Covalent Organic Framework Membranes from Oppositely Charged Nanosheets toward Efficient Desalination.

Fabricating covalent organic framework (COF) membranes through the pre-assembly of nanosheets with different properties may open a novel avenue to the fabrication of advanced 2D membranes. Herein, COF membranes are fabricated using oppositely-charged COF nanosheets (CONs). Negatively-charged CONs and positively-charged CONs are pre-assembled through simple physical mixing, yielding the CONs with an aspect ratio of exceeding 10 000, which are assembled into three kinds of COF membranes. The optimal membranes exhibit the highest desalination performance with permeation flux of 132.66 kg m-2 h-1, salt rejection of 99.99%, and superior long-term operation stability.

Pseudomonas subflava sp. nov., a new Gram-negative bacterium isolated from Guishan in Yunnan province, south-west China.

A new Gram-negative, rod-shaped, flagellated bacterium was isolated from soil in the Guishan, Xinping County, Yuxi City, Yunnan Province, China, and named YIM B01952T. Growth occurred at 10-40 °C (optimum, 30 °C), pH 6.0-9.0 (optimum, pH 7.5) and with up to ≤ 5.0% (w/v) NaCl on Tryptic Soy Broth Agar (TSA) plates. Phylogenetic analysis based on the 16S rRNA gene and draft-genome sequence showed that strain YIM B01952T belonged to the genus Pseudomonas, and was closely related to the type strain of Pseudomonas alcaligenes (sequence similarity was 98.8%). The digital DNA-DNA hybridization (dDDH) value between strain YIM B01952T and the parallel strain P. alcaligenes ATCC 14909T was 49.0% based on the draft genome sequence. The predominant menaquinone was Q-9. The major fatty acids were summed feature 8 (C18:1 ω6c and/or C18:1 ω7c), summed feature 3 (C16:1 ω6c and/or C16:1 ω7c) and C16:0. The major polar lipids were phosphatidylethanolamine, diphosphatidylglycerol, and phosphatidylglycerol. The genome size of strain YIM B01952T was 4.341 Mb, comprising 4156 predicted genes with a DNA G + C content of 66.4 mol%. In addition, we detected that strain YIM B01952T had some traditional functional genes (plant growth promotion and multidrug resistance), unique genes through genome comparison and analysis with similar strains. Based on genetic analyses and biochemical characterization, the strain YIM B01952T was identified as a novel species in the genus Pseudomonas, for which the name Pseudomonas subflava sp. nov. is proposed. The type strain is YIM B01952T (=CCTCC AB 2021498T = KCTC 92073T).

Chitinolyticbacter albus sp. Nov., A Novel Chitin-Degrading Bacterium Isolated from Ancient Wood Rhizosphere Soil.

In this study, a novel aerobic mesophilic bacterial strain with capable of degrading chitin, designated YIM B06366T, was isolated and classified. The rod-shaped, Gram-stain-negative, on-spore-forming bacterium originated from rhizosphere soil sample collected in Kunming City, Yunnan Province, southwest PR China. Strain YIM B06366T exhibited growth at temperatures between 20 and 35 °C (optimum, 30 °C) and at pH 6.0-8.0 (optimum, pH 6.0). The analysis of 16S rRNA gene sequence similarity revealed that strain YIM B06366T was most closely related to type strain Chitinolyticbacter meiyuanensis SYBC-H1T (98.9%). Phylogenetic analysis based on genome data indicated that strain YIM B06366T should be assigned to the genus Chitinolyticbacter. The Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (dDDH) values between strain YIM B06366T and the reference strain Chitinolyticbacter meiyuanensis SYBC-H1T were 84.4% and 27.7%, respectively. The major fatty acids included Summed Feature 3 (C16:1 ω6c/C16:1 ω7c), Summed Feature 8 (C18:1 ω6c/C18:1 ω7c), and C16:0. The polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, aminophospholipids, and two unidentified phospholipids. The predominant menaquinone was Q-8, and the genomic DNA G + C content was 64.1%. Considering the polyphasic taxonomic evidence, strain YIM B06366T is proposed as a novel species within the genus Chitinolyticbacter, named Chitinolyticbacter albus sp. nov. (type strain YIM B06366T = KCTC 92434T = CCTCC AB 2022163T).

