Intelligent system to predict intradialytic hypotension in chronic hemodialysis.

BACKGROUND: Intradialytic hypotension (IDH) is a serious complication and a major risk factor of increased mortality during hemodialysis (HD). However, predicting the occurrence of intradialytic blood pressure (BP) fluctuations clinically is difficult. This study aimed to develop an intelligent system with capability of predicting IDH. METHODS: In developing and training the prediction models in the intelligent system, we used a database of 653 HD outpatients who underwent 55,516 HD treatment sessions, resulting in 285,705 valid BP records. We built models to predict IDH at the next BP check by applying time-dependent logistic regression analyses. RESULTS: Our results showed the sensitivity of 86% and specificity of 81% for both nadir systolic BP (SBP) of <90 mmHg and <100 mmHg, suggesting good performance of our prediction models. We obtained similar results in validating via test data and data of newly enrolled patients (new-patient data), which is important for simulating prospective situations wherein dialysis staff are unfamiliar with new patients. This compensates for the retrospective nature of the BP records used in our study. CONCLUSION: The use of this validated intelligent system can identify patients who are at risk of IDH in advance, which may facilitate well-timed personalized management and intervention.

An injured man with acute altered mental status.

CLINICAL INTRODUCTION: A 58-year-old man sustained injuries due to accidental fall from a height of 5 m onto a solid floor while operating a crane. He was fully conscious without external evidence of head injury when presented to the ED. The patient had multiple fractures involving the fourth, fifth and seventh of the left ribs, left iliac wing and superior ramus of the pelvis, comminuted fracture of the left femur shaft, and an open fracture of the mandible. Two hours after ED presentation, the patient developed acute confusion and lethargy, which rapidly progressed to coma and respiratory distress, and was subsequently intubated with mechanical ventilation support. Emergent CT scan of the head is shown (figure 1).emermed;35/3/148/F1F1F1Figure 1CT scan of the head without contrast. QUESTION: What is the most likely diagnosis?A. Cerebral haemorrhageB. Brain metastasisC. Cerebral fat embolismD. Diffuse axonal injury.

Evaluation of the Accuracy of ECG Captured by CardioChip through Comparison of Lead I Recording to a Standard 12-Lead ECG Recording Device.

BACKGROUND: Remote cardiac rhythm monitoring and recording, using hand-carried electrocardiogram (ECG) device had been widely used in telemedicine. The feasibility and accuracy analysis on the data recorded by a new miniature ECG system-on-chip (SoC) system has not been explored before. METHODS: This study evaluated the accuracy of the ECG recordings captured by CardioChip - a single-channeled, low-powered, miniature ECG SoC designed for mobile applications; comparing against Philips Pagewriter Trim III - a Food and Drug Administration certified, widely-used standard 12-lead ECG recording device, within Mackay Memorial Hospital in Taiwan. RESULTS: Total of 111 participants, age ranging from 39 to 87years old [mean age: 61.2 ± 13.4, 57 male (51.3%)] were enrolled. Two experienced cardiologists rated and scored the ECG morphology to be the same between the two devices, while CardioChip ECG was more sensitive to baseline noise. R-peak amplitudes measured both devices using single lead information (CardioChip ECG vs. Lead 1 in standard 12-lead ECG) showed statistical consistency. Offline analysis of signal correlation coefficients and coherence showed good correlation with both over 0.94 in average (0.94 ± 0.04 and 0.95 ± 0.04, respectively), high agreement between raters (94% agreement) for detecting abnormal cardiac rhythm with excellent R-peak amplitude (r = 0.98, p < 0.001) and PR interval (r = 0.91, p < 0.001) correlations, indicating excellent correlation between ECG recordings derived from two different modalities. CONCLUSIONS: The results suggested that CardioChip ECG is comparable to medical industry standard ECG. The future implementation of wearable ECG device embedded with miniature ECG system-on-chip (SoC) system is ready for clinical use, which will potentially enhance efficacy on identifying subjects with suspected cardiac arrhythmias.

Association between Chronic Kidney Disease and Risk of Cataract: A Nationwide Retrospective Cohort Study.

