Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.

BACKGROUND: Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. METHODS: All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. RESULTS: LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. CONCLUSIONS: LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

The effects of multiwalled carbon nanotubes and Bacillus subtilis treatments on the salt tolerance of maize seedlings.

Nanomaterials, including multiwalled carbon nanotubes (MWCNTs), have been recently applied in agriculture to improve stress resistance, leading to contradictory findings for antioxidant responses and mineral nutrient uptake. A pot experiment involving maize in low-salinity sandy loam soils was conducted with the application of different concentrations (0, 20, 50 mg/L) of MWCNTs and the growth-promoting rhizobacterium Bacillus subtilis (B. subtilis). The dose-dependent effects of MWCNTs were confirmed: 20 mg/L MWCNTs significantly promoted the accumulation of osmolytes in maize, particularly K+ in the leaves and roots, increased the leaf indoleacetic acid content, decreased the leaf abscisic acid content; but the above-mentioned promoting effects decreased significantly in 50 mg/L MWCNTs-treated plants. We observed a synergistic effect of the combined application of MWCNTs and B. subtilis on plant salt tolerance. The increased lipid peroxidation and antioxidant-like proline, peroxidase (POD), and catalase (CAT) activities suggested that MWCNTs induced oxidative stress in maize growing in low-salinity soils. B. subtilis reduced the oxidative stress caused by MWCNTs, as indicated by a lower content of malondialdehyde (MDA). The MWCNTs significantly increased the leaf Na+ content and leaf Na+/K+ ratio; however, when applied in combination with B. subtilis, the leaf Na+/K+ ratio decreased sharply to 69% and 44%, respectively, compared to those of the control (CK) group, the contents of which were partially regulated by abscisic acid and nitrate, according to the results of the structural equation model (SEM). Overall, the increased osmolytes and well-regulated Na+/K+ balance and transport in plants after the combined application of MWCNTs and B. subtilis reveal great potential for their use in combating abiotic stress.

A safe and effective surgical method for complex pyriform sinus fistula.

Objective: This article aims to propose a new surgical method for the treatment of pyriform fistula, especially for the complex pyriform fistula. Methods: A total of 36 patients with pyriform fistula underwent the procedure between August 2017 to October 2020. Surgery was performed by the senior authors using the same technique at the same clinical center for all patients. The median follow-up time was 33 months. Meantime, we collected information on patients with pyriform fistula using traditional surgical methods in our hospital from April 2015 to November 2018 for comparison. Results: The surgery was successfully completed in 36 patients. In all, 32 patients had a history of multiple incisions and drainage, 16 patients had a history of surgical resections, and two patients had a history of cauterization of the internal fistula. Compared with traditional surgical methods, our new surgical method greatly shortens the length of the surgical incision (4.3 vs. 5.5, p < 0.0001), reduces the operation time (8.1 vs. 27.1, p < 0.0001), and reduces the blood loss (103.2 vs. 196.8, p < 0.0001). None of the 36 patients in this study had complications such as pharyngeal fistula, recurrent laryngeal nerve paralysis, or hypothyroidism. The mean follow-up duration after the excision of the lesion was 34.1 months. To date, no patients have relapsed. Conclusion: Our experience showed that this surgical technique could be used to completely remove the fistula, and it was easier to perform than the conventional strategies. These treatment options result in less trauma and reliable results, especially for complex pyriform fistulas. Level of evidence: IV.

Responses of the Soil Microbial Community to Salinity Stress in Maize Fields.

To investigate the diversity and structure of soil bacterial and fungal communities in saline soils, soil samples with three increasing salinity levels (S1, S2 and S3) were collected from a maize field in Yanqi, Xinjiang Province, China. The results showed that the K+, Na+, Ca2+ and Mg2+ values in the bulk soil were higher than those in the rhizosphere soil, with significant differences in S2 and S3 (p < 0.05). The enzyme activities of alkaline phosphatase (ALP), invertase, urease and catalase (CAT) were lower in the bulk soil than those in the rhizosphere. Principal coordinate analysis (PCoA) demonstrated that the soil microbial community structure exhibited significant differences between different salinized soils (p < 0.001). Data implied that the fungi were more susceptible to salinity stress than the bacteria based on the Shannon and Chao1 indexes. Mantel tests identified Ca2+, available phosphorus (AP), saturated electrical conductivity (ECe) and available kalium (AK) as the dominant environmental factors correlated with bacterial community structures (p < 0.001); and AP, urease, Ca2+ and ECe as the dominant factors correlated with fungal community structures (p < 0.001). The relative abundances of Firmicutes and Bacteroidetes showed positive correlations with the salinity gradient. Our findings regarding the bacteria having positive correlations with the level of salinization might be a useful biological indicator of microorganisms in saline soils.

Stat3-mTOR signaling mediates the stimulation of GLP-1 production induced by IL-27.

GLP-1 is a potent glucose-dependent insulinotropic hormone derived from intestinal L cells. Inflammatory Interleukin-27 (IL-27), a pleiotropic two-chain cytokine, is composed of EBI3 and IL-27 p28 subunits. IL-27 has a protective effect on pancreatic ß-cell function. The relationship between IL-27 and GLP-1 is still unexplored. Here we showed interleukin-27-stimulated GLP-1 production via the Stat3-mTOR-dependent mechanism. Interleukin 27 receptor subunit alpha (IL-27 Rα) was detected in ileum and STC-1 cells. Co-localization of EBI3 and GLP-1 was observed not only in mouse ileums but also in human ileums and colons. Third-ventricular infusion of IL-27 increased ileal and plasma GLP-1 in both lean C57BL/6J mice and diet-induced obese and diabetic mice. These changes were associated with a significant increase in Stat3-mTOR activity. Treatment of STC-1 cells with IL-27 contributed to the increments of Stat3-mTOR signaling and GLP-1. Interference of mTOR activity by mTOR siRNA or rapamycin abolished the stimulation of GLP-1 production induced by IL-27 in STC-1 cells. Stat3 siRNA also blocked the stimulus effect of IL-27 on GLP-1. IL-27 increased the interaction of mTOR and Stat3 in STC-1 cells. Our results identify Stat3-mTOR as a critical signaling pathway for the stimulation of GLP-1 induced by IL-27.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.

BACKGROUND: Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. RESULTS: In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. CONCLUSIONS: Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm.

BACKGROUND: Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. RESULTS: In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. CONCLUSIONS: The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs.