A Study on Intelligent Optical Bone Densitometry.

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual's bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

RAPID-ED: A predictive model for risk assessment of patient's early in-hospital deterioration from emergency department.

Introduction: The objective of this multi-center retrospective cohort study was to devise a predictive tool known as RAPID-ED. This model identifies non-traumatic adult patients at significant risk for cardiac arrest within 48 hours post-admission from the emergency department. Methods: Data from 224,413 patients admitted through the emergency department (2016-2020) was analyzed, incorporating vital signs, lab tests, and administered therapies. A multivariable regression model was devised to anticipate early cardiac arrest. The efficacy of the RAPID-ED model was evaluated against traditional scoring systems like National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) and its predictive ability was gauged via the area under the receiver operating characteristic curve (AUC) in both hold-out validation set and external validation set. Results: RAPID-ED outperformed traditional models in predicting cardiac arrest with an AUC of 0.819 in the hold-out validation set and 0.807 in the external validation set. In this critical care update, RAPID-ED offers an innovative approach to assessing patient risk, aiding emergency physicians in post-discharge care decisions from the emergency department. High-risk score patients (≥13) may benefit from early ICU admission for intensive monitoring. Conclusion: As we progress with advancements in critical care, tools like RAPID-ED will prove instrumental in refining care strategies for critically ill patients, fostering an improved prognosis and potentially mitigating mortality rates.

[Effects of Organic Fertilizer of Kitchen Waste on Soil Microbial Activity and Function].

Changes in soil microbial activity and ecological function can be used to assess the level of soil fertility and the stability of ecosystems. To assess the fertility and safety of organic fertilizer of kitchen waste (OFK), soils containing 0% (CK), 1%, 3%, and 5% OFK were cultured, and the physical, chemical, and microbial properties of the soils were measured dynamically with routine agrochemical analysis measures and amplicon sequencing. The results showed that compared with those in CK, the contents of organic matter, available phosphorus, available potassium, NH4+-N, and NO3--N in soils with OFK increased by 23.80%-35.13%, 13.29%-29.72%, 16.91%-39.37%, 164.7%-340.2%, and 28.56%-32.71%, respectively. The activities of hydrolases related to the cycle of carbon, nitrogen, and phosphorus (α-glucosidase, leucine aminopeptidase, acid phosphatase, etc.) were also significantly higher than those of the CK treatment. OFK stimulated the growth of soil microorganisms and increased the carbon content of the microbial biomass. The amplicon sequencing analysis found that the microbial community structures of different treatments were significantly different at both the class and genus levels. In addition, it was found that the abundance of beneficial microbes in the soils with OFK increased, whereas pathogenic microbes decreased. RDA results confirmed that soil properties (including soil pH, organic matter, available nutrients, and microbial biomass) had a significant impact on microbial community structure. The results of investing bacterial community based on PICRUSt and FAPROTAX revealed that the function of the soil bacterial community was similar in the four treatments, but OFK supply significantly improved the microbial carbon utilization and metabolic ability. Moreover, by using the FUNGuild software, we found that the application of OFK increased the proportion of saprotroph-symbiotroph and symbiotroph and stimulated the growth of ectomycorrhizal fungi-undefined saprophytic fungi but inhibited plant and animal pathogenic fungi in soil. These results implied that OFK could promote the establishment of symbiotic relationships and inhibit the growth of pathogenic fungi. In summary, OFK could improve soil fertility and hydrolase activity, stimulate the growth of beneficial microorganisms, and defend against pathogens, indicating a promising use as safe and efficient organic fertilizer.

Mild cognitive impairment estimation based on functional near-infrared spectroscopy.

Patients with mild cognitive impairment (MCI) are at a high risk of developing future dementia. However, early identification and active intervention could potentially reduce its morbidity and the incidence of dementia. Functional near-infrared spectroscopy (fNIRS) has been proposed as a noninvasive modality for detecting oxygenation changes in the time-varying hemodynamics of the prefrontal cortex. This study sought to provide an effective method for detecting patients with MCI using fNIRS and the Wisconsin card sorting test (WCST) to evaluate changes in blood oxygenation. The results revealed that all groups with a lower mini-mental state examination grade had a higher increase in HHb concentration during a modified WCST (MCST). The increase in the change in oxygenated hemoglobin concentration in the stroke group was smaller than that in the normal group due to weak cerebrovascular reactivity.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment.

