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Some syphilis patients remain in a serologically active state after the recommended therapy. We currently know too little about the characteristics of this serological response. We conducted a cohort study using the clinical database from Zhongshan Hospital, Medical College of Xiamen. In total, 1,327 HIV-negative patients with primary, secondary, latent, and tertiary syphilis were enrolled. Bivariate and multivariate analyses were utilised to identify factors associated with a serological cure and serofast state in syphilis patients one year after therapy. Chi-square tests were used to determine the differences in the serological cure rate across different therapy time points. One year after the recommended therapy, 870 patients achieved a serological cure, and 457 patients (34.4%) remained in the serofast state. The serological cure rate increased only within the first 6 months. The bivariate analysis indicated that male or younger patients had a higher likelihood of a serological cure than female or older patients. Having a baseline titre ≤ 1∶2 or ≥ 1∶64 was associated with an increased likelihood of a serological cure. The serological cure rate decreased for the different disease stages in the order of primary, secondary, latent, and tertiary syphilis. A distinction should be drawn between early and late syphilis. The multivariate analysis indicated that a serological cure was significantly associated with the disease phase, gender, age, and baseline rapid plasma reagin (RPR) titre. The serofast state is common in clinical work. After one year of the recommended therapy, quite a few syphilis patients remained RPR positive. The primary endpoint of the study indicated that disease phase, gender, age and baseline RPR titre were crucial factors associated with a serological cure.

Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients with locoregionally advanced nasopharyngeal carcinoma. In this multicentre, retrospective, cohort analysis, we included 937 patients with locoregionally advanced nasopharyngeal carcinoma from three Chinese hospitals: the Sun Yat-sen University Cancer Center (Guangzhou, China), the Affiliated Hospital of Guilin Medical University (Guilin, China), and the First People's Hospital of Foshan (Foshan, China). Using microarray analysis, we profiled mRNA gene expression between 24 paired locoregionally advanced nasopharyngeal carcinoma tumours from patients at Sun Yat-sen University Cancer Center with or without distant metastasis after radical treatment. Differentially expressed genes were examined using digital expression profiling in a training cohort (Guangzhou training cohort; n=410) to build a gene classifier using a penalised regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (Guangzhou internal validation cohort, n=204) and two external independent cohorts (Guilin cohort, n=165; Foshan cohort, n=158). The primary endpoint was distant metastasis-free survival. Secondary endpoints were disease-free survival and overall survival. We identified 137 differentially expressed genes between metastatic and non-metastatic locoregionally advanced nasopharyngeal carcinoma tissues. A distant metastasis gene signature for locoregionally advanced nasopharyngeal carcinoma (DMGN) that consisted of 13 genes was generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter distant metastasis-free survival (hazard ratio [HR] 4·93, 95% CI 2·99–8·16; p<0·0001), disease-free survival (HR 3·51, 2·43–5·07; p<0·0001), and overall survival (HR 3·22, 2·18–4·76; p<0·0001) than patients with low-risk scores. The prognostic accuracy of DMGN was validated in the internal and external cohorts. Furthermore, among patients with low-risk scores in the combined training and internal cohorts, concurrent chemotherapy improved distant metastasis-free survival compared with those patients who did not receive concurrent chemotherapy (HR 0·40, 95% CI 0·19–0·83; p=0·011), whereas patients with high-risk scores did not benefit from concurrent chemotherapy (HR 1·03, 0·71–1·50; p=0·876). This was also validated in the two external cohorts combined. We developed a nomogram based on the DMGN and other variables that predicted an individual's risk of distant metastasis, which was strengthened by adding Epstein–Barr virus DNA status. The DMGN is a reliable prognostic tool for distant metastasis in patients with locoregionally advanced nasopharyngeal carcinoma and might be able to predict which patients benefit from concurrent chemotherapy. It has the potential to guide treatment decisions for patients at different risk of distant metastasis. The National Natural Science Foundation of China, the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, the Natural Science Foundation of Guang Dong Province, the National Key Research and Development Program of China, the Innovation Team Development Plan of the Ministry of Education, the Health & Medical Collaborative Innovation Project of Guangzhou City, China, and the Program of Introducing Talents of Discipline to Universities.

\"Past, present and future \"The archaeological materials recovered from the Anyang excavations ... in the period between 1928 and 1937...have laid a new foundation for the study of ancient China (Li, C. 1977: ix).\" When inscribed oracle bones and enormous material remains were found through scientific excavation in Anyang in 1928, the historicity of the Shang dynasty was confirmed beyond dispute for the first time (Li, C. 1977: ix-xi). This excavation thus marked the beginning of a modern Chinese archaeology endowed with great potential to reveal much of China's ancient history.. Half a century later, Chinese archaeology had made many unprecedented discoveries which surprised the world, leading Glyn Daniel to believe that \"a new awareness of the importance of China will be a key development in archaeology in the decades ahead (Daniel 1981: 211). This enthusiasm was soon shared by the Chinese archaeologists when Su Bingqi announced that \"the Golden Age of Chinese archaeology is arriving (Su, B. 1994: 139--140)\". In recent decades, archaeology has continuously prospered, becoming one of the most rapidly developing fields in social science in China\"-- Provided by publisher.

