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Surgery Surgery

A cost-effective smartphone-based device for rapid C-reaction protein (CRP) detection using magnetoelastic immunosensor.

In Lab on a chip

C-Reaction protein (CRP) is a marker of nonspecific immunity for vital signs and wound assessment, and it can be used to diagnose infections in clinical medicine. However, measuring CRP level currently requires hospital-based instruments, high-cost reagents, and a complex process, all of which have limited its full capabilities for self-detection, a growing trend in modern medicine. In this study, we developed a novel smartphone-based device using advanced methods of magnetoelastic immunosensing to mitigate these limitations. We combined a system-on-chip (SoC) hardware architecture with smartphone apps to realize the sampling of resonance frequency shift on magnetoelastic chips, which can determine the ultra-sensitivity to mass change caused by the binding of anti-CRP antibody and CRP. Through detecting a multi-group of samples, we found that the resonance frequency shift was linearly proportional to the CRP concentration in the range from 0.1 to 100 μg mL-1, with a sensitivity of 12.90 Hz μg-1 mL-1 and a detection limit of 2.349 × 10-4 μg mL-1. Meanwhile, compared with the large-scale instrument used in clinical settings, the performance of our device was stable and significantly more portable, rapid and cost-effective, offering excellent potential for modern home-based diagnosis.

Yuan Zhongyun, Han Mengshu, Li Donghao, Hao Runfang, Guo Xing, Sang Shengbo, Zhang Hongpeng, Ma Xingyi, Jin Hu, Xing Zhijin, Zhao Chun

2023-Mar-14

General General

A Machine-Learning Approach to Assess Factors Associated With Hospitalization of Children and Youths in Psychiatric Crisis.

In Psychiatric services (Washington, D.C.)

OBJECTIVE : The authors used a machine-learning approach to model clinician decision making regarding psychiatric hospitalization of children and youths in crisis and to identify factors associated with the decision to hospitalize.

METHODS : Data consisted of 4,786 mobile crisis response team assessments of children and youths, ages 4.0-19.5 years (mean±SD=14.0±2.7 years, 56% female), in Nevada. The sample assessments were split into training and testing data sets. A random-forest machine-learning algorithm was used to identify variables related to the decision to hospitalize a child or youth after the crisis assessment. Results from the training sample were externally validated in the testing sample.

RESULTS : The random-forest model had good performance (area under the curve training sample=0.91, testing sample=0.92). Variables found to be important in the decision to hospitalize a child or youth were acute suicidality, followed by poor judgment or decision making, danger to others, impulsivity, runaway behavior, other risky behaviors, nonsuicidal self-injury, psychotic or depressive symptoms, sleep problems, oppositional behavior, poor functioning at home or with peers, depressive or schizophrenia spectrum disorders, and age.

CONCLUSIONS : In crisis settings, clinicians were found to mostly focus on acute factors that increased risk for danger to self or others (e.g., suicidality, poor judgment), current psychiatric symptoms (e.g., psychotic symptoms), and functioning (e.g., poor home functioning, problems with peer relationships) when deciding whether to hospitalize or stabilize a child or youth. To reduce psychiatric hospitalization, community-based services should target interventions to address these important factors associated with the need for a higher level of care among youths in psychiatric crisis.

Chen Yen-Ling, Kraus Shane W, Freeman Megan J, Freeman Andrew J

2023-Mar-14

Clinical decision making, Crisis intervention, Hospitalization, Psychiatric hospitalization, Random forests, Supervised machine learning

General General

Disulfiram enhances the antitumor activity of cisplatin by inhibiting the Fanconi anemia repair pathway.

In Journal of Zhejiang University. Science. B

A series of chemotherapeutic drugs that induce DNA damage, such as cisplatin (DDP), are standard clinical treatments for ovarian cancer, testicular cancer, and other diseases that lack effective targeted drug therapy. Drug resistance is one of the main factors limiting their application. Sensitizers can overcome the drug resistance of tumor cells, thereby enhancing the antitumor activity of chemotherapeutic drugs. In this study, we aimed to identify marketable drugs that could be potential chemotherapy sensitizers and explore the underlying mechanisms. We found that the alcohol withdrawal drug disulfiram (DSF) could significantly enhance the antitumor activity of DDP. JC-1 staining, propidium iodide (PI) staining, and western blotting confirmed that the combination of DSF and DDP could enhance the apoptosis of tumor cells. Subsequent RNA sequencing combined with Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis and cell biology studies such as immunofluorescence suggested an underlying mechanism: DSF makes cells more vulnerable to DNA damage by inhibiting the Fanconi anemia (FA) repair pathway, exerting a sensitizing effect to DNA damaging agents including platinum chemotherapy drugs. Thus, our study illustrated the potential mechanism of action of DSF in enhancing the antitumor effect of DDP. This might provide an effective and safe solution for combating DDP resistance in clinical treatment.

Yuan Meng, Wu Qian, Zhang Mingyang, Lai Minshan, Chen Wenbo, Yang Jianfeng, Jiang Li, Cao Ji

2023-Mar-15

Chemotherapy, Cisplatin (DDP), DNA damage, Disulfiram (DSF), Fanconi anemia (FA) repair

General General

Population-based active screening strategy contributes to the prevention and control of tuberculosis.

In Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences

Despite the achievements obtained worldwide in the control of tuberculosis in recent years, many countries and regions including China still face challenges such as low diagnosis rate, high missed diagnosis rate, and delayed diagnosis of the disease. The discovery strategy of tuberculosis in China has changed from "active discovery by X-ray examination" to "passive discovery by self-referral due to symptoms", and currently the approach is integrated involving self-referral due to symptoms, active screening, and physical examination. Active screening could help to identify early asymptomatic and untreated cases. With the development of molecular biology and artificial intelligence-assisted diagnosis technology, there are more options for active screening among the large-scale populations. Although the implementation cost of a population-based active screening strategy is high, it has great value in social benefits, and active screening in special populations can obtain better benefits. Active screening of tuberculosis is an important component of the disease control. It is suggested that active screening strategies should be optimized according to the specific conditions of the regions to ultimately ensure the benefit of the tuberculosis control.

Ding Cheng, Ji Zhongkang, Zheng Lin, Jin Xiuyuan, Ruan Bing, Zhang Ying, Li Lanjuan, Xu Kaijin

2023-Feb-25

Active screening, Population, Prevention and control, Review, Strategy, Tuberculosis

Pathology Pathology

Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.

In Journal of pathology informatics ; h5-index 23.0

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.

Gondim Dibson D, Al-Obaidy Khaleel I, Idrees Muhammad T, Eble John N, Cheng Liang

2023

Artificial intelligence, Digital pathology, Histopathology, Metanephric adenoma, Renal cell carcinoma, Renal oncocytoma

General General

Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

In Biomedical signal processing and control

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.

Morís Daniel I, de Moura Joaquim, Marcos Pedro J, Rey Enrique Míguez, Novo Jorge, Ortega Marcos

2023-Jul

COVID-19, Classification, Clinical data, Feature selection, Machine learning