher proportion of switched isotypes (IgG), increased clonality of BCRs, higher rates of SHM, and shorter lengths of CDR3. Notably, advanced age was identified as a clinical factor associated with these activated BCR features, including increased levels of clonality and SHM rates in the NMOSD group. Conversely, no such clinical factors were found to be associated with activated BCR features in the other CIDD groups. CONCLUSIONS: NMOSD patients, among those with CIDDs, displayed the most pronounced B cell activation, characterized by higher levels of isotype class switching, clonality, SHM rates, and shorter CDR3 lengths. These findings suggest that B cell-mediated humoral immune responses and characteristics in NMOSD patients are distinct from those observed in the other CIDDs, including MOGAD. Age was identified as a clinical factor associated with BCR activation specifically in NMOSD, implying the significance of persistent B cell activation attributed to anti-aquaporin-4 antibodies, even in the absence of clinical relapses throughout an individual's lifetime.

Apoptotic activity of genipin in human oral squamous cell carcinoma in vitro by regulating STAT3 signaling.

Genipin, a natural compound derived from the fruit of Gardenia jasminoides Ellis, was reported to have activity against various cancer types. In this study, we determined the underlying mechanism for genipin-induced cell death in human oral squamous cell carcinoma (OSCC). The growth-inhibitory effects of genipin in human OSCC cells was examined by the Cell Counting Kit-8 and soft agar assays. The effects of genipin on apoptosis were assessed by nuclear morphological changes by 4',6-diamidino-2-phenylindole staining, measurement of the sub-G1 population, and Annexin V-fluorescein isothiocyanate/propidium iodide double staining. The underlying mechanism of genipin activity was analyzed by western blot analysis, subcellular fractionation of the nucleus and cytoplasm, immunocytochemistry, and quantitative real-time polymerase chain reaction. Genipin inhibited the growth of OSCC cells and induced apoptosis, which was mediated by a caspase-dependent pathway. Genipin reduced the phosphorylation of signal transducer and activator of transcription 3 (STAT3) at Tyr705 and its nuclear localization. Furthermore, inhibition of p-STAT3Tyr705 levels following genipin treatment was required for the reduction of survivin and myeloid cell leukemia-1 (Mcl-1) expression, leading to apoptotic cell death. The genipin-mediated reduction in survivin and Mcl-1 expression was caused by transcriptional and/or posttranslational regulatory mechanisms. The results provide insight into the regulatory mechanism by which genipin induces apoptotic cell death through the abrogation of nuclear STAT3 phosphorylation and suggest that genipin may represent a potential therapeutic option for the treatment of human OSCC.

PRMT7 ablation in cardiomyocytes causes cardiac hypertrophy and fibrosis through ß-catenin dysregulation.

Angiotensin II (AngII) has potent cardiac hypertrophic effects mediated through activation of hypertrophic signaling like Wnt/ß-Catenin signaling. In the current study, we examined the role of protein arginine methyltransferase 7 (PRMT7) in cardiac function. PRMT7 was greatly decreased in hypertrophic hearts chronically infused with AngII and cardiomyocytes treated with AngII. PRMT7 depletion in rat cardiomyocytes resulted in hypertrophic responses. Consistently, mice lacking PRMT7 exhibited the cardiac hypertrophy and fibrosis. PRMT7 overexpression abrogated the cellular hypertrophy elicited by AngII, while PRMT7 depletion exacerbated the hypertrophic response caused by AngII. Similar with AngII treatment, the cardiac transcriptome analysis of PRMT7-deficient hearts revealed the alteration in gene expression profile related to Wnt signaling pathway. Inhibition of PRMT7 by gene deletion or an inhibitor treatment enhanced the activity of ß-catenin. PRMT7 deficiency decreases symmetric dimethylation of ß-catenin. Mechanistic studies reveal that methylation of arginine residue 93 in ß-catenin decreases the activity of ß-catenin. Taken together, our data suggest that PRMT7 is important for normal cardiac function through suppression of ß-catenin activity.

