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Several studies have evaluated the reliability of using temperature sensors placed in different locations on the body to identify the day of ovulation. However, such demonstrations are lacking for axillary temperature wearable devices. This study aimed to evaluate the accuracy with which an axillary temperature armband sensor (Tempdrop) identifies the day of ovulation and the fertile window, using the Clearblue Connected Ovulation Test System as the reference method. A total of 194 cycles were analyzed from 125 women that participated in the study between April 2023 and June 2024. The performance parameters were high: the sensitivity (96.8% (95% CI 95.6; 97.7)), specificity (99.1% (98.8; 99.4)), accuracy (98.6% (98.2; 98.9)), positive predictive value (96.8% (95.6; 97.7)) and negative predictive value (99.1% (98.8; 99.4)). Furthermore, the results revealed a remarkably clear and better-than-expected change in temperature around the time of ovulation. This axillary temperature wearable sensor is an effective alternative to urine ovulation tests for determining the timing of ovulation. Another advantage is that it provides a clear temperature curve that can be used to evaluate the quality of the luteal phase.

Surgical instrument detection in robot-assisted surgery videos is an import vision component for these systems. Most of the current deep learning methods focus on single-tool detection and suffer from low detection speed. To address this, the authors propose a novel frame-by-frame detection method using a cascading convolutional neural network (CNN) which consists of two different CNNs for real-time multi-tool detection. An hourglass network and a modified visual geometry group (VGG) network are applied to jointly predict the localisation. The former CNN outputs detection heatmaps representing the location of tool tip areas, and the latter performs bounding-box regression for tool tip areas on these heatmaps stacked with input RGB image frames. The authors’ method is tested on the publicly available EndoVis Challenge dataset and the ATLAS Dione dataset. The experimental results show that their method achieves better performance than mainstream detection methods in terms of detection accuracy and speed.

The widespread spread of bacterial antimicrobial resistance (AMR) and multidrug-resistant bacteria poses a significant threat to global public health. Traditional methods for detecting bacterial AMR are simple, reproducible, and intuitive, requiring long time incubation and high labor intensity. To quickly identify and detect bacterial AMR is urgent for clinical treatment to reduce mortality rate, and many new methods and technologies were required to be developed. This review summarizes the current phenotypic and genotypic detection methods for bacterial AMR. Phenotypic detection methods mainly include antimicrobial susceptibility tests, while genotypic detection methods have higher sensitivity and specificity and can detect known or even unknown drug resistance genes. However, most of the current tests are either genotypic or phenotypic and rarely combined. Combining the advantages of phenotypic and genotypic methods, combined with the joint application of multiple rapid detection methods may be the trend for future AMR testing. Driven by rapid diagnostic technology, big data analysis, and artificial intelligence, detection methods of bacterial AMR are expected to constantly develop and innovate. Adopting rational detection methods and scientific data analysis can better address the challenges of bacterial AMR and ensure human health and social well-being.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

This study presents a hybrid data‐driven physics model‐based framework for real‐time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real‐time monitoring becomes more vulnerable to cyber‐attacks like false data injections (FDIs). Although smart grids cyber‐physical security has an extensive scope, this study focuses on FDI attacks, which are modelled as bad data. State‐of‐the‐art strategies for FDI detection in real‐time monitoring rely on physics model‐based weighted least‐squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modelling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this study presents a framework which explores the use of data‐driven anomaly detection methods in conjunction with physics model‐based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118‐bus system.

Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing number of power systems, SG data becomes increasingly vulnerable to cyber‐attacks. Classic analytic physics‐model based bad data detection methods may not detect these attacks. Recently, physics‐model and data‐driven methods have been proposed to use the temporal aspect of the data to learn multivariate statistics of the SG such as mean and covariance matrices of voltages, power flows etc., and then make decisions based on fixed values of these statistics. However, as loads and generation change within a system, these statistics may change rapidly. In this study, an adaptive data‐driven anomaly detection framework, Ensemble CorrDet with Adaptive Statistics (ECD‐AS), is proposed to detect false data injection cyber‐attacks under a constantly changing system state. ECD‐AS is tested on the IEEE 118‐bus system for 15 different sets of training and test datasets for a variety of current state‐of‐the‐art bad data detection strategies. Experimental results show that the proposed ECD‐AS solution outperforms the related strategies due to its unique ability to capture and account for rapidly changing statistics in SG.

