Search

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability. [ABSTRACT FROM AUTHOR]

Mammalian DNA derived from invertebrates (iDNA), including blowfly iDNA, is an alternative tool to conventional camera trapping in assessing mammalian diversity. The method has been used in tropical mammal surveillance but no attempt has been made to compare the efficacy of blowfly iDNA and camera trapping for monitoring mammal diversity in subtropical forests. We compared the blowfly iDNA monitoring with camera trapping to assess mammal diversity in the subtropical Jiulongfeng Nature Reserve (JLF), China, over a one-month period. The camera traps captured 2508 animal photos of 11 species belonging to four orders, eight genera, and eight families, whereas the blowfly iDNA method successfully detected ten species from six orders, eight genera, and eight families in JLF. Both methods were complementing each other instead of competing due to the low overlaps of mammal species detected. Of the total number of mammal species listed in JLF's threatened list, 40% and 10% were detected through camera traps and blowfly iDNA methods, respectively. The estimated species richness curves indicated that combining camera traps and blowfly traps would increase the detection of mammal species. The strategy would significantly contribute to mammalian diversity surveillance and conservation programs in the tropical and subtropical forests. [ABSTRACT FROM AUTHOR]

ABSTRACT We evaluated the diagnostic accuracy of various international guideline criteria for identifying HBeAg‐positive chronic HBV infection patients with no significant liver disease. A total of 1108 HBeAg‐positive CHB patients were retrospectively enrolled. The guidelines assessed included those from the European Association for the Study of the Liver (EASL) 2017, the American Association for the Study of the Liver Disease (AASLD) 2018, the Asian Pacific Association for the Study of the Liver (APASL) 2015 and the Chinese Society of Hepatology (CSH) 2022. The CSH criteria demonstrated a higher proportion of patients with G0‐1 and S0‐1 (82.9%) compared to the EASL (75.9%), AASLD (75.3%) and APASL groups (58.8%). Additionally, the CSH criteria exhibited a significantly higher predictive value (AUC 0.782, 95% CI 0.754–0.809) than the EASL (AUC 0.765, 95% CI 0.737–0.793), AASLD (AUC 0.749, 95% CI 0.720–0.778) and APASL (AUC 0.720, 95% CI 0.690–0.750) criteria for identifying G0‐1 and S0‐1. Adding quantitative HBsAg levels (> 104 IU/mL) to the EASL, AASLD and APASL criteria improved diagnostic performance. Consequently, the CSH guideline thresholds showed higher accuracy in identifying Chinese HBeAg‐positive patients with no significant liver disease compared to EASL, AASLD and APASL criteria, emphasising the importance of considering quantitative HBsAg in the evaluation of HBeAg‐positive chronic HBV infection. [ABSTRACT FROM AUTHOR]