Effectiveness and Adverse Events of Nirmatrelvir/Ritonavir Versus Molnupiravir for COVID-19 in Outpatient Setting: Multicenter Prospective Observational Study.

BACKGROUND: In this study, we aimed to compare the effectiveness and adverse reactions of nirmatrelvir/ritonavir and molnupiravir in high-risk outpatients with coronavirus disease 2019 (COVID-19). METHODS: This multicenter prospective observational study evaluated the rate of hospitalization, death, and adverse events within 28 days of oral antiviral agent prescription (molnupiravir, n = 240; nirmatrelvir/ritonavir, n = 240) to 480 nonhospitalized adult patients with COVID-19 from August 2, 2022 to March 31, 2023. RESULTS: Patients receiving molnupiravir had a higher prevalence of comorbidities (85.8% vs. 70.4%; P < 0.001) and a higher Charlson comorbidity index (2.8 ± 1.4 vs. 2.5 ± 1.5; P = 0.009) than those receiving nirmatrelvir/ritonavir. Three patients required hospitalization (nirmatrelvir/ritonavir group, n = 1 [0.4%]; molnupiravir group, n = 2 [0.8%]; P = 1.000). Nirmatrelvir/ritonavir was associated with a higher risk of adverse events than molnupiravir (odds ratio [OR], 1.96; 95% confidence interval [CI], 1.27-3.03), especially for patients aged 65 years and older (OR, 3.04; 95% CI, 1.71-5.39). The severity of adverse events in both groups was mild to moderate and improved after discontinuation of medication. In the molnupiravir group, age ≥ 65 years (OR, 0.43 95% CI, 0.22-0.86) and appropriate vaccination (OR, 0.37; 95% CI, 0.15-0.91) reduced the occurrence of adverse events. CONCLUSION: The rates of hospitalization and death were low and not significantly different between high-risk patients who received either nirmatrelvir/ritonavir or molnupiravir. Although adverse events were more frequent with nirmatrelvir/ritonavir than with molnupiravir, none were severe. Nirmatrelvir/ritonavir can be safely used to treat COVID-19, while molnupiravir could be considered as an alternative treatment option for high-risk groups.

Mothers' use of touch across infants' development and its implications for word learning: Evidence from Korean dyadic interactions.

Caregivers' touches that occur alongside words and utterances could aid in the detection of word/utterance boundaries and the mapping of word forms to word meanings. We examined changes in caregivers' use of touches with their speech directed to infants using a multimodal cross-sectional corpus of 35 Korean mother-child dyads across three age groups of infants (8, 14, and 27 months). We tested the hypothesis that caregivers' frequency and use of touches with speech change with infants' development. Results revealed that the frequency of word/utterance-touch alignment as well as word + touch co-occurrence is highest in speech addressed to the youngest group of infants. Thus, this study provides support for the hypothesis that caregivers' use of touch during dyadic interactions is sensitive to infants' age in a way similar to caregivers' use of speech alone and could provide cues useful to infants' language learning at critical points in early development.

Quantum Neural Network Based Distinguisher on SPECK-32/64.

As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr's work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network's various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper.

Deep-Learning-Based Cryptanalysis of Lightweight Block Ciphers Revisited.

With the development of artificial intelligence, deep-learning-based cryptanalysis has been actively studied. There are many cryptanalysis techniques. Among them, cryptanalysis was performed to recover the secret key used for cryptography encryption using known plaintext. In this paper, we propose a cryptanalysis method based on state-of-art deep learning technologies (e.g., residual connections and gated linear units) for lightweight block ciphers (e.g., S-DES, S-AES, and S-SPECK). The number of parameters required for training is significantly reduced by 93.16%, and the average of bit accuracy probability increased by about 5.3% compared with previous the-state-of-art work. In addition, cryptanalysis for S-AES and S-SPECK was possible with up to 12-bit and 6-bit keys, respectively. Through this experiment, we confirmed that the-state-of-art deep-learning-based key recovery techniques for modern cryptography algorithms with the full round and the full key are practically infeasible.