Background/aim: Hypertensive nephropathy (HN) is a common complication of hypertension. Traditional Chinese medicine has long been used in the clinical treatment of Hypertensive nephropathy. However, botanical drug prescriptions have not been summarized. The purpose of this study is to develop a prescription for improving hypertensive nephropathy, explore the evidence related to clinical application of the prescription, and verify its molecular mechanism of action. Methods: In this study, based on the electronic medical record data on Hypertensive nephropathy, the core botanical drugs and patients' symptoms were mined using the hierarchical network extraction and fast unfolding algorithm, and the protein interaction network between botanical drugs and Hypertensive nephropathy was established. The K-nearest neighbors (KNN) model was used to analyze the clinical and biological characteristics of botanical drug compounds to determine the effective compounds. Hierarchical clustering was used to screen for effective botanical drugs. The clinical efficacy of botanical drugs was verified by a retrospective cohort. Animal experiments were performed at the target and pathway levels to analyze the mechanism. Results: A total of 14 botanical drugs and five symptom communities were obtained from real-world clinical data. In total, 76 effective compounds were obtained using the K-nearest neighbors model, and seven botanical drugs were identified as Gao Shen Formula by hierarchical clustering. Compared with the classical model, the Area under the curve (AUC) value of the K-nearest neighbors model was the best; retrospective cohort verification showed that Gao Shen Formula reduced serum creatinine levels and Chronic kidney disease (CKD) stage [OR = 2.561, 95% CI (1.025-6.406), p < 0.05]. With respect to target and pathway enrichment, Gao Shen Formula acts on inflammatory factors such as TNF-α, IL-1ß, and IL-6 and regulates the NF-κB signaling pathway and downstream glucose and lipid metabolic pathways. Conclusion: In the retrospective cohort, we observed that the clinical application of Gao Shen Formula alleviates the decrease in renal function in patients with hypertensive nephropathy. It is speculated that Gao Shen Formula acts by reducing inflammatory reactions, inhibiting renal damage caused by excessive activation of the renin-angiotensin-aldosterone system, and regulating energy metabolism.

Identification of line-source transfer mobility and force density by ground acceleration measurements.

Ground-borne vibration caused by railway traffic has been a research concern due to its possible side effects on nearby residences. The force density and line-source mobility can effectively characterize the generation and transmission of train-induced vibrations, respectively. This research proposed a frequency-domain method for identifying the line-source transfer mobility and force density using measured vibrations at the ground surface, which was on the basis of the least-square method. The proposed method was applied to a case study at Shenzhen Metro in China, where a total of seven fixed-point hammer impacts with 3.3 m equal intervals were used to represent the train vibration excitations. Line-source transfer mobility of the site and force density levels of the metro train were identified, respectively. Causes for different dominant frequencies can be traced by separating the dynamic characteristics of vibration excitation and transmission. It was found in the case study that at a location 3 m away from the track, the peak at 50 Hz was caused by excitations, while that at 63 Hz was attributed to transmission efficiency related to the soil properties. Subsequently, numerical validations of the fixed-point loads' assumption and identified force density levels were carried out. Good comparisons between numerically predicted and experimentally identified force density levels indicated the feasibility of the proposed method. At last, the identified line-source transfer mobility and force density levels were applied to the forward problem, i.e., making predictions of train-induced vibrations. The predicted ground and structural vibrations at different locations were compared to corresponding measurements, with good agreement, which experimentally validated the identification method. The identification results of the case study can be employed by similar railway systems as a good reference.

Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

BACKGROUND: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of these models can make them difficult to apply in clinical practice. In this study, we developed and validated an explainable deep learning model that can predict IBI in febrile infants ≤ 60 days of age visiting the emergency department. METHODS: We conducted a retrospective study of febrile infants aged ≤ 60 days who presented to the pediatric emergency department of a medical center in Taiwan between January 1, 2011 and December 31, 2019. Patients with uncertain test results and complex chronic health conditions were excluded. IBI was defined as the growth of a pathogen in the blood or cerebrospinal fluid. We used a deep neural network to develop a predictive model for IBI and compared its performance to the IBI score and step-by-step approach. The SHapley Additive Explanations (SHAP) technique was used to explain the model's predictions at different levels. RESULTS: Our study included 1847 patients, 53 (2.7%) of whom had IBI. The deep learning model performed similarly to the IBI score and step-by-step approach in terms of sensitivity and negative predictive value, but provided better specificity (54%), positive predictive value (5%), and area under the receiver-operating characteristic curve (0.87). SHapley Additive exPlanations identified five influential predictive variables (absolute neutrophil count, body temperature, heart rate, age, and C-reactive protein). CONCLUSION: We have developed an explainable deep learning model that can predict IBI in febrile infants aged 0-60 days. The model not only performs better than previous scoring systems, but also provides insight into how it arrives at its predictions through individual features and cases.

Intraoperative application of optical coherence tomography for lung tumor.