Cohnella mopanensis sp. nov., a new Gram-negative bacterium isolated from soil in Xinping Mountain National Forest Park.

A Gram-stain-negative, aerobic, rod-shaped bacteria strain, named YIM B01951T, was isolated from a forest soil sample collected from Mopan Mountain National Forest Park, Xinping City, Yunnan Province, southwest PR China (101°58'06" N, 23°03'02" E). Growth occurred at 15-40 °C (optimum, 30 °C), pH 5.0-8.0 (optimum, pH 6.5) and with up to ≤ 3.0% (w/v) NaCl on Nutrient Agar plates. The results of 16S rRNA gene sequence similarity analysis showed that strain YIM B01951T was closely related to the type strain of Cohnella arctica M9-62T (96.5%) and Cohnella lupini RLAHU4BT (96.3%). YIM B01951T contains anteiso-C15:0 and iso-C16:0 as the major cellular fatty acids; the main polar lipids are diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), lysylphosphatidylglycerol (PGL) and five aminophospholipids (APL). The MK-7 is the major respiratory quinone and the DNA G + C content is 49.2 mol%. Based on these phenotypic, chemotaxonomic and phylogenetic analyses, strain YIM B01951T is considered to be a novel species of the genus Cohnella, and named Cohnella mopanensis sp. nov. The type strain is YIM B01951T (= NBRC 115331T = KCTC 43370T).

Deep insights into the viscosity of deep eutectic solvents by an XGBoost-based model plus SHapley Additive exPlanation.

Deep eutectic solvents (DESs) are emerging as novel green solvents for the processes of mass transport and heat transfer, in which the viscosity of DESs is important for their industrial applications. However, for DESs, the measurement of viscosity is time-consuming, and there are many factors influencing the viscosity, which impedes their wider application. This study aims to develop a data-driven model which could accurately and rapidly predict the viscosity of diverse DESs at different temperatures, and furthermore boost the design and screening of novel DESs. In this work, we collected 107 DESs with 994 experimental values of viscosity from published works. Given the significant effect of water on viscosity, the water content of each collected DES was labeled. The Morgan fingerprint was first employed as a feature to describe the chemical environment of DESs. And four machine learning algorithms were used to train models: support vector regression (SVR), random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost), and XGBoost showed the best predictive performance. In combination with the powerful interpretation method SHapley Additive exPlanation (SHAP), we further revealed the positive or negative effect of features on viscosity. Overall, this work provides a machine learning model which could predict viscosity precisely and facilitate the design and application of DESs.

Brevibacillus daliensis sp. nov., Isolated From Soil in Machangqing Nature Reserve.

A new aerobic bacterial strain, designated strain YIM B02290T, was isolated from the soil of Machangqing, Dali city, Yunnan Province, China. Cells were Gram-stain-positive, sporogenous, rod-shaped, and motile with peritrichous flagella. Strain YIM B02290T showed the highest 16S rRNA gene sequence similarity with Brevibacillus laterosporus (97.6%) and Brevibacillus halotolerans (97.6%). The ANI and dDDH values between strain YIM B02290T and the two reference strains Brevibacillus laterosporus LAM00312T and Brevibacillus halotolerans DSM 25T are 72.6% and 72.2%, 20.2% and 19.5% based on the draft genome sequence, respectively. The major cellular fatty acids contain anteiso-C15: 0 and iso-C15: 0. The diagnostic diamino acid of the cell-wall peptidoglycan was meso-diaminopimelic acid. The predominant menaquinone was identified as menaquinone-7. The main polar lipids of strain YIM B02290T were diphosphatidylglycerol, phosphatidylglycerol, phospholipid, phosphatidyl monomethylethanolamine. The genomic DNA G + C content was 40.6 mol%. All results showed that strain YIM B02290T represents a novel species of the genus Brevibacillus, for which the name Brevibacillus daliensis sp. nov. is proposed. The type strain is YIM B02290T (= CCTCC AB 2021094T = CGMCC 1.18802T = KCTC 43376T).

Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology.

In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development.

Inflammation-associated factors for predicting in-hospital mortality in patients with COVID-19.