Campus safety is an essential concern as schools, colleges, and universities work to create secure environments for students, staff, and visitors. Many existing security systems are not fully effective at detecting unusual behaviors or sending fast alerts, which can delay responses to potential threats. To improve this, the research introduces DeepCARE(Deep-learning-based Campus Anomaly & Risk Evaluation), a deep learning-based framework designed to enhance behavior recognition and early warning for campus security. DeepCARE combines convolutional neural networks (CNNs) with long short-term memory (LSTM) networks to process real-time video footage and detect abnormal activities such as aggression, unauthorized access, and people staying too long in restricted areas. The system’s main feature is its hybrid model, where CNNs extract key visual features from surveillance footage while LSTM networks analyze these features over time to recognize behavior patterns. DeepCARE also includes an anomaly detection module using autoencoders, which helps improve the system’s accuracy and reduces false alarms. This makes DeepCARE a flexible and scalable solution, suitable not only for educational campuses but also for public spaces, transport hubs, and smart cities. By applying deep learning, DeepCARE supports early risk detection and faster response times, helping security teams create safer spaces. Experimental results show that DeepCARE achieves a behavior recognition accuracy of 94.5%, performs 8% better than traditional methods, and shortens emergency response times by 30%.

Deep learning has revolutionized a number of applications in artificial intelligence, including speech, vision, natural language, game playing, healthcare, and robotics. In particular, the recent striking success of deep learning in a wide variety of Natural Language Processing (NLP) application areas has been taken as a landmark of deep learning in one of the most important tasks in Artificial Intelligence. The book presents the state-of-the-art of deep learning research, and its applications in major NLP tasks including speech recognition, lexical analysis, parsing, knowledge graph, machine translation, information retrieval, question answering, sentiment analysis, social computing, spoken language understanding, and dialogue systems. The self-contained, comprehensive chapters have been written by leading researchers in the field. It appeals undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers and anyone interested in deep learning and natural language processing.

Medicines for the treatment of 2019-novel coronavirus (2019-nCoV) infections are urgently needed. However, drug screening using live 2019-nCoV requires high-level biosafety facilities, which imposes an obstacle for those institutions without such facilities or 2019-nCoV. This study aims to repurpose the clinically approved drugs for the treatment of coronavirus disease 2019 (COVID-19) in a 2019-nCoV-related coronavirus model. A 2019-nCoV-related pangolin coronavirus GX_P2V/pangolin/2017/Guangxi was described. Whether GX_P2V uses angiotensin-converting enzyme 2 (ACE2) as the cell receptor was investigated by using small interfering RNA (siRNA)-mediated silencing of ACE2. The pangolin coronavirus model was used to identify drug candidates for treating 2019-nCoV infection. Two libraries of 2406 clinically approved drugs were screened for their ability to inhibit cytopathic effects on Vero E6 cells by GX_P2V infection. The anti-viral activities and anti-viral mechanisms of potential drugs were further investigated. Viral yields of RNAs and infectious particles were quantified by quantitative real-time polymerase chain reaction (qRT-PCR) and plaque assay, respectively. The spike protein of coronavirus GX_P2V shares 92.2% amino acid identity with that of 2019-nCoV isolate Wuhan-hu-1, and uses ACE2 as the receptor for infection just like 2019-nCoV. Three drugs, including cepharanthine (CEP), selamectin, and mefloquine hydrochloride, exhibited complete inhibition of cytopathic effects in cell culture at 10 μmol/L. CEP demonstrated the most potent inhibition of GX_P2V infection, with a concentration for 50% of maximal effect [EC50] of 0.98 μmol/L. The viral RNA yield in cells treated with 10 μmol/L CEP was 15,393-fold lower than in cells without CEP treatment ([6.48 ± 0.02] × 10vs. 1.00 ± 0.12, t = 150.38, P < 0.001) at 72 h post-infection (p.i.). Plaque assays found no production of live viruses in media containing 10 μmol/L CEP at 48 h p.i. Furthermore, we found CEP had potent anti-viral activities against both viral entry (0.46 ± 0.12, vs.1.00 ± 0.37, t = 2.42, P < 0.05) and viral replication ([6.18 ± 0.95] × 10vs. 1.00 ± 0.43, t = 3.98, P < 0.05). Our pangolin coronavirus GX_P2V is a workable model for 2019-nCoV research. CEP, selamectin, and mefloquine hydrochloride are potential drugs for treating 2019-nCoV infection. Our results strongly suggest that CEP is a wide-spectrum inhibitor of pan-betacoronavirus, and further study of CEP for treatment of 2019-nCoV infection is warranted.

Lung cancer is often diagnosed at an advanced stage and has a poor prognosis. Conventional treatments are not effective for metastatic lung cancer therapy. Although some of molecular targets have been identified with favorable response, those targets cannot be exploited due to the lack of suitable drug carriers. Lung cancer cell-derived exosomes (LCCDEs) receive recent interest in its role in carcinogenesis, diagnosis, therapy, and prognosis of lung cancer due to its biological functions and natural ability to carry donor cell biomolecules. LCCDEs can promote cell proliferation and metastasis, affect angiogenesis, modulate antitumor immune responses during lung cancer carcinogenesis, regulate drug resistance in lung cancer therapy, and be now considered an important component in liquid biopsy assessments for detecting lung cancer. Therapeutic deliverable exosomes are emerging as promising drug delivery agents specifically to tumor high precision medicine because of their natural intercellular communication role, excellent biocompatibility, low immunogenicity, low toxicity, long blood circulation ability, biodegradable characteristics, and their ability to cross various biological barriers. Several studies are currently underway to develop novel diagnostic and prognostic modalities using LCCDEs, and to develop methods of exploiting exosomes for use as efficient drug delivery vehicles. Current status of lung cancer and extensive applicability of LCCDEs are illustrated in this review. The promising data and technologies indicate that the approach on LCCDEs implies the potential application of LCCDEs to clinical management of lung cancer patients.