The popularity of testosterone among drug users is due to its powerful effects on muscle strength and mass. Important mechanisms behind the myotrophic effects of testosterone were uncovered both in athletes using steroids for several years and in short‐term controlled studies. Both long‐term and short‐term steroid usage accentuates the degree of fibre hypertrophy in human skeletal muscle by enhancing protein synthesis. A mechanism by which testosterone facilitates the hypertrophy of muscle fibres is the activation of satellite cells and the promotion of myonuclear accretion when existing myonuclei become unable to sustain further enhancement of protein synthesis. Interestingly, long‐term steroid usage also enhances the frequency of fibres with centrally located myonuclei, which implies the occurrence of a high regenerative activity. Under the action of testosterone, some daughter cells generated by satellite cell proliferation may escape differentiation and return to quiescence, which help to replenish the satellite cell reserve pool. However, whether long‐term steroid usage induces adverse effects of satellite cells remains unknown. Testosterone might also favour the commitment of pluripotent precursor cells into myotubes and inhibit adipogenic differentiation. The effects of testosterone on skeletal muscle are thought to be mediated via androgen receptors expressed in myonuclei and satellite cells. Some evidence also suggests the existence of an androgen‐receptor‐independent pathway. Clearly, testosterone abuse is associated with an intense recruitment of multiple myogenic pathways. This provides an unfair advantage over non‐drug users. The long‐term consequences on the regenerative capacity of skeletal muscle are unknown. British Journal of Pharmacology (2008) 154, 522–528; doi:fn1; published online 14 April 2008

Introduction: Staphylococcal biofilms are prominent cause for acute and chronic infection both in hospital and community settings across the world. Current study explores biofilm formation by Staphylococcus aureus isolates from clinical samples by different methods. Methodology: Standard techniques used for the characterization of S.aureus. Qualitative and quantitative biofilm formation was assessed by Congo red Agar, Tube and Microtiter plate methods. Results: A total of 188 clinical isolates of S.aureus were screened for biofilm formation and 72 (38.29%) of them were found to be biofilm producers, 34 (18.08%) strong, 38 (20.21%) moderate. The remaining 116 (61.7%) were weak/ non biofilm producers. Maximum biofilm formers were recorded in pus samples (39.06%), followed by isolates from blood (38.23%) and urine (34.61%). Statistical analysis for the formation of biofilm indicated that Microtiter plate method is the most sensitive and specific method for screening biofilm production. Conclusions: Biofilm formation is one of the influential virulence factor in staphylococcal pathogenesis and persistence. Microtiter plate and Congo red agar remain as reliable methods for the qualitative and quantitative estimation of biofilm formation. Monitoring of biofilm formation in various etiological agents will help in determining the severity of infection.

Exosomes derived from tumor cells contain various molecular components, such as proteins, RNA, DNA, lipids, and carbohydrates. These components play a crucial role in all stages of tumorigenesis and development. Moreover, they reflect the physiological and pathological status of parental tumor cells. Recently, tumor-derived exosomes have become popular biomarkers for non-invasive liquid biopsy and the diagnosis of numerous cancers. The interdisciplinary significance of exosomes research has also attracted growing enthusiasm. However, the intrinsic nature of tumor-derived exosomes requires advanced methods to detect and evaluate the complex biofluid. This review analyzes the relationship between exosomes and tumors. It also summarizes the exosomal biological origin, composition, and application of molecular markers in clinical cancer diagnosis. Remarkably, this paper constitutes a comprehensive summary of the innovative research on numerous detection strategies for tumor-derived exosomes with the intent of providing a theoretical basis and reference for early diagnosis and clinical treatment of cancer.

Objective： This review aimed to update the progress of microRNA （miRNA） in early detection of ovarian cancer. We discussed the current clinical diagnosis methods and biomarkers of ovarian cancer, especially the methods of miRNA in early detection of ovarian cancer. Data Sources： We collected all relevant studies about miRNA and ovarian cancer in PubMed and CNKI from 1995 to 2015. Study Selection： We included all relevant studies concerning miRNA in early detection of ovarian cancer, and excluded the duplicated articles. Results： miRNAs play a key role in various biological processes of ovarian cancer, such as development, proliferation, differentiation, apoptosis and metastasis, and these phenomena appear in the early-stage. Therefore, miRNA can be used as a new biomarker for early diagnosis of ovarian cancer, intervention on miRNA expression of known target genes, and potential target genes can achieve the effect of early prevention. With the development ofnanoscience and technology, analysis methods ofmiRNA are also quickly developed, which may provide better characterization of early detection of ovarian cancer. Conclusions： In the near future, miRNA therapy could be a powerful tool for ovarian cancer prevention and treatment, and combining with the new analysis technology and new nanomaterials, point-of-care tests for miRNA with high throughput, high sensitivity, and strong specificity are developed to achieve the application of diagnostic kits in screening of early ovarian cancer.