The aim is to explore the relation between inflammation-associated factors and in-hospital mortality and investigate which factor is an independent predictor of in-hospital death in patients with coronavirus disease-2019. This study included patients with coronavirus disease-2019, who were hospitalized between February 9, 2020, and March 30, 2020. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO) were used to select variables. Multivariate Cox regression analysis was applied to identify independent risk factors in coronavirus disease-2019. A total of 1135 patients were analyzed during the study period. A total of 35 variables were considered to be risk factors after the univariate regression analysis of the clinical characteristics and laboratory parameters (p < .05), and LASSO regression analysis screened out seven risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were myoglobin (HR, 5.353; 95% CI, 2.633-10.882; p < .001), C-reactive protein (HR, 2.063; 95% CI, 1.036-4.109; p = .039), neutrophil count (HR, 2.015; 95% CI, 1.154-3.518; p = .014), interleukin 6 (Il-6; HR, 9.753; 95% CI, 2.952-32.218; p < .001), age (HR, 2.016; 95% CI, 1.077-3.773; p = .028), and international normalized ratio (HR, 2.595; 95% CI, 1.412-4.769; p = .002). Our results suggested that inflammation-associated factors were significantly associated with in-hospital mortality in coronavirus disease-2019 patients. C-reactive protein, neutrophil count, and interleukin 6 were independent factors for predicting in-hospital mortality and had a better independent predictive ability. We believe these findings may allow early identification of the patients at high risk for death, and can also assist in better management of these patients.

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy.

Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is extracting valuable features and filtering out redundancy. To achieve a more efficient classification model, we put the feature synonym problem into consideration and create a new method based on functional word clustering and integration. Through evaluating the evolution correlation between features using the transition scores in mature molecular substitution matrices, candidate features are clustered into synonym groups. Each group of the clustered features is then integrated and represented by a unique key functional word. These retained key functional words are used to form a feature knowledge base. The original GPCR sequences are then transferred into feature vectors based on a feature re-extraction strategy according to the features in the knowledge base before the training and testing stage. We create multiple machine learning models based on Naïve Bayesian (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The established model is applied to classify two public data sets containing 8354 and 12,731 GPCRs, respectively. These models achieve significant performance in almost all evaluation criteria in comparison with state-of-the art. This work demonstrated the potential of the novel feature extraction strategy and provided an effective theoretical design for the hierarchical classification of GPCRs.

Comparison of Methods for Molecular Species Delimitation Across a Range of Speciation Scenarios.

Species are fundamental units in biological research and can be defined on the basis of various operational criteria. There has been growing use of molecular approaches for species delimitation. Among the most widely used methods, the generalized mixed Yule-coalescent (GMYC) and Poisson tree processes (PTP) were designed for the analysis of single-locus data but are often applied to concatenations of multilocus data. In contrast, the Bayesian multispecies coalescent approach in the software Bayesian Phylogenetics and Phylogeography (BPP) explicitly models the evolution of multilocus data. In this study, we compare the performance of GMYC, PTP, and BPP using synthetic data generated by simulation under various speciation scenarios. We show that in the absence of gene flow, the main factor influencing the performance of these methods is the ratio of population size to divergence time, while number of loci and sample size per species have smaller effects. Given appropriate priors and correct guide trees, BPP shows lower rates of species overestimation and underestimation, and is generally robust to various potential confounding factors except high levels of gene flow. The single-threshold GMYC and the best strategy that we identified in PTP generally perform well for scenarios involving more than a single putative species when gene flow is absent, but PTP outperforms GMYC when fewer species are involved. Both methods are more sensitive than BPP to the effects of gene flow and potential confounding factors. Case studies of bears and bees further validate some of the findings from our simulation study, and reveal the importance of using an informed starting point for molecular species delimitation. Our results highlight the key factors affecting the performance of molecular species delimitation, with potential benefits for using these methods within an integrative taxonomic framework.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Valorizing guava (Psidium guajava L.) seeds through germination-induced carbohydrate changes.

