Acinetobacter baumannii: an evolving and cunning opponent

Acinetobacter baumannii is one of the most common multidrug-resistant pathogens causing nosocomial infections. The prevalence of multidrug-resistant A. baumannii infections is increasing because of several factors, including unregulated antibiotic use. A. baumannii drug resistance rate is high; in particular, its resistance rates for tigecycline and polymyxin—the drugs of last resort for extensively drug-resistant A. baumannii—has been increasing annually. Patients with a severe infection of extensively antibiotic-resistant A. baumannii demonstrate a high mortality rate along with a poor prognosis, which makes treating them challenging. Through carbapenem enzyme production and other relevant mechanisms, A. baumannii has rapidly acquired a strong resistance to carbapenem antibiotics—once considered a class of strong antibacterials for A. baumannii infection treatment. Therefore, understanding the resistance mechanism of A. baumannii is particularly crucial. This review summarizes mechanisms underlying common antimicrobial resistance in A. baumannii, particularly those underlying tigecycline and polymyxin resistance. This review will serve as a reference for reasonable antibiotic use at clinics, as well as new antibiotic development

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model

During the development of large language models (LLMs), the scale and quality of the pre-training data play a crucial role in shaping LLMs' capabilities. To accelerate the research of LLMs, several large-scale datasets, such as C4 [1], Pile [2], RefinedWeb [3] and WanJuan [4], have been released to the public. However, most of the released corpus focus mainly on English, and there is still lack of complete tool-chain for extracting clean texts from web data. Furthermore, fine-grained information of the corpus, e.g. the quality of each text, is missing. To address these challenges, we propose in this paper a new complete tool-chain EvalWeb to extract Chinese clean texts from noisy web data. First, similar to previous work, manually crafted rules are employed to discard explicit noisy texts from the raw crawled web contents. Second, a well-designed evaluation model is leveraged to assess the remaining relatively clean data, and each text is assigned a specific quality score. Finally, we can easily utilize an appropriate threshold to select the high-quality pre-training data for Chinese. Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1.42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds. We also release a much cleaner subset of 600 GB Chinese data with the quality exceeding 90%

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

Detection and analysis of human papillomavirus (HPV) DNA in breast cancer patients by an effective method of HPV capture

Despite an increase in the number of molecular epidemiological studies conducted in recent years to evaluate the association between human papillomavirus (HPV) and the risk of breast carcinoma, these studies remain inconclusive. Here we aim to detect HPV DNA in various tissues from patients with breast carcinoma using the method of HPV capture combined with massive paralleled sequencing (MPS). To validate the confidence of our methods, 15 cervical cancer samples were tested by PCR and the new method. Results showed that there was 100% consistence between the two methods.DNA from peripheral blood, tumor tissue, adjacent lymph nodes and adjacent normal tissue were collected from seven malignant breast cancer patients, and HPV type 16(HPV16) was detected in 1/7, 1/7, 1/7and 1/7 of patients respectively. Peripheral blood, tumor tissue and adjacent normal tissue were also collected from two patients with benign breast tumor, and 1/2, 2/2 and 2/2 was detected to have HPV16 DNA respectively. MPS metrics including mapping ratio, coverage, depth and SNVs were provided to characterize HPV in samples. The average coverage was 69% and 61.2% for malignant and benign samples respectively. 126 SNVs were identified in all 9 samples. The maximum number of SNVs was located in the gene of E2 and E4 among all samples. Our study not only provided an efficient method to capture HPV DNA, but detected the SNVS, coverage, SNV type and depth. The finding has provided further clue of association between HPV16 and breast cancer

Charging Station Management Strategy for Returns Maximization via Improved TD3 Deep Reinforcement Learning

Maximizing the return on electric vehicle charging station (EVCS) operation helps to expand the EVCS, thus expanding the EV (electric vehicle) stock and better addressing climate change. However, in the face of dynamic regulation scenarios with large data, multiple variables, and low time scales, the existing regulation strategies aiming at maximizing EVCS returns many times fail to meet the demand. To handle increasingly complex regulation scenarios, a deep reinforcement learning algorithm (DRL) based on the improved twin delayed deep deterministic policy gradient (TD3) is used to construct basic energy management strategies in this paper. To enable the strategy to be more suitable for the goal of real-time energy regulation strategy, we used Thompson sampling strategy to improve TD3’s exploration noise sampling strategy, which greatly accelerated the initial convergence of TD3 during training. Also, we use marginalised importance sampling to calculate the Q-return function for TD3, which ensures that the constructed strategies are more likely to learn high-value experiences while having higher robustness. It is shown in numerical experiments that the charging station management strategy (CSMS) based on the modified TD3 obtains the fastest convergence speed and the highest robustness and achieves the largest operational returns compared to the CSMS constructed using deep deterministic policy gradient (DDPG), actor-critic using Kronecker-factored trust region (ACKTR), trust region policy optimization (TRPO), proximal policy optimization (PPO), soft actor-critic (SAC), and the original TD3

Combined effects of reproductive and hormone factors and obesity on the prevalence of knee osteoarthritis and knee pain among middle-aged or older Chinese women: a cross-sectional study

