Development of a Predictive Model for Potentially Inappropriate Medications in Older Patients with Cardiovascular Disease.

BACKGROUND: Older patients with cardiovascular disease (CVD) are highly susceptible to adverse drug reactions due to age-related physiological changes and the presence of multiple comorbidities, polypharmacy, and potentially inappropriate medications (PIMs). OBJECTIVE: This study aimed to develop a predictive model to identify the use of PIMs in older patients with CVD. METHODS: Data from 2012 to 2021 from the Changhua Christian Hospital Clinical Research Database (CCHRD) and the Kaohsiung Medical University Hospital Research Database (KMUHRD) were analyzed. Participants over the age of 65 years with CVD diagnoses were included. The CCHRD data were randomly divided into a training set (80% of the database) and an internal validation set (20% of the database), while the KMUHRD data served as an external validation set. The training set was used to construct the prediction models, and both validation sets were used to validate the proposed models. RESULTS: A total of 48,569 patients were included. Comprehensive data analysis revealed significant associations between the use of PIMs and clinical factors such as total cholesterol, glycated hemoglobin (HbA1c), creatinine, and uric acid levels, as well as the presence of diabetes, hypertension, and cerebrovascular accidents. The predictive models demonstrated moderate power, indicating the importance of these factors in assessing the risk of PIMs. CONCLUSIONS: This study developed predictive models that improve understanding of the use of PIMs in older patients with CVD. These models may assist clinicians in making informed decisions regarding medication safety.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Impact of SARS-CoV-2 pandemic and lockdown measures on microbial keratitis in Taiwan.

PURPOSE: This study aimed to evaluate how the SARS-CoV-2 pandemic and associated lockdown measures influenced microbial keratitis in Taiwan by comparing demographic data, predisposing factors, pathogen profiles, and treatment outcomes in 2019 and 2020. METHODS: Data from patients diagnosed with microbial keratitis at National Chung Kung University Hospital between January 2019 and December 2020 were examined, focusing on patient demographics, predisposing factors, isolated pathogens, antibiotic usage, and clinical progress. RESULTS: No significant differences were found in patient sex, laterality, or average age between the two years. Predisposing factors, such as contact lens use and chronic ocular/systemic disorders, remained unchanged. While fungal isolates slightly increased during the lockdown, bacterial isolates remained consistent. Medical treatment effectiveness, treatment strategies, and antibiotic susceptibility for common bacteria showed no significant alterations. CONCLUSION: Despite the challenges posed by the SARS-CoV-2 pandemic and lockdown measures, this study revealed minimal changes in microbial keratitis trends in Taiwan. This highlights the importance of maintaining access to medical care during crises and offers insights into potential treatment strategies for patients facing difficulties in receiving timely care. Further research should investigate the pandemic's impact on healthcare access and patient outcomes in various populations and regions.

Risk Factors for Isolated Sphenoid Sinusitis after Endoscopic Endonasal Transsphenoidal Pituitary Surgery.

(1) Background: Transsphenoidal pituitary surgery can be conducted via microscopic or endoscopic approaches, and there has been a growing preference for the latter in recent years. However, the occurrence of rare complications such as postoperative sinusitis remains inadequately documented in the existing literature. (2) Methods: To address this gap, we conducted a comprehensive retrospective analysis of medical records spanning from 2018 to 2023, focusing on patients who underwent transsphenoidal surgery for pituitary neuroendocrine tumors (formerly called pituitary adenoma). Our study encompassed detailed evaluations of pituitary function and MRI imaging pre- and postsurgery, supplemented by transnasal endoscopic follow-up assessments at the otolaryngology outpatient department. Risk factors for sinusitis were compared using univariate and multivariate logistic regression analyses. (3) Results: Out of the 203 patients included in our analysis, a subset of 17 individuals developed isolated sphenoid sinusitis within three months postoperation. Further scrutiny of the data revealed significant associations between certain factors and the occurrence of postoperative sphenoid sinusitis. Specifically, the classification of the primary tumor emerged as a notable risk factor, with patients exhibiting nonfunctioning pituitary neuroendocrine tumors with 3.71 times the odds of developing sinusitis compared to other tumor types. Additionally, postoperative cortisol levels demonstrated a significant inverse relationship, with lower cortisol levels correlating with an increased risk of sphenoid sinusitis postsurgery. (4) Conclusions: In conclusion, our findings underscore the importance of considering tumor classification and postoperative cortisol levels as potential predictors of postoperative sinusitis in patients undergoing transsphenoidal endoscopic pituitary surgery. These insights offer valuable guidance for clinicians in identifying at-risk individuals and implementing tailored preventive and management strategies to mitigate the occurrence and impact of sinusitis complications in this patient population.