Application of convolutional neural network for analyzing hepatic fibrosis in mice.

Recently, with the development of computer vision using artificial intelligence (AI), clinical research on diagnosis and prediction using medical image data has increased. In this study, we applied AI methods to analyze hepatic fibrosis in mice to determine whether an AI algorithm can be used to analyze lesions. Whole slide image (WSI) Sirius Red staining was used to examine hepatic fibrosis. The Xception network, an AI algorithm, was used to train normal and fibrotic lesion identification. We compared the results from two analyses, that is, pathologists' grades and researchers' annotations, to observe whether the automated algorithm can support toxicological pathologists efficiently as a new apparatus. The accuracies of the trained model computed from the training and validation datasets were greater than 99%, and that obtained by testing the model was 100%. In the comparison between analyses, all analyses showed significant differences in the results for each group. Furthermore, both normalized fibrosis grades inferred from the trained model annotated the fibrosis area, and the grades assigned by the pathologists showed significant correlations. Notably, the deep learning algorithm derived the highest correlation with the pathologists' average grade. Owing to the correlation outcomes, we conclude that the trained model might produce results comparable to those of the pathologists' grading of the Sirius Red-stained WSI fibrosis. This study illustrates that the deep learning algorithm can potentially be used for analyzing fibrotic lesions in combination with Sirius Red-stained WSIs as a second opinion tool in non-clinical research.

Modulation of IKs channel-PIP2 interaction by PRMT1 plays a critical role in the control of cardiac repolarization.

Recent studies have shown that protein arginine methyltransferase 1 (PRMT1) is highly expressed in the human heart, and loss of PRMT1 contributes to cardiac remodeling in the heart failure. However, the functional importance of PRMT1 in cardiac ion channels remains uncertain. The slow activating delayed rectifier K+ (IKs ) channel is a cardiac K+ channel composed of KCNQ1 and KCNE1 subunits and is a new therapeutic target for treating lethal arrhythmias in many cardiac pathologies, especially heart failure. Here, we demonstrate that PRMT1 is a critical regulator of the IKs channel and cardiac rhythm. In the guinea pig ventricular myocytes, treatment with furamidine, a PRMT1-specific inhibitor, prolonged the action potential duration (APD). We further show that this APD prolongation was attributable to IKs reduction. In HEK293T cells expressing human KCNQ1 and KCNE1, inhibiting PRMT1 via furamidine reduced IKs and concurrently decreased the arginine methylation of KCNQ1, a pore-forming α-subunit. Evidence presented here indicates that furamidine decreased IKs mainly by lowering the affinity of IKs channels for the membrane phospholipid, phosphatidylinositol 4,5-bisphosphate (PIP2 ), which is crucial for pore opening. Finally, applying exogenous PIP2 to cardiomyocytes prevented the furamidine-induced IKs reduction and APD prolongation. Taken together, these results indicate that PRMT1 positively regulated IKs activity through channel-PIP2 interaction, thereby restricting excessive cardiac action potential.

Early Bactericidal Activity of Delpazolid (LCB01-0371) in Patients with Pulmonary Tuberculosis.

Delpazolid, an oxazolidinone, has been studied in non-clinical studies of efficacy and toxicity and Phase 1 clinical studies. Delpazolid has in vitro activity against Gram-positive bacteria, including Mycobacterium tuberculosis. This study evaluated the bactericidal activity, safety, and pharmacokinetics of delpazolid in patients with pulmonary tuberculosis (TB). Seventy-nine subjects, aged 19 to 75 years with newly diagnosed smear-positive TB with no prior treatment for the current episode and no confirmed resistance to rifampin or isoniazid, were randomized to receive delpazolid 800 mg once a day (QD), 400 mg twice a day (BID), 800 mg BID or 1,200 mg QD or an active control of isoniazid, rifampin, pyrazinamide, and ethambutol (HRZE) or linezolid 600 mg BID. The primary endpoint was the average daily reduction in log transformed bacterial load, assessed on 7H11 solid-media culture, from days 0 to 14. The average daily decline in log-CFU was 0.044 ± 0.016, 0.053 ± 0.017, 0.043 ± 0.016, and 0.019 ± 0.017, for the delpazolid 800 mg QD, 400 mg BID, 800 mg BID, and the 1,200 mg QD groups, respectively. The average daily decline in log-CFU was 0.192 ± 0.028 for the HRZE group and 0.154 ± 0.023 for the linezolid 600 mg BID group. Three serious adverse events (SAE) were reported, one each in the delpazolid 400 mg BID group (death due to worsening of TB at day 2), the HRZE group (hospitalization due to pleural effusion) and the linezolid group (hyperkalemia); none of the SAEs were assessed as related to study drugs. This study has been registered at ClinicalTrials.gov with registration number NCT02836483.