Abstract Background Knee osteoarthritis (KOA) is one form of degenerative arthritis that results from the breakdown of cartilage and underlying bone. The prevalence of KOA is considerably higher in women than in men; however, the reason for this difference has not been thoroughly elucidated to date. The aim of the present study was to estimate the effects of reproductive and hormone factors and obesity on KOA prevalence among Chinese women. Methods The cross-sectional study included 7510 women with a mean age of 62.6 ± 8.6 years. Knee pain was defined as pain or aching stiffness on most days for at least 1 month during the past 12 months or persistent pain or aching stiffness within the past week. Clinical KOA was diagnosed based on both pain complaints and a Kellgren-Lawrence grade ≥ 2 X-ray radiograph of at least one knee. Results Oral contraceptives use (OR 1.18, 1.05–1.34), ≥3 pregnancies (1.38, 1.20–1.60), and postmenopausal hormone replacement therapy (HT) (1.59, 1.23–2.06) were positively associated with knee pain, while oral contraceptives use (1.28, 1.04–1.57), and HT (1.79, 1.21–2.65) were positively associated with clinical KOA. Obesity and oral contraceptives use showed additive and multiplicative effects on knee pain. The OR for knee pain among women with a BMI ≥24 kg/m2 and oral contraceptives use was 2.00 (1.68–2.38) compared with women with a BMI < 24 kg/m2 and no oral contraceptives use. Conclusions A high number of pregnancies, oral contraceptives use, and HT are independent risk factors for KOA, and the effects of reproductive and hormone factors on KOA may be increased by obesity

MicroRNA-409-3p Functions as a Tumor Suppressor in Human Lung Adenocarcinoma by Targeting c-Met

Background/Aims: Dysregulation of microRNAs is correlated with tumor development. The aim of this study is to investigate the clinicopathologic and prognostic significance of microRNA (miR)-409-3p and its tumor suppressor roles in lung adenocarcinoma (LAD). Methods: Quantitative real-time PCR (qRT-PCR) was performed to detect miR-409-3p expression in LAD tissues and corresponding noncancerous tissues. Additionally, the correlations of miR-409-3p expression with clinicopathologic factors and prognosis of patients were statistically analyzed. Next, we investigated whether miR-409-3p could function as a tumor suppressor in LAD cells via regulation of Akt signaling by targeting receptor tyrosine kinase (c-Met). Results: MiR-409-3p was significantly downregulated in LAD tissues compared with corresponding noncancerous tissues. Low miR-409-3p expression was observed to be significantly correlated with poorer tumor differentiation, advanced pTNM stage and higher incidence of lymph node metastasis. Multivariate Cox regression analyses showed that miR-409-3p expression was an independent prognostic factor for LAD patients. Functional analyses indicated that miR-409-3p could inhibit growth, induce apoptosis, reduce migration and invasion in LAD cells via inactivation of Akt signaling by targeting c-Met. Conclusions: MiR-409-3p was an independent prognostic factor and functioned as a tumor suppressor in LAD via regulation of Akt signaling by targeting c-Met

Analysis for discharge within 2 days after thoracoscopic anatomic lung cancer surgery

Abstract Objectives The risk and beneficial factors of early discharge after thoracoscopic anatomic lung cancer surgery are unknown, and this study aims to investigate predictors and associated 30‐day readmission for early discharge. Methods We performed a single‐center retrospective analysis of 10,834 consecutive patients who underwent thoracoscopic anatomic lung cancer surgery. Two groups were determined based on discharge date: “discharged by postoperative Day 2” and “discharged after postoperative Day 2.” Univariable and multivariable analysis were conducted to identify predictors for discharge. Using propensity score matching (PSM) to compare 30‐day readmission rate between two cohorts. Results A total of 1911 patients were discharged by postoperative Day 2. Multivariable analysis identified older age (odds ratio (OR) = 1.014, p < 0.001), male sex (OR = 1.183, p = 0.003), larger tumor size (OR = 1.248, p < 0.001), pleural adhesions (OR = 1.638, p = 0.043), lymph nodes calcification (OR = 1.443, p = 0.009), advanced clinical T stage (vs. T < 2, OR = 1.470, p = 0.010), lobectomy resection (vs. segmentectomy resection, OR = 2.145, p < 0.001) and prolonged operative time (OR = 1.011, p < 0.001) as independent risk factors for discharge after postoperative Day 2. Three adjustable variables including higher FEV1/FVC (OR = 0.989, p = 0.001), general anesthesia (GA) plus thoracic paravertebral blockade (vs. GA alone, OR = 0.823, p = 0.006) and uni‐portal thoracoscopic surgery (vs. multi‐portal, OR = 0.349, p < 0.001) were associated with a decreased likelihood of discharge after postoperative Day 2. Before and after a 1:1 PSM, discharged by postoperative Day 2 did not increase the risk of 30‐day readmission compared to counterparts. Conclusions Carefully selected patients can be safely discharged within 2 days after thoracoscopic anatomic lung cancer surgery. Three modifiable variables may be favorable for promoting discharge by postoperative Day 2

Upl-sfda:Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.</p