Project description:Drought represents a significant stress to microorganisms and is known to reduce microbial activity and organic matter decomposition in Mediterranean ecosystems. However, we lack a detailed understanding of the drought stress response of microbial decomposers. Here we present metatranscriptomic data on the physiological response of in situ microbial communities on plant litter to long-term drought in Californian grass and shrub ecosystems.
Project description:Interventions: We enrolled 415 subjects who under went colonoscopy at our institution from January 2021 to March 2022.The randomization method used in this study was the numbered container method according to the CONSORT guideline. The allocation message was generated by the computer program of the Python random module and then sealed in sequentially numbered identical containers according to the allocation sequence. After an eligibility check, the endoscopist opened theallocation message, and subsequently, examinees were randomly assigned to the CADe or control groups.;D016449;Numbered container method
Primary outcome(s): Adenoma detection rate in total colonoscopy
Study Design: randomized controlled trial, single blind, no treatment control/standard of care control, parallel assignment, diagnostic purpose
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description:Assembled contigs of 760 MetaHIT metagenomes
Project description:We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.
Project description:IntroductionThe detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study.Methods and resultsThis retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance.ConclusionThe proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.
Project description:In the recent years, RNA silencing has been studied extensively to be a conserved regulatory process in plants. In the antiviral silencing, the intermediate double-stranded RNA form during the replication of RNA viruses were recognized and processed into abundant of overlapping viral siRNA (viRNAs). Accordingly, the cloned viRNAs could be conversely assembled into some contigs of viruses, which is recently exploited for identifying new viruses and their genome sequences.To obtain rapidly the complete genome sequence of BYSMV, we carried out deep sequencing of small RNAs from healthy and BYSMV infected wheat, respectively. Thirteen contigs were assembled from the overlapping viRNAs only present in the infected wheat but not in the healthy wheat. The results of BLAST showed that ten contigs shared about 96% identity with the reported L gene of BYSMV isolate Zanjan-1.
Project description:Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.
Project description:Artificial intelligence (AI) is one hotspot of research in the field of modern medical technology. Medical AI has been applied to various areas and has two main branches including virtual and physical. Recently, Chinese State Council issued a guideline on developing AI and indicated that the widespread application of AI will improve the level of precision in medical services and achieve the intelligent medical care. Medical resources, especially the high-quality resources, are deficient across the entire health service system in China. AI technologies, such that virtual AI and telemedical technology, are expected to overcome the current limitations of the distribution of medical resources and relieve the pressure associated with obtaining high-quality health care.