BACKGROUND: Previous studies have established a relationship between chronic kidney disease (CKD) and cataract, but the relationship between the severity of renal impairment and risk of cataract is uncertain. This study investigates the relationship between the severity of renal disease and cataract in a nationwide sample from Taiwan. METHODS: The cohort from 1-million National Health Insurance beneficiaries from Taiwan was retrospectively analyzed. All adult beneficiaries were followed from January 1, 2005 until December 31, 2013, to identify patients who underwent cataract surgeries. On the basis of the ambulatory care records, those diagnosed with CKD (ICD-9-CM code: 585) during the follow-up period were enrolled as CKD patients after careful evaluation. Each patient with CKD was age- and gender-matched with 4 individuals who did not have CKD. Cox regression models were applied to compare the hazard of cataract surgery in individuals with and without CKD. Subgroup analysis was used to compare patients with end-stage renal disease (ESRD) with age- and gender-matched non-CKD individuals. ESRD was defined by CKD patients who need regular renal replacement therapy. The same method was applied to evaluate hazard ratios (HRs). RESULTS: After age and gender matching, there were 11,881 patients in the CKD group and 47,524 in the non-CKD group. After control for possible confounding, the adjusted HR (aHR) of cataract was 1.84 (95% CI 1.73-1.95) for the CKD group. Subgroup analysis of patients with ESRD (n = 3,209) and non-CKD individuals (n = 12,836), with matching done on the basis of age and gender, indicated an aHR of cataract was 2.33 (95% CI 2.10-2.59) for the ESRD group. CONCLUSIONS: This study indicates a relationship between CKD and cataract, and suggests that the risk for cataract increases with the severity of renal impairment.

Ultrasound-assisted extraction of Mangiferin from Mango (Mangifera indica L.) leaves using response surface methodology.

Mangiferin is a xanthone widely distributed in higher plants showing antioxidative, antiviral, anticancer, antidiabetic, immunomodulatory, hepatoprotective and analgesic effects. In the present study, an ultrasonic-assisted extraction method was developed for the effective extraction of mangiferin from mango leaves. Some parameters such as ethanol concentration, liquid-to-solid ratio, extraction temperature, and extraction time were optimized by single-factor experiment and response surface methodology. The optimal extraction conditions were 44% ethanol, the liquid-to-solid ratio was 38:1, and extraction for 19.2 min at 60 °C under ultrasound irradiation of 200 W. Under optimal conditions, the yield of mangiferin was 58.46 ± 1.27 mg/g. The results obtained are helpful for the full utilization of mango leaves, and also indicated that ultrasonic-assisted extraction is a very useful method for the extraction of mangiferin from plant materials.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Exploring medical students' metacognitive and regulatory dimensions of diagnostic problem solving.

Solving clinical problems requires an individual to apply not only domain-specific medical knowledge and cognitive skills for reasoning, but also to be consciously aware of, monitor, and evaluate their thinking processes (i.e., metacognition). The purpose of this study was to map critical metacognitive dimensions of clinical problem solving and to explore the structural relationships among them, which may help frame a conceptual framework and better pedagogy for effective intervention. A context-specific inventory was adapted and modified from a domain-general instrument to capture essential metacognitive skills for learning and solving clinical problems. This inventory was administered to 72 undergraduate medical students to survey their capabilities in five dimensions: knowledge of cognition, objectives, problem representation, monitoring, and evaluation. The interplay among these dimensions was further examined using partial least squares structural equation modeling.Our findings revealed that the medical students fell short of some expert-like, metacognitive, and regulatory competence, even after receiving years of medical education and on-site training. In particular, they did not know when a holistic understanding of a problem had been reached. Many of them often do not have a set of clear diagnostic procedures in mind, nor do they concurrently monitor their thinking during diagnostic reasoning. Moreover, their lack of self-improving approaches seemed to worsen their learning. Finally, the structural equation model indicated that knowledge of cognition and objectives significantly predicted problem representation, suggesting that medical learners' knowledge of and goals for learning are influential in framing the clinical problems at hand. A significant linear prediction path was observed from problem representation, monitoring, to evaluation, signifying a possible sequenced process of clinical problem solving. Metacognitive-based instruction can help improve clinical problem-solving skills and awareness of potential biases or errors.

Using a chatbot to reduce emergency department visits and unscheduled hospitalizations among patients with gynecologic malignancies during chemotherapy: A retrospective cohort study.