Surface Functionalization of Enzyme-Coronated Gold Nanoparticles with an Erythrocyte Membrane for Highly Selective Glucose Assays.

Colorimetric glucose sensors using enzyme-coronated gold nanoparticles have been developed for high-throughput assays to monitor the blood glucose levels of diabetic patients. Although those sensors have shown sensitivity and wide linear detection ranges, they suffer from poor selectivity and stability in detecting blood glucose, which has limited their practical use. To address this limitation, herein, we functionalized glucose-oxidase-coronated gold nanoparticles with an erythrocyte membrane (EM-GOx-GNPs). Because the erythrocyte membrane (EM) selectively facilitates the permeation of glucose via glucose transporter-1 (GLUT1), the functionalization of GOx-GNPs with EM improved the stability, selectivity (3.3- to 15.8-fold higher), and limit of detection (LOD). Both membrane proteins, GLUT1 and aquaporin-1 (AQP1), on EM were shown to be key components for selective glucose detection by treatment with their inhibitors. Moreover, we demonstrated the stability of EM-GOx-GNPs in high-antioxidant-concentration conditions, under long-term storage (∼4 weeks) and a freeze-thaw cycle. Selectivity of the EM-GOx-GNPs against other saccharides was increased, which improved the LOD in phosphate-buffered saline and human serum. Our results indicated that the functionalization of colorimetric glucose sensors with EM is beneficial for improving selectivity and stability, which may make them candidates for use in a practical glucose sensor.

Longitudinal follow-up of serum biomarkers in patients with neuromyelitis optica spectrum disorder.

BACKGROUND: Recently, several serum biomarkers have been proposed in Neuromyelitis Optica Spectrum Disorders (NMOSD) to monitor disease activity. OBJECTIVE: The objective of the study is to evaluate the longitudinal clinical value of serum biomarkers in patients with NMOSD. METHODS: We prospectively recruited consecutive NMOSD patients with anti-aquaporin-4 antibody and obtained serum samples at enrollment, after 6-12 months of follow-up (main period), and at attacks. Using single-molecule array assays, we evaluated longitudinal changes of serum neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and GFAP/NfL levels. RESULTS: Overall, 64 patients (58 women) were enrolled (age: 51 years, disease duration: 6.7 years) and 133 samples were obtained. Among patients who did not develop new attacks during the main period (n = 62), serum levels of NfL, GFAP, and GFAP/NfL were significantly decreased over time in patients with attacks (<2 months) at enrollment (n = 14 (23%)), whereas serum NfL and GFAP levels gradually increased in the others (n = 48 (77%)). During the study, five (8%) patients developed new attacks; only serum GFAP levels increased consistently upon these events compared with baseline levels. To differentiate attacks from remissions, serum GFAP levels showed the largest area under the receiver operating characteristic curve (0.876, 95% confidence interval: 0.801-0.951). CONCLUSION: Among NfL, GFAP, and GFAP/NfL, serum GFAP might be the most appropriate for monitoring NMOSD longitudinally, which warrants future confirming studies.

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats.

Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we applied Mask Regions with Convolution Neural Network (Mask R-CNN), a deep learning model that uses instance segmentation, to detect hepatic fibrosis induced by N-nitrosodimethylamine (NDMA) in Sprague-Dawley rats. From 51 WSIs, we collected 2011 cropped images with hepatic fibrosis annotations. Training and detection of hepatic fibrosis via artificial intelligence methods was performed using Tensorflow 2.1.0, powered by an NVIDIA 2080 Ti GPU. From the test process using tile images, 95% of model accuracy was verified. In addition, we validated the model to determine whether the predictions by the trained model can reflect the scoring system by the pathologists at the WSI level. The validation was conducted by comparing the model predictions in 18 WSIs at 20× and 10× magnifications with ground truth annotations and board-certified pathologists. Predictions at 20× showed a high correlation with ground truth (R2 = 0.9660) and a good correlation with the average fibrosis rank by pathologists (R2 = 0.8887). Therefore, the Mask R-CNN algorithm is a useful tool for detecting and quantifying pathological findings in nonclinical studies.

The effect of the multimodal intervention including an automatic notification of catheter days on reducing central line-related bloodstream infection: a retrospective, observational, quasi-experimental study.

BACKGROUND: A central venous catheter (CVC) is an important medical device, but it could be preceding infection and the risk of central line-associated bloodstream infection (CLABSI). CLABSI is a common healthcare-associated infection but results in high cost and mortality; therefore, various efforts to reduce CLABSI have been attempted. METHODS: This is a retrospective, observational, quasi-experimental study in the intensive care unit (ICU) of a single tertiary care hospital. We reviewed and analysed the data of CLABSI rates and days from the insertion to the removal of the temporary CVC between January 2018 and June 2021 with transient periods over 9 months. Sequentially, all patients with the CVC in the ICU underwent the following interventions: maximal barrier precaution, automatic notification of catheter days and 2% chlorhexidine gluconate bathing. A segmented regression analysis of interrupted time series was conducted to compare the CLABSI rates before and after the introduction of multimodal interventions. During study periods, the impact of interventions on CLABSI was evaluated using multivariate logistic regression analyses. RESULTS: A total of 76,504 patient-days, 28,312 catheter days and 66 CLABSI cases were reviewed in ICU-hospitalised patients. As additional interventions, the CLABSI rate declined from 3.1 per 1000 CVC days to 1.2 per 1000 CVC days in post-interventions. In the pre-intervention and post-intervention periods, 4146 patents had one more short-term CVC. In the multivariate logistic regression analyses, multimodal intervention was one of determinants reducing CLABSI rates (odds ratio (OR), 0.52 [95% confidence interval {CI}, 0.28-0.94]). Indwelling time of CVC over 10 days was the risk factor for CLABSI rates (OR, 6.27 [95% CI, 3.36-12.48]). Of the three interventions, the automatic notification of catheter days was associated with decreased median monthly total CVC days and duration of CVC days per patient. CONCLUSIONS: Multidisciplinary and evidence-based interventions could lead to a decrease in the CLABSI rates. Moreover, the automatic notification of catheter days of the electronic medical healthcare system has shortened the time of indwelling CVC.

An Indoor-Monitoring LiDAR Sensor for Patients with Alzheimer Disease Residing in Long-Term Care Facilities.

This paper introduces an indoor-monitoring LiDAR sensor for patients with Alzheimer disease residing in long-term care facilities (LTCFs), and this sensor exploits an optoelectronic analog front-end (AFE) to detect light signals from targets by utilizing on-chip avalanche photodiodes (APDs) realized in a 180 nm CMOS process and a neural processing unit (NPU) used for motion detection and decisions, especially for incidents of falls occurring in LTCFs. The AFE consists of an on-chip CMOS P+/N-well APD, a linear-mode transimpedance amplifier, a post-amplifier, and a time-to-digital converter, whereas the NPU exploits network sparsity and approximate processing elements for low-power operation. This work provides a potential solution of low-cost, low-power, indoor-monitoring LiDAR sensors for patients with Alzheimer disease in LTCFs.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer.

OBJECTIVES: Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer. METHODS: In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance. RESULTS: Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94. CONCLUSIONS: MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer. KEY POINTS: • MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.