Mechanistic and Kinetic Insights into Cellular Uptake of Biomimetic Dinitrosyl Iron Complexes and Intracellular Delivery of NO for Activation of Cytoprotective HO-1.

Dinitrosyl iron unit (DNIU), [Fe(NO)2], is a natural metallocofactor for biological storage, delivery, and metabolism of nitric oxide (NO). In the attempt to gain a biomimetic insight into the natural DNIU under biological system, in this study, synthetic dinitrosyl iron complexes (DNICs) [(NO)2Fe(µ-SCH2CH2COOH)2Fe(NO)2] (DNIC-COOH) and [(NO)2Fe(µ-SCH2CH2COOCH3)2Fe(NO)2] (DNIC-COOMe) were employed to investigate the structure-reactivity relationship of mechanism and kinetics for cellular uptake of DNICs, intracellular delivery of NO, and activation of cytoprotective heme oxygenase (HO)-1. After rapid cellular uptake of dinuclear DNIC-COOMe through a thiol-mediated pathway (tmax = 0.5 h), intracellular assembly of mononuclear DNIC [(NO)2Fe(SR)(SCys)]n-/[(NO)2Fe(SR)(SCys-protein)]n- occurred, followed by O2-induced release of free NO (tmax = 1-2 h) or direct transfer of NO to soluble guanylate cyclase, which triggered the downstream HO-1. In contrast, steady kinetics for cellular uptake of DNIC-COOH via endocytosis (tmax = 2-8 h) and for intracellular release of NO (tmax = 4-6 h) reflected on the elevated activation of cytoprotective HO-1 (∼50-150-fold change at t = 3-10 h) and on the improved survival of DNIC-COOH-primed mesenchymal stem cell (MSC)/human corneal endothelial cell (HCEC) under stressed conditions. Consequently, this study unravels the bridging thiolate ligands in dinuclear DNIC-COOH/DNIC-COOMe as a switch to control the mechanism, kinetics, and efficacy for cellular uptake of DNICs, intracellular delivery of NO, and activation of cytoprotective HO-1, which poses an implication on enhanced survival of postengrafted MSC for advancing the MSC-based regenerative medicine.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer.

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

Protocol for assessing total antioxidant capacity in minimal volumes of varying clinical human samples.

Total antioxidant capacity (TAC), representative of the capacity to combat oxidative stress, is closely linked to numerous diseases. Here, we present a protocol for measuring TAC using minimal samples that are stable across varying pH levels and at room temperature. We describe steps for preparing and loading samples and working solutions and conducting and analyzing the colorimetric reaction. Sample sources include aqueous humor, vitreous, tears, and plasma, which allow the protocol to be used in various clinical diagnostic settings. For complete details on the use and execution of this protocol, please refer to publications by Tsao et al. (2022).1,2.

Inertial Measurement Unit-Based Romberg Test for Assessing Adults With Vestibular Hypofunction.

