Explore the vast range of titles available.

The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

Type 2 diabetes mellitus (T2DM) is a chronic, complex metabolic disease characterized by chronic hyperglycemia causing from insufficient insulin signaling because of insulin resistance or defective insulin secretion, and may induce severe complications and premature death. Sodium-glucose cotransporter-2 (SGLT2) inhibitors are oral drugs used to reduce hyperglycemia in patients with T2DM, including empagliflozin, ertugliflozin, dapagliflozin and canagliflozin. The primary objective of this article is to examine the clinical benefit, safety, and tolerability of the four SGLT2 inhibitors approved by the US FDA. SGLT2 inhibitors increase urinary glucose excretion via inhibiting SGLT2 to decrease renal reabsorption of filtered glucose and reduce the renal threshold for glucose. Rather than stimulating insulin release, SGLT2 inhibitors improve β-cell function by improving glucotoxicity, as well as reduce insulin resistance and increase insulin sensitivity. Early clinical trials have confirmed the beneficial effects of SGLT2 in T2DM with acceptable safety and excellent tolerability. In recent years, SGLT2 inhibitors has been successively approved by the FDA to decrease cardiovascular death and decrease the risk of stroke and cardiac attack in T2DM adults who have been diagnosed with cardiovascular disease, treating heart failure (HF) with reduced ejection fraction and HF with preserved ejection fraction, and treat diabetic kidney disease (DKD), decrease the risk of hospitalization for HF in T2DM and DKD patients. SGLT2 inhibitors are expected to be an effective treatment for T2DM patients with non alcoholic fatty liver disease. SGLT2 inhibitors have a similar safety profile to placebo or other active control groups, with major adverse events such as Ketoacidosis or hypotension and genital or urinary tract infections.

With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students’ autonomous learning, the artificial intelligence teaching system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students’ learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results.

The realization of optical non-reciprocity is crucial for many applications, and also of fundamental importance for manipulating and protecting the photons with desired time-reversal symmetry. Recently, various new mechanisms of magnetic-free non-reciprocity have been proposed and implemented, avoiding the limitation of the strong magnetic field imposed by the Faraday effect. However, due to the difficulties in separating the signal photons from the drive laser and the noise photons induced by the drive laser, these devices exhibit limited isolation performances and their quantum noise properties are rarely studied. Here, we demonstrate an approach of magnetic-free non-reciprocity by optically-induced magnetization in an atom ensemble. Excellent isolation (highest isolation ratio is 51 . 5 − 2.5 + 6.5 dB ) is observed over a power dynamic range of 7 orders of magnitude, with the noiseless property verified by quantum statistics measurements. The approach is applicable to other atoms and atom-like emitters, paving the way for future studies of integrated photonic non-reciprocal devices. Optical nonreciprocity through magneto-optical effects requires bulky apparatuses and strong magnetic fields, while magnetic-free approaches are difficult to implement. Here, the authors use optically-induced magnetisation in an atomic ensemble to get 50 dB of isolation over a large power dynamic range.

Numerous studies have used human pluripotent stem cell-derived cerebral organoids to elucidate the mystery of human brain development and model neurological diseases in vitro, but the potential for grafted organoid-based therapy in vivo remains unknown. Here, we optimized a culturing protocol capable of efficiently generating small human cerebral organoids. After transplantation into the mouse medial prefrontal cortex, the grafted human cerebral organoids survived and extended projections over 4.5 mm in length to basal brain regions within 1 month. The transplanted cerebral organoids generated human glutamatergic neurons that acquired electrophysiological maturity in the mouse brain. Importantly, the grafted human cerebral organoids functionally integrated into pre-existing neural circuits by forming bidirectional synaptic connections with the mouse host neurons. Furthermore, compared to control mice, the mice transplanted with cerebral organoids showed an increase in freezing time in response to auditory conditioned stimuli, suggesting the potentiation of the startle fear response. Our study showed that subcortical projections can be established by microtransplantation and may provide crucial insights into the therapeutic potential of human cerebral organoids for neurological diseases.

