We explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method. The data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model. For the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis. DNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.

Uric acid (UA) forms monosodium urate (MSU) crystals to exert proinflammatory actions, thus causing gout arthritis, urolithiasis, kidney disease, and cardiovascular disease. UA is also one of the most potent antioxidants that suppresses oxidative stress. Hyper andhypouricemia are caused by genetic mutations or polymorphism. Hyperuricemia increases urinary UA concentration and is frequently associated with urolithiasis, which is augmented by low urinary pH. Renal hypouricemia (RHU) is associated with renal stones by increased level of urinary UA, which correlates with the impaired tubular reabsorption of UA. Hyperuricemia causes gout nephropathy, characterized by renal interstitium and tubular damage because MSU precipitates in the tubules. RHU is also frequently associated with tubular damage with elevated urinary beta2-microglobulin due to increased urinary UA concentration, which is related to impaired tubular UA reabsorption through URAT1. Hyperuricemia could induce renal arteriopathy and reduce renal blood flow, while increasing urinary albumin excretion, which is correlated with plasma xanthine oxidoreductase (XOR) activity. RHU is associated with exercise-induced kidney injury, since low levels of SUA could induce the vasoconstriction of the kidney and the enhanced urinary UA excretion could form intratubular precipitation. A U-shaped association of SUA with organ damage is observed in patients with kidney diseases related to impaired endothelial function. Under hyperuricemia, intracellular UA, MSU crystals, and XOR could reduce NO and activate several proinflammatory signals, impairing endothelial functions. Under hypouricemia, the genetic and pharmacological depletion of UA could impair the NO-dependent and independent endothelial functions, suggesting that RHU and secondary hypouricemia might be a risk factor for the loss of kidney functions. In order to protect kidney functions in hyperuricemic patients, the use of urate lowering agents could be recommended to target SUA below 6 mg/dL. In order to protect the kidney functions in RHU patients, hydration and urinary alkalization may be recommended, and in some cases an XOR inhibitor might be recommended in order to reduce oxidative stress.

Background: Exertional dyspnea is the primary symptom that limits exercise in patients with chronic obstructive pulmonary disease (COPD). It is unknown which activated brain area is associated with this symptom in COPD patients. Objectives: To investigate the activation of cortical areas associated with dyspnea during exercise in COPD patients. Methods: COPD patients (n = 10) and age-matched controls (n = 10) performed mild-intensity constant work rate cycle exercise (40% of their symptom-limited peak work rates) for 10 min, while cerebral hemodynamics and oxygenation were measured by near-infrared spectroscopy (NIRS). Ventilatory responses (breathing pattern and pulmonary gas exchange) and Borg scale ratings of dyspnea and leg fatigue were measured during exercise. Three NIRS probes were placed over the prefrontal and temporoparietal cortical regions of the subjects’ heads. Changes in cortical oxyhemoglobin (oxy-Hb), deoxyhemoglobin (deoxy-Hb), and total hemoglobin (total Hb) concentrations from baseline recordings were measured. Increased oxy-Hb (oxygenation) was assumed to reflect cortical activation. Results: Oxy-Hb concentration was significantly increased in the prefrontal region during exercise in both groups but not in the temporoparietal regions. The change in prefrontal oxy-Hb concentration of COPD patients was not different from that of controls. Dyspnea scores were positively correlated with changes in oxy-Hb concentrations of the prefrontal regions in both groups. Multivariate analysis showed that oxy-Hb concentration in the prefrontal region was the best predictor of dyspnea in both groups. Conclusions: Exertional dyspnea was related to activation (oxygenation) of the prefrontal cortex in COPD patients and control subjects.

An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings.

Few studies have examined variations in both the physical signs and clinical findings in the final days and hours before the death of elderly patients. We determined the physical signs and clinical findings in patients who were at least 75 years old or were diagnosed with cancer with impending death and examined the association of these parameters with the profile and timing of their endocardiography (ECG) and oxygen saturation (SpO ₂ ) changes prior to death. In this prospective, observational study, between April 2014 and June 2017, we enrolled elderly patients who were admitted to the Respiratory Medicine Ward in our hospital and were near death, which was determined based on certain symptoms such as a loss of oral intake. We recorded their physical signs (oral intake, consciousness level, and respiration with mandibular movement) 4 times a day from admission to death. We evaluated their changes in ECG and in SpO ₂ levels for up to 24 hours preceding death. For the 70 patients who died in our ward, we found a loss of oral intake at 6 days, consciousness impairment at 1.3 days, and then respiration with mandibular movement at 12 hours before death were the most consistent findings before death. When we analyzed the ECG and SpO ₂ changes during impending death, 83% of the patients showed undetectable SpO ₂ levels followed by a loss of heart rate. The loss of P wave in the ECG was characterized as process on impending death. Respiration with mandibular movement was one of the specific signs of impending death in ill elderly patients. The monitoring of ECG and SpO ₂ levels may be a useful tool to predict the impending time of death.

Background BNT162b2, an mRNA COVID-19 vaccine, was launched in many countries as an intramuscular vaccination for COVID-19 infection. Few studies have assessed the physical indications of pain at the immunization site. This study aimed to characterize pain at the injection site and investigate morphological attributes using ultrasound.Methods Forty-three of 211 healthcare workers who received a second dose of BNT162b2 between February 2021 and March 2021 were enrolled in the study. The mean age of the subjects was 40 years. We evaluated patients’ pain at the injection site using the Numerical Rating Pain Scale (NRPS). We also assessed the thickness of the deltoid muscle fascia at the injection site by ultrasound. Bayesian robust correlation was employed to explore the relationship between the pain intensity scores and ultrasound measurements.Results All eligible subjects complained of pain at the injection site. A median pain onset of 8 hours post-vaccination and a median peak intensity score of 4 were reported. Onset of relief occurred after 2 days. Ultrasound images demonstrated a 2.5-fold increase in fascia thickness at the injection site without intramuscular echogenicity change in all subjects. A correlation was established between the NRPS score and the non-injection-to-injection-side ratio of fascia thickness at the injection site (rho = 0.66).Conclusion A sore arm was the most prevalent side effect of BNT162b2 vaccination and could be attributed to temporal fasciitis.