This work aims to explore the utility of wearable inertial measurement units (IMUs) for quantifying movement in Romberg tests and investigate the extent of movement in adults with vestibular hypofunction (VH). A cross-sectional study was conducted at an academic tertiary medical center between March 2021 and April 2022. Adults diagnosed with unilateral vestibular hypofunction (UVH) or bilateral vestibular hypofunction (BVH) were enrolled in the VH group. Healthy controls (HCs) were recruited from community or outpatient clinics. The IMU-based instrumented Romberg and tandem Romberg tests on the floor were applied to both groups. The primary outcomes were kinematic body metrics (maximum acceleration [ACC], mean ACC, root mean square [RMS] of ACC, and mean sway velocity [MV]) along the medio-lateral (ML), cranio-caudal (CC), and antero-posterior (AP) axes. A total of 31 VH participants (mean age, 33.48 [SD 7.68] years; 19 [61%] female) and 31 HCs (mean age, 30.65 [SD 5.89] years; 18 [58%] female) were recruited. During the eyes-closed portion of the Romberg test, VH participants demonstrated significantly higher maximum ACC and increased RMS of ACC in head movement, as well as higher maximum ACC in pelvic movement along the ML axis. In the same test condition, individuals with BVH exhibited notably higher maximum ACC and RMS of ACC along the ML axis in head and pelvic movements compared with HCs. Additionally, BVH participants exhibited markedly increased maximum ACC along the ML axis in head movement during the eyes-open portion of the tandem Romberg test. Conversely, no significant differences were found between UVH participants and HCs in the assessed parameters. The instrumented Romberg and tandem Romberg tests characterized the kinematic differences in head, pelvis, and ankle movement between VH and healthy adults. The findings suggest that these kinematic body metrics can be useful for screening BVH and can provide goals for vestibular rehabilitation.

Tofacitinib and pirfenidone as rescue therapies for severe COVID-19 in a patient with previously stable interstitial lung disease associated with Sjögren syndrome.

Coronavirus disease 2019 (COVID-19) can lead to pulmonary fibrosis due to the inflammatory process in the lung, resulting in a series of respiratory consequences. Patients with underlying systemic diseases or pre-existing pulmonary diseases are particularly at risk of severe respiratory distress and persistent pulmonary abnormalities. Pirfenidone, a well-known anti-fibrotic agent recognized for its therapeutic effect on idiopathic pulmonary fibrosis, could be a feasible option in severe COVID-19 cases given the similar pathophysiological features shared with interstitial lung diseases. In this paper, we share our experience of early administration of pirfenidone in combination with tofacitinib in a 61-year-old female patient with severe COVID-19 pneumonia. Pirfenidone was initiated because of persistent dependence on high-flow oxygen support and even the requirement for mechanical ventilation due to disease progression after initial standard COVID-19 treatment. The patient was successfully extubated 15 days after the initiation of pirfenidone, and 13 days after extubation, she was completely weaned off supplemental oxygen. A series of chest radiographs and computed tomography scans demonstrated notable improvements in her lung condition. We propose a strategy of using pirfenidone plus tofacitinib as a rescue therapy in the management of patients with severe COVID-19.

Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection.

OBJECTIVE: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. METHODS AND PROCEDURES: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. RESULTS: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ([Formula: see text]) compared to prior GLP processing. CONCLUSION: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. CLINICAL IMPACT: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.

Multi-Task Learning U-Net for Functional Shoulder Sub-Task Segmentation.

The assessment of a frozen shoulder (FS) is critical for evaluating outcomes and medical treatment. Analysis of functional shoulder sub-tasks provides more crucial information, but current manual labeling methods are time-consuming and prone to errors. To address this challenge, we propose a deep multi-task learning (MTL) U-Net to provide an automatic and reliable functional shoulder sub-task segmentation (STS) tool for clinical evaluation in FS. The proposed approach contains the main task of STS and the auxiliary task of transition point detection (TPD). For the main STS task, a U-Net architecture including an encoder-decoder with skip connection is presented to perform shoulder sub-task classification for each time point. The auxiliary TPD task uses lightweight convolutional neural networks architecture to detect the boundary between shoulder sub-tasks. A shared structure is implemented between two tasks and their objective functions of them are optimized jointly. The fine-grained transition-related information from the auxiliary TPD task is expected to help the main STS task better detect boundaries between functional shoulder sub-tasks. We conduct the experiments using wearable inertial measurement units to record 815 shoulder task sequences collected from 20 healthy subjects and 43 patients with FS. The experimental results present that the deep MTL U-Net can achieve superior performance compared to using single-task models. It shows the effectiveness of the proposed method for functional shoulder STS. The code has been made publicly available at https://github.com/RobinChu9890/MTL-U-Net-for-Functional-Shoulder-STS.Clinical Relevance- This work provides an automatic and reliable functional shoulder sub-task segmentation tool for clinical evaluation in frozen shoulder.