On-site instant determination of benign or malignant tumors for deciding the types of resection is crucial during pulmonary surgery. We designed a portable spectral-domain optical coherence tomography (SD-OCT) system to do real-time scanning intraoperatively for the distinction of fresh tumor specimens in the lung. A total of 12 ex vivo lung specimens from six patients were enrolled. Three patients were diagnosed with invasive adenocarcinoma (IA), while the others were benign. After OCT-imaged reconstruction, we compared the qualitative morphology of OCT and histology among malignant, benign, and normal tissues. In addition, through analysis of the quantitative data, a discrete difference in optical attenuation coefficients around the junctional surface was shown by our data processing. This study demonstrated a feasible OCT-assisted resection guide by a rapid on-site tumor diagnosis. The results indicate that future deep learning of OCT-captured image systems able to improve diagnostic and therapeutic efficiency is warranted.

Using Signal Features of Functional Near-Infrared Spectroscopy for Acute Physiological Score Estimation in ECMO Patients.

Extracorporeal membrane oxygenation (ECMO) is a vital emergency procedure providing respiratory and circulatory support to critically ill patients, especially those with compromised cardiopulmonary function. Its use has grown due to technological advances and clinical demand. Prolonged ECMO usage can lead to complications, necessitating the timely assessment of peripheral microcirculation for an accurate physiological evaluation. This study utilizes non-invasive near-infrared spectroscopy (NIRS) to monitor knee-level microcirculation in ECMO patients. After processing oxygenation data, machine learning distinguishes high and low disease severity in the veno-venous (VV-ECMO) and veno-arterial (VA-ECMO) groups, with two clinical parameters enhancing the model performance. Both ECMO modes show promise in the clinical severity diagnosis. The research further explores statistical correlations between the oxygenation data and disease severity in diverse physiological conditions, revealing moderate correlations with the acute physiologic and chronic health evaluation (APACHE II) scores in the VV-ECMO and VA-ECMO groups. NIRS holds the potential for assessing patient condition improvements.

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach.

BACKGROUND: Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. OBJECTIVE: This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. METHODS: This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS: The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). CONCLUSIONS: By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

Metastatic lymph nodes and prognosis assessed by the number of retrieved lymph nodes in gastric cancer.

BACKGROUND: The prognostic value of quantitative assessments of the number of retrieved lymph nodes (RLNs) in gastric cancer (GC) patients needs further study. AIM: To discuss how to obtain a more accurate count of metastatic lymph nodes (MLNs) based on RLNs in different pT stages and then to evaluate patient prognosis. METHODS: This study retrospectively analyzed patients who underwent GC radical surgery and D2/D2+ LN dissection at the Cancer Hospital of Harbin Medical University from January 2011 to May 2017. Locally weighted smoothing was used to analyze the relationship between RLNs and the number of MLNs. Restricted cubic splines were used to analyze the relationship between RLNs and hazard ratios (HRs), and X-tile was used to determine the optimal cutoff value for RLNs. Patient survival was analyzed with the Kaplan-Meier method and log-rank test. Finally, HRs and 95% confidence intervals were calculated using Cox proportional hazards models to analyze independent risk factors associated with patient outcomes. RESULTS: A total of 4968 patients were included in the training cohort, and 11154 patients were included in the validation cohort. The smooth curve showed that the number of MLNs increased with an increasing number of RLNs, and a nonlinear relationship between RLNs and HRs was observed. X-tile analysis showed that the optimal number of RLNs for pT1-pT4 stage GC patients was 26, 31, 39, and 45, respectively. A greater number of RLNs can reduce the risk of death in patients with pT1, pT2, and pT4 stage cancers but may not reduce the risk of death in patients with pT3 stage cancer. Multivariate analysis showed that RLNs were an independent risk factor associated with the prognosis of patients with pT1-pT4 stage cancer (P = 0.044, P = 0.037, P = 0.003, P < 0.001). CONCLUSION: A greater number of RLNs may not benefit the survival of patients with pT3 stage disease but can benefit the survival of patients with pT1, pT2, and pT4 stage disease. For the pT1, pT2, and pT4 stages, it is recommended to retrieve 26, 31 and 45 LNs, respectively.

IL-13 Regulates Orai1 Expression in Human Bronchial Smooth Muscle Cells and Airway Remodeling in Asthma Mice Model via LncRNA H19.