Protein lipidation is a pivotal post-translational modification that increases protein hydrophobicity and influences their function, localization, and interaction network. Emerging evidence has shown significant roles of lipidation in the tumor microenvironment (TME). However, a comprehensive review of this topic is lacking. In this review, we present an integrated and in-depth literature review of protein lipidation in the context of the TME. Specifically, we focus on three major lipidation modifications: S- prenylation, S- palmitoylation, and N- myristoylation. We emphasize how these modifications affect oncogenic signaling pathways and the complex interplay between tumor cells and the surrounding stromal and immune cells. Furthermore, we explore the therapeutic potential of targeting lipidation mechanisms in cancer treatment and discuss prospects for developing novel anticancer strategies that disrupt lipidation-dependent signaling pathways. By bridging protein lipidation with the dynamics of the TME, our review provides novel insights into the complex relationship between them that drives tumor initiation and progression.

Hydrosalpinx is a chronic inflammatory condition with high recurrence rate, and it is reported among female population having fallopian tubal factor infertility. Previously, we have reported that interventional ultrasound sclerotherapy improves endometrial receptivity and pregnancy rate with negligible adverse effects in patients suffering from hydrosalpinx. During present investigation, we have used next generation sequencing (NGS) to characterize the isomiR profiles from the endometrium of patients suffering from hydrosalpinx before and after interventional ultrasound sclerotherapy. Our results indicated that miRNA arm shift and switch remained unaffected when compared in patients before and after interventional ultrasound sclerotherapy. We observed that isomiRs with trimming at 3’ and isomiRs with canonical sequences were lower in post-treatment than in pre-treatment group. Gene ontology (GO) annotation and KEGG pathway analysis revealed that the expression of mature mir-30 was significantly lower in the pre-treatment as compared to post treatment group while the expression of mir-30 isomiR was 4.26-fold higher in pre-treatment when compared with the post-treatment group. These different expression patterns of mir-30 mature miRNA and mir-30 isomiRs in two groups are affecting the physiological function of the endometrium. Our results suggested that differential isomiR distribution in hydrosalpinx patients before and after treatment plays an important role in hydrosalpinx incidence and can help in designing novel strategy for the treatment of hydrosalpinx in female population.

Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Non-magnetic non-reciprocal transparency and amplification is experimentally achieved by optomechanics using a whispering-gallery microresonator. The idea may lead to integrated all-optical isolators or non-reciprocal phase shifters. Non-reciprocal devices, such as circulators and isolators, are indispensable components in classical and quantum information processing in integrated photonic circuits 1 . Aside from these applications, the non-reciprocal phase shift is of fundamental interest for exploring exotic topological photonics 2 , such as the realization of chiral edge states and topological protection 3 , 4 . However, incorporating low-optical-loss magnetic materials into a photonic chip is technically challenging 5 . In this study we experimentally demonstrate non-magnetic non-reciprocity using optomechanical interactions in a whispering gallery microresonator, as proposed in a previous work 6 . Optomechanically induced non-reciprocal transparency and amplification are observed and a non-reciprocal phase shift of up to 40° is also demonstrated. The underlying mechanism of optomechanically induced non-reciprocity has great potential for all-optical controllable isolators and circulators, as well as non-reciprocal phase shifters in integrated photonic chips.

Non-reciprocal devices, which allow non-reciprocal signal routing, serve as fundamental elements in photonic and microwave circuits and are crucial in both classical and quantum information processing. The radiation-pressure-induced coupling between light and mechanical motion in travelling-wave resonators has been exploited to break the Lorentz reciprocity, enabling non-reciprocal devices without magnetic materials. Here, we experimentally demonstrate a reconfigurable non-reciprocal device with alternative functions as either a circulator or a directional amplifier via optomechanically induced coherent photon–phonon conversion or gain. The demonstrated device exhibits considerable flexibility and offers exciting opportunities for combining reconfigurability, non-reciprocity and active properties in single photonic devices, which can also be generalized to microwave and acoustic circuits. Upconversion nanoparticles, which convert lower-energy light into higher-energy light, have many potential applications including sensing and imaging. Here, Wen et al. review recent advances that have addressed concentration quenching and enabled increasingly bright nanoparticles, opening up their full potential.