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General General

Terahertz spectroscopic diagnosis of early blast-induced traumatic brain injury in rats.

In Biomedical optics express

The early diagnosis of blast-induced traumatic brain injury (bTBI) is of great clinical significance for prognostication and treatment. Here, we report a new strategy for early bTBI diagnosis through serum and cerebrospinal fluid (CSF) based on terahertz time-domain spectroscopy (THz-TDS). The spectral differences of serum and CSF for different degrees of experimental bTBI in rats have been demonstrated in the early period. In addition, the THz spectra of total protein in the hypothalamus and hippocampus were investigated at different time points after blast exposure, which both showed clear differences with time increasing compared with that in the normal brain. This might help to explain the neurological symptoms caused by bTBI. Moreover, based on the THz absorption spectra of serum and CSF, the principal component analysis and machine learning algorithms were performed to automatically identify the degree of bTBI. The highest diagnostic accuracy was up to 95.5%. It is suggested that this method has potential as an alternative method for high-sensitive, rapid, label-free, economical and early diagnosis of bTBI.

Wang Yuye, Wang Guoqiang, Xu Degang, Jiang Bozhou, Ge Meilan, Wu Limin, Yang Chuanyan, Mu Ning, Wang Shi, Chang Chao, Chen Tunan, Feng Hua, Yao Jianquan


General General

An integrated spectroscopic strategy to trace the geographical origins of emblic medicines: Application for the quality assessment of natural medicines.

In Journal of pharmaceutical analysis

Emblic medicine is a popular natural source in the world due to its outstanding healthcare and therapeutic functions. Our preliminary results indicated that the quality of emblic medicines might have an apparent regional variation. A rapid and effective geographical traceability system has not been designed yet. To trace the geographical origins so that their quality can be controlled, an integrated spectroscopic strategy including spectral pretreatment, outlier diagnosis, feature selection, data fusion, and machine learning algorithm was proposed. A featured data matrix (245 × 220) was successfully generated, and a carefully adjusted RF machine learning algorithm was utilized to develop the geographical traceability model. The results demonstrate that the proposed strategy is effective and can be generalized. Sensitivity (SEN), specificity (SPE) and accuracy (ACC) of 97.65%, 99.85% and 97.63% for the calibrated set, as well as 100.00% predictive efficiency, were obtained using this spectroscopic analysis strategy. Our study has created an integrated analysis process for multiple spectral data, which can achieve a rapid, nondestructive and green quality detection for emblic medicines originating from seventeen geographical origins.

Qi Luming, Zhong Furong, Chen Yang, Mao Shengnan, Yan Zhuyun, Ma Yuntong


Emblic medicine, Geographical traceability, Quality assessment, Spectroscopic analysis process

Public Health Public Health

Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot.

In Journal of aging research

Background : Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients' longevity and quality of life.

Objectives : The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT.

Method : Unicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death.

Results : 18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences (P > 0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher (P = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m2) versus elderlies (53.3 ± 21.1 mL/min/1.73 m2). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m2 presented a statistical significance for age ≥60 years (P = 0.01) and reduction in serum haemoglobin (P = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F1 score: 0.79).

Conclusion : Renal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

Elihimas Júnior Ubiracé Fernando, Couto Jamila Pinho, Pereira Wallace, Barros de Oliveira Sá Michel Pompeu, Tenório de França Eduardo Eriko, Aguiar Filipe Carrilho, Cabral Diogo Buarque Cordeiro, Alencar Saulo Barbosa Vasconcelos, Feitosa Saulo José da Costa, Claizoni Dos Santos Thais Oliveira, Dos Santos Elihimas Helen Conceição, Alves Emilly Pereira, José de Carvalho Lima Marcio, Branco Cavalcanti Frederico Castelo, Schwingel Paulo Adriano


General General

Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model.

In Pulmonary circulation

The pathogenesis of pulmonary arterial hypertension is closely associated with dysregulated inflammation. Recently, abnormal alterations in gut microbiome composition and function were reported in a pulmonary arterial hypertension experimental animal model. However, it remains unclear whether these alterations are a result or the cause of pulmonary arterial hypertension. The purpose of this study was to investigate whether alterations in the gut microbiome affected the hemodynamics in SU5416/hypoxia rats. We used the SU5416/hypoxia rat model in our study. SU5416/hypoxia rats were treated with a single SU5416 injection (30 mg/kg) and a three-week hypoxia exposure (10% O2). Three SU5416/hypoxia rats were treated with a combination of four antibiotics (SU5416/hypoxia + ABx group) for four weeks. Another group was exposed to hypoxia (10% O2) without the SU5416 treatment, and control rats received no treatment. Fecal samples were collected from each animal, and the gut microbiota composition was analyzed by 16S rRNA sequencing. The antibiotic treatment significantly suppressed the vascular remodeling, right ventricular hypertrophy, and increase in the right ventricular systolic pressure in SU5416/hypoxia rats. 16S rRNA sequencing analysis revealed gut microbiota modification in SU5416/hypoxia + ABx group. The Firmicutes-to-Bacteroidetes ratio in SU5416/hypoxia rats was significantly higher than that in control and hypoxia rats. Compared with the control microbiota, 14 bacterial genera, including Bacteroides and Akkermansia, increased, whereas seven bacteria, including Rothia and Prevotellaceae, decreased in abundance in SU5416/hypoxia rats. Antibiotic-induced modification of the gut microbiota suppresses the development of pulmonary arterial hypertension. Dysbiosis may play a causal role in the development and progression of pulmonary arterial hypertension.

Sanada Takayuki J, Hosomi Koji, Shoji Hiroki, Park Jonguk, Naito Akira, Ikubo Yumiko, Yanagisawa Asako, Kobayashi Takayuki, Miwa Hideki, Suda Rika, Sakao Seiichiro, Mizuguchi Kenji, Kunisawa Jun, Tanabe Nobuhiro, Tatsumi Koichiro

dysbiosis, inflammation, pathogenesis, pulmonary hypertension experimental, vascular remodeling

oncology Oncology

Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy.

In Journal of Cancer

Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.

Kriegsmann Mark, Casadonte Rita, Maurer Nadine, Stoehr Christine, Erlmeier Franziska, Moch Holger, Junker Klaus, Zgorzelski Christiane, Weichert Wilko, Schwamborn Kristina, Deininger Sören-Oliver, Gaida Matthias, Mechtersheimer Gunhild, Stenzinger Albrecht, Schirmacher Peter, Hartmann Arndt, Kriegsmann Joerg, Kriegsmann Katharina


Oncocytic renal tumors, chromophobe renal cell carcinoma, mass spectrometry imaging, proteomics, renal oncocytoma

General General

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

In Therapeutic advances in neurological disorders

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Abedi Vida, Khan Ayesha, Chaudhary Durgesh, Misra Debdipto, Avula Venkatesh, Mathrawala Dhruv, Kraus Chadd, Marshall Kyle A, Chaudhary Nayan, Li Xiao, Schirmer Clemens M, Scalzo Fabien, Li Jiang, Zand Ramin


acute stroke, artificial intelligence, cerebrovascular disease/stroke, computer aided diagnosis, ischemic stroke, machine learning, stroke diagnosis, stroke in emergency department