Background: Increased proliferation and hypertrophy of airway smooth muscle cells (ASMCs) contribute substantially to airway remodeling in asthma. Interleukin (IL)-13 regulates ASMC proliferation by increasing Orai1 expression, the pore-forming subunit of store-operated Ca2+ entry (SOCE). The underlying mechanisms of this effect are not fully understood. Methods: Bioinformatic analysis identified an interaction between microRNA 93-5p (miR-93-5p) and long non-coding RNA (lncRNA) H19, and between miR-93-5p and Orai1. RNA interference was used to investigate H19 knockdown on IL-13-induced proliferation and migration of in vitro cultured human bronchial smooth muscle cells (hBSMCs). Functional relevance of H19 in airway inflammation and airway remodeling was investigated in murine models of acute and chronic asthma. Results: IL-13 concentration-dependently increased the expression of H19 and Orai1 and decreased the expression of miR-93-5p in hBSMCs. H19 knockdown partly reversed the effects of IL-13 on the expression of miR-93-5p and Orai1 and attenuated the proliferation and migration of hBSMCs promoted by IL-13. IL-13-promoted expression of Orai1 was attenuated by miR-93-5p mimic and increased by miR-93-5p inhibitor. IL-13-promoted proliferation of hBSMCs was increased by miR-93-5p inhibitor but not affected by miR-93-5p mimic, whereas IL-13-promoted migration of hBSMCs was increased by miR-93-5p inhibitor and attenuated by miR-93-5p mimic. The inhibiting effect of H19 knockdown on IL-13-induced Orai1 expression and the proliferation and migration of hBSMCs was counteracted by miR-93-5p inhibitor but only marginally or not impacted by miR-93-5p mimic. The expression of H19 and Orai1 was higher in the lungs of asthmatic mice than in control mice. In asthmatic mice, H19 siRNA reduced Orai1 expression, inflammatory cell infiltration, goblet cell hyperplasia, collagen deposition and smooth muscle mass in the lungs. Conclusion: H19 may mediate the effects of IL-13 on Orai1 expression by inhibition of miR-93-5p in hBSMCs. H19 may be a therapeutic target for airway inflammation and airway remodeling.

Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task.

Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

Correlation Between the Number of Fiberoptic Bronchoscopies and Nosocomial Infection/Colonization of Carbapenem-Resistant Enterobacteriaceae.

Objective: The present study aims to explore the effects of different numbers of fiberoptic bronchoscopic examinations on the nosocomial infection/colonization of carbapenem-resistant Enterobacteriaceae (CRE). Methods: The data of 129 patients admitted to the intensive care unit of a grade 3A hospital were retrospectively analyzed, and CRE nosocomial infection/colonization situations in patients with fiberoptic bronchoscope application times of 1, 2, 3, and ≥4 were statistically analyzed. Results: The incidence of nosocomial infection/colonization of CRE increased significantly when the number of fiberoptic bronchoscopic examinations was ≥3. Conclusion: Nosocomial infection/colonization of CRE is highly correlated with an increased number of fiberoptic bronchoscopic examinations.

Prognostic significance of serum inflammation indices for different tumor infiltrative pattern types of gastric cancer.

BACKGROUND: Inflammatory indices are considered to be potential prognostic biomarkers for patients with gastric cancer (GC). However, there is no evidence defining the prognostic significance of inflammatory indices for GC with different tumor infiltrative pattern (INF) types. AIM: To evaluate the significance of inflammatory indices and INF types in predicting the prognosis of patients with GC. METHODS: A total of 962 patients who underwent radical gastrectomy were retrospectively selected for this study. Patients were categorized into the expansive growth type (INFa), the intermediate type (INFb), and the infiltrative growth type (INFc) groups. The cutoff values of inflammatory indices were analyzed by receiver operating characteristic curves. The Kaplan-Meier method and log-rank test were used to analyze overall survival (OS). The chi-square test was used to analyze the association between inflammatory indices and clinical characteristics. The independent risk factors for prognosis in each group were analyzed by univariate and multivariate analyses based on logistic regression. Nomogram models were constructed by R studio. RESULTS: The INFc group had the worst OS (P < 0.001). The systemic immune-inflammation index (P = 0.039) and metastatic lymph node ratio (mLNR) (P = 0.003) were independent risk factors for prognosis in the INFa group. The platelet-lymphocyte ratio (PLR) (P = 0.018), age (P = 0.026), body mass index (P = 0.003), and postsurgical tumor node metastasis (pTNM) stage (P < 0.001) were independent risk factors for prognosis in the INFb group. The PLR (P = 0.021), pTNM stage (P = 0.028), age (P = 0.021), and mLNR (P = 0.002) were independent risk factors for prognosis in the INFc group. The area under the curve of the nomogram model for predicting 5-year survival in the INFa group, INFb group, and INFc group was 0.787, 0.823, and 0.781, respectively. CONCLUSION: The outcome of different INF types GC patients could be assessed by nomograms based on different inflammatory indices and clinicopathologic features.