Air pollution after acute bronchiolitis is a risk factor for preschool asthma: a nested case-control study.

BACKGROUND: Acute bronchiolitis and air pollution are both risk factor of pediatric asthma. This study aimed to assess subsequent exposure to air pollutants related to the inception of preschool asthma in infants with acute bronchiolitis. This study aimed to assess subsequent exposure to air pollutants related to the inception of preschool asthma in infants with acute bronchiolitis. METHODS: A nested case-control retrospective study was performed at the Kaohsiung Medical University Hospital systems between 2009 and 2019. The average concentration of PM10, PM2.5, SO2, NO, NO2, and NOX was collected for three, six, and twelve months after the first infected episode. Adjusted regression models were employed to evaluate the association between asthma and air pollution exposure after bronchiolitis. RESULTS: Two thousand six hundred thirty-seven children with acute bronchiolitis were included. Exposure to PM10, PM2.5, SO2, NO, NO2, and NOX in the three, six, and twelve months following an episode of bronchiolitis was found to significantly increase the risk of preschool asthma in infants with a history of bronchiolitis.(OR, 95%CI: PM10 = 1.517-1.559, 1.354-1.744; PM2.5 = 2.510-2.603, 2.148-3.061; SO2 = 1.970-2.040, 1.724-2.342; ; NO = 1.915-1.950, 1.647-2.272; NO2 = 1.915-1.950, 1.647-2.272; NOX = 1.752-1.970, 1.508-2.252) In a sensitive analysis of hospitalized infants, only PM10, PM2.5, SO2, and NO were found to have significant effects during all time periods. (OR, 95%CI: PM10 = 1.613-1.650, 1.240-2.140; PM2.5 = 2.208-2.286, 1.568-3.061; SO2 = 1.679-1.622, 1.197-2.292; NO = 1.525-1.557, 1.094-2.181) CONCLUSION: The presence of ambient PM10, PM2.5, SO2 and NO in the three, six, and twelve months following an episode of acute bronchiolitis has been linked to the development of preschool asthma in infants with a history of acute bronchiolitis.

Polymer architecture dictates multiple relaxation processes in soft networks with two orthogonal dynamic bonds.

Materials with tunable modulus, viscosity, and complex viscoelastic spectra are crucial in applications such as self-healing, additive manufacturing, and energy damping. It is still challenging to predictively design polymer networks with hierarchical relaxation processes, as many competing factors affect dynamics. Here, networks with both pendant and telechelic architecture are synthesized with mixed orthogonal dynamic bonds to understand how the network connectivity and bond exchange mechanisms govern the overall relaxation spectrum. A hydrogen-bonding group and a vitrimeric dynamic crosslinker are combined into the same network, and multimodal relaxation is observed in both pendant and telechelic networks. This is in stark contrast to similar networks where two dynamic bonds share the same exchange mechanism. With the incorporation of orthogonal dynamic bonds, the mixed network also demonstrates excellent damping and improved mechanical properties. In addition, two relaxation processes arise when only hydrogen-bond exchange is present, and both modes are retained in the mixed dynamic networks. This work provides molecular insights for the predictive design of hierarchical dynamics in soft materials.

Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer.

The clinical signs and symptoms of esophageal cancer (EC) are often not discernible until the intermediate or advanced phases. The detection of EC in advanced stages significantly decreases the survival rate to below 20%. This study conducts a comparative analysis of the efficacy of several imaging techniques, including white light image (WLI), narrowband imaging (NBI), cycle-consistent adversarial network simulated narrowband image (CNBI), and hyperspectral imaging simulated narrowband image (HNBI), in the early detection of esophageal cancer (EC). In conjunction with Kaohsiung Armed Forces General Hospital, a dataset consisting of 1000 EC pictures was used, including 500 images captured using WLI and 500 images captured using NBI. The CycleGAN model was used to generate the CNBI dataset. Additionally, a novel method for HSI imaging was created with the objective of generating HNBI pictures. The evaluation of the efficacy of these four picture types in early detection of EC was conducted using three indicators: CIEDE2000, entropy, and the structural similarity index measure (SSIM). Results of the CIEDE2000, entropy, and SSIM analyses suggest that using CycleGAN to generate CNBI images and HSI model for creating HNBI images is superior in detecting early esophageal cancer compared to the use of conventional WLI and NBI techniques.