Background: A chatbot is an automatic text-messaging tool that creates a dynamic interaction and simulates a human conversation through text or voice via smartphones or computers. A chatbot could be an effective solution for cancer patients' follow-up during treatment, and could save time for healthcare providers. Objective: We conducted a retrospective cohort study to evaluate whether a chatbot-based collection of patient-reported symptoms during chemotherapy, with automated alerts to clinicians, could decrease emergency department (ED) visits and hospitalizations. A control group received usual care. Methods: Self-reporting symptoms were communicated via the chatbot, a Facebook Messenger-based interface for patients with gynecologic malignancies. The chatbot included questions about common symptoms experienced during chemotherapy. Patients could also use the text-messaging feature to speak directly to the chatbot, and all reported outcomes were monitored by a cancer manager. The primary and secondary outcomes of the study were emergency department visits and unscheduled hospitalizations after initiation of chemotherapy after diagnosis of gynecologic malignancies. Multivariate Poisson regression models were applied to assess the adjusted incidence rate ratios (aIRRs) for chatbot use for ED visits and unscheduled hospitalizations after controlling for age, cancer stage, type of malignancy, diabetes, hypertension, chronic renal insufficiency, and coronary heart disease. Result: Twenty patients were included in the chatbot group, and 43 in the usual-care group. Significantly lower aIRRs for chatbot use for ED visits (0.27; 95% CI 0.11-0.65; p = 0.003) and unscheduled hospitalizations (0.31; 95% CI 0.11-0.88; p = 0.028) were noted. Patients using the chatbot approach had lower aIRRs of ED visits and unscheduled hospitalizations compared to usual-care patients. Conclusions: The chatbot was helpful for reducing ED visits and unscheduled hospitalizations in patients with gynecologic malignancies who were receiving chemotherapy. These findings are valuable for inspiring the future design of digital health interventions for cancer patients.

Deep convolutional neural network for rib fracture recognition on chest radiographs.

Introduction: Rib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure. Point of care ultrasound offers an alternative but has drawbacks in terms of procedural time and operator expertise. Therefore, this study aims to explore the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs. Methods: We assembled a comprehensive retrospective dataset of chest radiographs with formal image reports documenting rib fractures from a single medical center over the last five years. The DCNN models were trained using 2000 region-of-interest (ROI) slices for each category, which included fractured ribs, non-fractured ribs, and background regions. To optimize training of the deep learning models (DLMs), the images were segmented into pixel dimensions of 128 × 128. Results: The trained DCNN models demonstrated remarkable validation accuracies. Specifically, AlexNet achieved 92.6%, GoogLeNet achieved 92.2%, EfficientNetb3 achieved 92.3%, DenseNet201 achieved 92.4%, and MobileNetV2 achieved 91.2%. Discussion: By integrating DCNN models capable of rib fracture recognition into clinical decision support systems, the incidence of missed rib fracture diagnoses can be significantly reduced, resulting in tangible decreases in morbidity and mortality rates among trauma patients. This innovative approach holds the potential to revolutionize the diagnosis and treatment of chest trauma, ultimately leading to improved clinical outcomes for individuals affected by these injuries. The utilization of DCNNs in rib fracture detection on chest radiographs addresses the limitations of other imaging modalities, offering a promising and practical solution to improve patient care and management.

Correction: A Novel mHealth Approach for a Patient-Centered Medication and Health Management System in Taiwan: Pilot Study.

[This corrects the article DOI: 10.2196/mhealth.9987.].

Association of Hyperhomocysteinemia with Increased Coronary Microcirculatory Resistance and Poor Short-Term Prognosis of Patients with Acute Myocardial Infarction after Elective Percutaneous Coronary Intervention.

BACKGROUND: This study aims to investigate the coronary microcirculatory resistance and prognosis of patients with acute myocardial infarction (AMI) concomitant with hyperhomocysteinemia (HHcy) after an elective percutaneous coronary intervention (PCI). METHODS: A total of 101 patients that underwent elective PCI between May 2015 and July 2018 due to AMI were consecutively enrolled in this study. Patients were divided into a HHcy group (53) and a normal Hcy group (control; 48) based on their plasma homocysteine concentration. The characteristics of coronary angiography, the index of microcirculatory resistance (IMR) of infarct-related vessels (IRV), changes in left ventricular end diastolic diameter (LVEDd) and left ventricular ejection fraction (LVEF) before and after PCI, and the incidence of major adverse cardiovascular events (MACE) three months after PCI were compared between these groups. RESULTS: Compared to the results from the Hcy group, the HHcy group had a higher IMR. The HHcy group had significantly higher LVEDd and a lower LVEF than the Hcy group 3 months after PCI. Additionally, the incidence of MACE at three months after PCI was higher in the HHcy group than in the Hcy group. Pearson correlation analysis revealed a positive correlation with IMR in the HHcy group. Furthermore, there was a difference in the LVEDd measured at one day after PCI and at three months after PCI in the HHcy group. CONCLUSION: AMI patients concomitant with HHcy that undergo elective PCI are prone to coronary microcirculatory dysfunction and have a poor cardiac function and poor prognosis at three months after PCI.

