This study aimed to explore the diagnostic value of novel technique-targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid (BALF) in pulmonary mycobacterial infections.
This retrospective study was conducted on patients who underwent bronchoscopy and tNGS, smear microscopy, and mycobacterial culture of BALF. Patients with positive Mycobacterium tuberculosis (MTB) culture or GeneXpert results were classified into the tuberculosis case group. Those diagnosed with nontuberculous mycobacteria (NTM)-pulmonary disease (NTM-PD) composed the case group of NTM-PD patients. The control group comprised patients without tuberculosis or NTM-PD. Sensitivity, specificity, and receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance.
For tuberculosis patients with positive mycobacterial culture results, the areas under the ROC curves (AUCs) for tNGS, GeneXpert, and smear microscopy were 0.975 (95% CI: 0.935, 1.000), 0.925 (95% CI: 0.859, 0.991), and 0.675 (95% CI: 0.563, 0.787), respectively. For tuberculosis patients with positive GeneXpert results, the AUCs of tNGS, culture, and smear microscopy were 0.970 (95% CI: 0.931, 1.000), 0.850 (95% CI: 0.770, 0.930), and 0.680 (95% CI: 0.579, 0.781), respectively. For NTM-PD, the AUCs of tNGS, culture, and smear-positive but GeneXpert-negative results were 0.987 (95% CI: 0.967, 1.000), 0.750 (95% CI: 0.622, 0.878), and 0.615 (95% CI: 0.479, 0.752), respectively. The sensitivity and specificity of tNGS in NTM-PD patients were 100% and 97.5%, respectively.
tNGS demonstrated superior diagnostic efficacy in mycobacterial infection, indicating its potential for clinical application.
This study aimed to evaluate and compare the effectiveness of knowledge base-optimized and unoptimized large language models (LLMs) in the field of orthopedics to explore optimization strategies for the application of LLMs in specific fields.
This research constructed a specialized knowledge base using clinical guidelines from the American Academy of Orthopaedic Surgeons (AAOS) and authoritative orthopedic publications. A total of 30 orthopedic-related questions covering aspects such as anatomical knowledge, disease diagnosis, fracture classification, treatment options, and surgical techniques were input into both the knowledge base-optimized and unoptimized versions of the GPT-4, ChatGLM, and Spark LLM, with their generated responses recorded. The overall quality, accuracy, and comprehensiveness of these responses were evaluated by 3 experienced orthopedic surgeons.
Compared with their unoptimized LLMs, the optimized version of GPT-4 showed improvements of 15.3% in overall quality, 12.5% in accuracy, and 12.8% in comprehensiveness; ChatGLM showed improvements of 24.8%, 16.1%, and 19.6%, respectively; and Spark LLM showed improvements of 6.5%, 14.5%, and 24.7%, respectively.
The optimization of knowledge bases significantly enhances the quality, accuracy, and comprehensiveness of the responses provided by the 3 models in the orthopedic field. Therefore, knowledge base optimization is an effective method for improving the performance of LLMs in specific fields.
The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object’s mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients’ physical and psychological status, thereby contributing to improvements in their health and quality of life.
Alzheimer’s disease (AD) is one of the most common forms of neurodegenerative dementia. The etiology of AD is multifactorial, and its complex pathophysiology involves tau and amyloid-β deposition, increased oxidative stress, neuroinflammation, metabolic disorders, and massive neuronal loss. Due to its complex pathology, no effective cure for AD has been found to date. Therefore, there is an unmet clinical need for the development of new drugs against AD. Natural products are known to be good sources of compounds with pharmacological activity and have potential for the development of new therapeutic agents. Naringin, a naturally occurring flavanone glycoside, is predominantly found in citrus fruits and Chinese medicinal herbs. Mounting evidence shows that naringin and its aglycone, naringenin, have direct neuroprotective effects on AD, such as anti-amyloidogenic, antioxidant, anti-acetylcholinesterase, and anti-neuroinflammatory effects, as well as metal chelation. Furthermore, they are known to improve disordered glucose/lipid metabolism, which is a high risk factor for AD. In this review, we summarize the latest data on the impact of naringin and naringenin on the molecular mechanisms involved in AD pathophysiology. Additionally, we provide an overview of the current clinical applications of naringin and naringenin. The novel delivery systems for naringin and naringenin, which can address their widespread pharmacokinetic limitations, are also discussed. The literature indicates that naringin and naringenin could be multilevel, multitargeted, and multifaceted for preventing and treating AD.
The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA.
This study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017, focusing on patients diagnosed with a solitary HCC lesion ≤5 cm in size. We compared the overall survival (OS) and cancer-specific survival (CSS) rates of these patients with those of patients who received hepatectomy, radiotherapy, or chemotherapy or who were part of a blank control group. To enhance the reliability of our findings, we employed stabilized inverse probability treatment weighting (sIPTW) and stratified analyses. Additionally, we conducted a Cox regression analysis to identify prognostic factors. XGBoost models were developed to predict 1-, 3-, and 5-year CSS. The XGBoost models were evaluated via receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) curves and so on.
Regardless of whether the data were unadjusted or adjusted for the use of sIPTWs, the 5-year OS (46.7%) and CSS (58.9%) rates were greater in the RFA group than in the radiotherapy (27.1%/35.8%), chemotherapy (32.9%/43.7%), and blank control (18.6%/30.7%) groups, but these rates were lower than those in the hepatectomy group (69.4%/78.9%). Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years. Age, race, marital status, grade, cirrhosis status, tumor size, and AFP level were selected to construct the XGBoost models based on the training cohort. The areas under the curve (AUCs) for 1, 3, and 5 years in the validation cohort were 0.88, 0.81, and 0.79, respectively. Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts.
RFA can improve the survival of patients diagnosed with a solitary HCC lesion ≤5 cm. In certain clinical scenarios, RFA achieves survival outcomes comparable to those of hepatectomy. The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.