Application of bidirectional long short-term memory network for prediction of cognitive age.

Electroencephalography (EEG) measures changes in neuronal activity and can reveal significant changes from infancy to adulthood concomitant with brain maturation, making it a potential physiological marker of brain maturation and cognition. To investigate a promising deep learning tool for EEG classification, we applied the bidirectional long short-term memory (BLSTM) algorithm to analyze EEG data from the pediatric EEG laboratory of Taipei Tzu Chi Hospital. The trained BLSTM model was 86% accurate when identifying EEGs from young children (8 months-6 years) and adolescents (12-20 years). However, there was only a modest classification accuracy (69.3%) when categorizing EEG samples into three age groups (8 months-6 years, 6-12 years, and 12-20 years). For EEG samples from patients with intellectual disability, the prediction accuracy of the trained BLSTM model was 46.4%, which was significantly lower than its accuracy for EEGs from neurotypical patients, indicating that the individual's intelligence plays a major role in the age prediction. This study confirmed that scalp EEG can reflect brain maturation and the BLSTM algorithm is a feasible deep learning tool for the identification of cognitive age. The trained model can potentially be applied to clinical services as a supportive measurement of neurodevelopmental status.

Successful lung transplantation for artificial stone-associated accelerated silicosis: a case report.

This report focuses on the occupational health risks associated with the use of artificial stones containing high levels of crystalline silica in the production of kitchen countertops. It presents the case of a 43-yr-old man who developed severe silicosis due to his occupation involving cutting and polishing quartz stone raw materials. A retrospective analysis of the patient's medical records and occupational history was conducted. The diagnosis of severe silicosis, moderate restrictive lung disease, and bilateral pneumothorax was based on clinical manifestations, pulmonary function test, radiological findings, and histological reports. The patient underwent lung transplantation, and his pulmonary function improved post-surgery. The study highlights the significant health risks associated with procedures involving artificial stones and emphasizes the importance of awareness and protective measures for employees and workers. Clinicians should be cautious when diagnosing respiratory symptoms in patients with a history of occupational exposure to artificial stones containing high levels of crystalline silica.

Post-stroke dysphagia and ambient air pollution are associated with dementia.

Introduction: This cohort study aimed to explore the potential association between ambient air pollution and dementia incidence in adults who have experienced a stroke. Additionally, the study aimed to determine dysphagia as a predictive factor for the subsequent development of dementia in patients with stroke. Materials and methods: This retrospective nested case-control study used data from the Kaohsiung Medical University Hospital Database in Taiwan. Data collected include average ambient air pollution concentrations within 3 months and 1 year after the index dysphagia date. The primary outcome includes incident dementia in patients with or without dysphagia. Logistic regression analysis was performed to examine the association between significant air pollution exposure and the risk of dementia while controlling for baseline demographic characteristics (age and sex), and comorbidities. Results: The univariable regression models revealed a higher likelihood of dementia diagnosis in patients with dysphagia (odds ratio = 1.493, 95% confidence interval = 1.000-2.228). The raw odds ratios indicated a potential link between air pollution exposure and elevated dementia risks in the overall study population and patients with stroke without dysphagia, except for O3. Particulate matter (PM)2.5 and nitrogen oxides (NOx) exhibited significant effects on the risk of dementia in the stepwise logistic regression models. Conclusion: The presence of dysphagia following a stroke may pose a risk of developing dementia. Additionally, PM2.5 and NOx exposure appears to elevate the risk of dementia in patients with stroke.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Assessment of Narrow Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer.

Video capsule endoscopy (VCE) is increasingly used to decrease discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared to NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can presently only be achieved in post-processing. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to assess the simulated NBI images. Results show the good performance of the NBI image reproduction method with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.