Is the Yale Global Tic Severity Scale a valid tool for parent-reported assessment in the paediatric population? A prospective observational study in Taiwan.

OBJECTIVE: The Yale Global Tic Severity Scale (YGTSS) is the most commonly used clinician-rated evaluation tool for Tourette syndrome (TS), with established reliability and validity. This study aims to determine whether the YGTSS is a valid parent-reported assessment in the TS population. DESIGN: A prospective cohort study. SETTING: A major medical centre in Taiwan. METHODS: A total of 594 patients were enrolled. A revised traditional Chinese version of the YGTSS was made available to parents via Google docs. Parents were encouraged to complete the YGTSS the day before each outpatient clinic visit. At each visit, a paediatric neurology fellow also administered the YGTSS assessment. We investigated whether differences in scores between physicians and parents changed as the number of parent evaluations increased. The results of the physician assessments were also taken as the expert standard for evaluating the sensitivity and specificity of the parent-reported assessments was conducted for the same visit. RESULTS: The differences in the YGTSS scores between participants and physicians were small. The mean difference in the total assessment score was 4.15 points. As the number of times the parent evaluation was performed increased, the difference between the parent and physician scores decreased. Discrimination of moderate-to-severe attacks was good using the parent-assessed YGTSS (area under the receiver operating characteristic curve, 0.858; 95% CI 0.839 to 0.876). The sensitivity for detecting a moderate-to-severe attack by YGTSS parent assessment was 79.7% (95% CI 76.6 to 82.8), and the specificity was 91.8% (95% CI 89.9 to 93.7). CONCLUSION: The parent-reported YGTSS is a promising tool for TS assessment, demonstrating good discriminative ability for disease severity, with user precision increasing with experience.

The Association between Metabolically Healthy Obesity, Cardiovascular Disease, and All-Cause Mortality Risk in Asia: A Systematic Review and Meta-Analysis.

We investigated the association among metabolically healthy obesity (MHO), cardiovascular disease (CVD)risk, and all-cause mortality in the Asian population. We searched databases from inception to 16 November, 2019 and pooled data using a random-effects model. Subgroup analysis was conducted according to the following comparison groups: MHNW (without overweight or underweight participants) and MHNO (non-obese, including overweight and underweight participants). Nineteen studies were included. The mean Newcastle-Ottawa Scale score was 7.8. Participants with MHO had a significantly higher CVD risk (odds ratio (OR) = 1.36, 95% confidence interval (CI) = 1.13-1.63) and significantly lower risk of all-cause mortality (OR = 0.88, 95% CI = 0.78-1.00) than the comparison group. Subgroup analyses revealed participants with MHO had a significantly higher CVD risk than MHNW participants (OR = 1.61; 95% CI = 1.24-2.08; I2 = 73%), but there was no significant difference compared with MHNO participants (OR, 1.04; 95% CI, 0.80-1.36; I2 = 68%). Participants with MHO had a significantly lower risk of all-cause mortality (OR = 0.83; 95% CI = 0.78-0.88; I2 = 9%) than MHNO participants, but a borderline significantly higher risk of all-cause mortality than MHNW participants (OR = 1.30; 95% CI = 0.99-1.72; I2 = 0%). The CVD risk and all-cause mortality of the MHO group changed depending on the control group. Thus, future studies should select control groups carefully.

Chloroplast genome structure and phylogeny of Spiranthes sinensis, an endangered medicinal orchid plant.

Spiranthes sinensis is an important medicinal plant of Spiranthes family and is currently in an endangered state. To better guide the systematic classification of S. sinensis, the complete genome of its chloroplast was sequenced and characterized. The complete chloroplast genome is 152,786 bp in length and contains a large single copy (LSC) region of 83,446 bp, a small single copy (SSC) region of 17,938 bp, and two inverted repeat (IRA and IRB) regions of 25,701 bp. The genome encodes 132 genes (112 unique genes), including 86 protein-coding genes (78 unique genes), 8 rRNA genes (4 unique genes), and 38 tRNA genes (30 unique genes). The total GC content of plasmid genome is 36.18%. Phylogenetic results indicated that S. sinensis is more closely related to Ludisia discolor, Goodyera schlechtendaliana, Goodyera fumata, and Goodyera procera. The study enriches the plasmid genomic information of S. sinensis and is of great significance for the genetic protection of this species.

A network pharmacology study on the triterpene saponins from Medicago sativa L. for the treatment of Neurodegenerative diseases.

