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Background: Tuberculosis (TB), one of the leading causes of death worldwide, is difficult to diagnose based only on signs and symptoms. Methods for TB detection are continuously being researched to design novel effective clinical tools for the diagnosis of TB. Objective: This article reviews the methods to diagnose TB at the latent and active stages and to recognize prospective TB diagnostic methods based on nanomaterials. Methods: The current methods for TB diagnosis were reviewed by evaluating their advantages and disadvantages. Furthermore, the trends in TB detection using nanomaterials were discussed regarding their performance capacity for clinical diagnostic applications. Results: Current methods such as microscopy, culture, and tuberculin skin test are still being employed to diagnose TB, however, a highly sensitive point of care tool without false results is still needed. The utilization of nanomaterials to detect the specific TB biomarkers with high sensitivity and specificity can provide a possible strategy to rapidly diagnose TB. Although it is challenging for nanodiagnostic platforms to be assessed in clinical trials, active TB diagnosis using nanomaterials is highly expected to achieve clinical significance for regular application. In addition, aspects and future directions in developing the high-efficiency tools to diagnose active TB using advanced nanomaterials are expounded. Conclusion: This review suggests that nanomaterials have high potential as rapid, costeffective tools to enhance the diagnostic sensitivity and specificity for the accurate diagnosis, treatment, and prevention of TB. Hence, portable nanobiosensors can be alternative effective tests to be exploited globally after clinical trial execution.

Fluorescence lifetime provides a third independent dimension of information for the resolution of total luminescence spectra of multicomponent mixtures. The incorporation of this parameter into the Excitation-Emission Matrix (EEM) by the phase-modulation technique results in a three-dimensional Excitation-Emission-Frequency Array (EEFA). Multicomponent analysis based on the three-dimensional EEFA brings a qualitative change for the resolved spectra, i.e., individual spectra can be uniquely resolved, which is impossible with any two-dimensional analysis. In this paper, we present a method for analyzing the EEFA. We show mathematically that with the three-dimensional analysis of the EEFA individual spectra and lifetimes can be obtained. Our algorithm is developed in mathematical detail and demonstrated by its application to a two-component mixture.

The article focuses on forming a benefit corporation, a type of for-profit corporate entity for mission driven businesses that provides positive impact on society and the environment apart from earning profits. It informs that Benefit corporations provides opportunities for women lawyers to work for community service.

Network news is an important way for netizens to get social information. Massive news information hinders netizens to get key information. Named entity recognition technology under artificial background can realize the classification of place, date and other information in text information. This article combines named entity recognition and deep learning technology. Specifically, the proposed method introduces an automatic annotation approach for Chinese entity triggers and a Named Entity Recognition (NER) model that can achieve high accuracy with a small number of training data sets. The method jointly trains sentence and trigger vectors through a trigger-matching network, utilizing the trigger vectors as attention queries for subsequent sequence annotation models. Furthermore, the proposed method employs entity labels to effectively recognize neologisms in web news, enabling the customization of the set of sensitive words and the number of words within the set to be detected, as well as extending the web news word sentiment lexicon for sentiment observation. Experimental results demonstrate that the proposed model outperforms the traditional BiLSTM-CRF model, achieving superior performance with only a 20% proportional training data set compared to the 40% proportional training data set required by the conventional model. Moreover, the loss function curve shows that my model exhibits better accuracy and faster convergence speed than the compared model. Finally, my model achieves an average accuracy rate of 97.88% in sentiment viewpoint detection., Competing Interests: The authors declare that they have no competing interests., (© 2024 Tu.)