Neurodegenerative diseases (NDDs) are characterized by progressive and irreversible, is a kind of complex illnesses, and the long-term therapy which is frequently associated with adverse side effects. Medicago sativa L., widely consumed as a vegetable, has the effects of improving memory and relieving central nervous system diseases. However, there are less studies on its specific mechanism for NDDs. In this investigation, we applied a method of network pharmacology, which combined molecular docking and network analysis to decipher the mechanisms of M. sativa in NDDs. The pharmacological system generated 55 triterpene saponins from M. sativa, and predicted 27 potential targets with 100 pathways in the treatment of NDDs. As a result, 13 compounds, 10 target proteins, and 6 signaling pathways were found to play important roles in the treatment of NDDs. In addition, in vitro experiments of isolates confirmed activities for NDDs, which were consistent with the results of network pharmacology prediction. PRACTICAL APPLICATIONS: Medicago sativa L. has been widely consumed as a vegetable, which possesses many nutritional components. As a functional food stuff, M. sativa can improve human health, such as memory improving activities, relieving central nervous system diseases, immunomodulatory, antioxidant, anticancer, and anti-inflammatory. In this article, the mechanism of triterpene saponins from M. sativa against NDDs was successfully predicted by network pharmacology method. The results will serve as a reference of M. sativa against NDDs.

Bioinspired Self-assembling Peptide Hydrogel with Proteoglycan-assisted Growth Factor Delivery for Therapeutic Angiogenesis.

Critical challenges still exist in surgical theaters and emergency rooms to stop bleeding effectively and facilitate wound healing efficiently. In circumstances of tissue ischemia, it is essential to induce proper angiogenesis to provide adequate vascular supply to the injury site. Methods: In view of these clinical unmet needs, we propose an applicable approach by designing functionalized self-assembling peptide (SAP) hydrogel with two sequences of RADA16-GGQQLK (QLK) and RADA16-GGLRKKLGKA (LRK) in this study. The SAP hydrogel conjugated with QLK functional motif could be crosslinked by endogenous transglutaminase, one of the intrinsic factors secreted during the coagulation process, the mechanical property of the hydrogel can then be enhanced without the need of external support. On the other hand, the LRK sequence exhibited a good binding affinity with the proteoglycan heparan sulfate and could act as a cofactor by sustaining the release of embedded growth factors. Results: The results showed that this SAP solution underwent self-assembling process in a physiological environment, formed hydrogel in situ, and possessed good shear thinning property with injectability. After pH adjustment, the SAP developed densely-compacted fiber entanglement that closely mimicked the three-dimensional fibrous framework of natural extracellular matrix. Such scaffold could not only support the survival of encapsulating cells but also promote the capillary-like tubular structure formation by dual angiogenic growth factors. The ex ovo chicken chorioallantoic membrane assay demonstrated that the growth factor-loaded hydrogel promoted the sprout of surrounding vessels in a spoke-wheel pattern compared to growth factor-free counterparts. Conclusion: The designer bioinspired SAP hydrogel may be an attractive and promising therapeutic modality for minimally-invasive surgery, ischemic tissue disorders and chronic wound healing.

Transplantation of human limbus-derived mesenchymal stromal cells via occipital approach improves hearing in animal auditory neuropathy.

OBJECTIVE: To develop a surgical approach for cell transplantation into mouse cochlear nerves via an intracranial route and investigate whether transplantation of human limbus-derived mesenchymal stromal cells (HL-MSCs) can improve hearing in this model of auditory neuropathy. METHODS: We used 8-week-old CBA/CaJ male mice and created ouabain-induced auditory neuropathy. The surgical approach passed through the cerebellum to reveal the superior semicircular canal and brainstem, allowing access to the auditory nerve. Then HL-MSCs were injected around the cochlear nerve trunk using a micropipette driven by a micropump. Hearing thresholds in the mice were determined by auditory brainstem responses (ABRs) and distortion product otoacoustic emissions (DPOAEs). RESULTS: We produced ouabain-induced neuropathy in mice with an elevated hearing threshold but normal DPOAE. Using immunohistological staining, we detected HL-MSCs were localized in the cochlear nerve trunk 2 days after cell transplantation via this occipital approach. More spiral ganglion neurons were detected in ouabain-treated cochleae 3 months after HL-MSCs transplantation compared to those without HL-MSCs transplantation. The ABR showed significant hearing improvement 3 months after HL-MSCs transplantation. CONCLUSIONS: We successfully established a mouse model for cell transplantation into the intracranial cochlear nerve trunk and showed that HL-MSCs potentially can be applied as cell therapy to treat sensorineural hearing loss.