Guava seeds are produced as a waste product by the guava processing industry. Their high carbohydrate contents may suit the carbohydrate needs of the feed sector but their high dietary fiber content limits their feed value. The feed values of fruit seeds can be improved through germination, which involves the mobilization of nutrients through seed enzymes and alters the seed carbohydrate composition. The changes of selected carbohydrates in guava (Psidium guajava L.) seeds brought by germination to those in red bean (Vigna angularis) and winter wheat (Triticum aestivum L.) were compared. The contents of soluble carbohydrates, digestible starch, resistant starch and cellulose in the seeds were determined. The radial diffusion method was used to detect carbohydrate-degrading enzymes in the seed extracts. Guava seeds were rich in cellulose (402.2 mg/g), which decreased progressively during germination, probably through the action of cellulase. Winter wheat contained the highest starch content (412.2 mg/g) and also distinct quantities of α-amylase and cellulase. The starch contents of all the seeds decreased, but the soluble carbohydrate contents in red beans and guava seeds increased significantly by the end of germination, suggesting the transient oversupply of reserve metabolites. The content of hydrolyzed polysaccharides increased in the germinated seeds with detectable amounts of cellulose-degrading enzymes present, indicating improved value as feed. Further research is warranted to explore the potential of guava seeds as a source of low-cost animal feed supplements.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

Research Progress of Nervonic Acid Biosynthesis.

Nervonic acid (NA) is a very-long-chain monounsaturated fatty acid with great application values. It plays a vital role in the development of brain nervous system and the treatment of neurological diseases, so it has attracted much attention from all walks of life. Although NA has a wide range of sources, its current acquisition methods are still mainly relied on chemical synthesis and plant extraction, which are challenging to meet the market and green industry demands, limiting its development and application. In recent years, with the rapid development of synthetic biology technology, NA biosynthesis has become an alternative production strategy. In this study, we summarize the physicochemical properties, pharmacological activities, resources, biosynthetic pathways and heterologous biosynthesis of NA, and discuss the challenges and prospects of NA biosynthesis. The application prospects of cell-free systems and retrobiosynthesis in NA synthesis were also reviewed.

Degenerate and non-degenerate all-optical switches using violet phosphorus nanosheets.

As an allotrope of phosphorus, layered violet phosphorus (VP) has a wide range of applications in electronics, photonics, and optoelectronics. However, its nonlinear optical properties remain to be explored. In this work, we prepare and characterize VP nanosheets (VP Ns), investigate their spatial self-phase modulation (SSPM) effects, and develop them in all-optical switching applications. The ring forming time of SSPM and the third-order nonlinear susceptibility of monolayer VP Ns were found to be about 0.4 s and 10-9 esu, respectively. The mechanism of SSPM formed by coherent light-VP Ns interaction is analyzed. Using the superior coherence electronic nonlinearity of VP Ns, we realize degenerate and non-degenerate all-optical switches based on the SSPM effect. It is demonstrated that the performance of all-optical switching can be controlled by adjusting the intensity of the control beam and/or the wavelength of the signal beam. The results will help us to better design and realize non-degenerate nonlinear photonic devices based on two-dimensional nanomaterials.

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale. The establishment of a rapid and efficient assessment method for small-scale ecosystem quality will greatly promote the protection and restoration of ecosystems in China. We focused on the calculation method of ecosystem quality assessment and NPP, and optimized the calculation process of the NPP, and obtained NPP by remote sensing without ground observation data. We established a regression model for remote sensing inversion of leaf area index, and estimated the vegetation coverage by using dimidiate pixel model, developed a set of methods for rapid assessment of ecosystem quality by remote sensing. Taking Nanwenghe National Nature Reserve as an example, we evaluated the change of ecosystem quality from 2001 to 2022. The results showed that from 2001 to 2022, the ecosystem qua-lity of the reserve was good as a whole, and that the area with good and excellent quality accounted for more than 85% in 2022. High vegetation coverage was the backbone of the sustainable good ecosystem quality of the reserve. From 2001 to 2022, ecosystem quality of the reserve showed a trend of first decreasing and then increasing, with the lowest point of ecosystem quality in 2013. This method had achieved good results in the evaluation of ecosystem quality in Nanwenghe National Nature Reserve. The index optimization method proposed in this study could facilitate the rapid and accurate assessment of ecosystem quality in small-scale regions, and thus provides technical reference for the precise improvement of ecosystem quality.

[Mid-term clinical outcome of arthroscopic surgery on early knee osteoarthritis in middle-old aged patients].

OBJECTIVE: To compare the mid-term clinical effect of arthroscopic surgery versus conservative treatment on the middle aged early knee osteoarthritis (EKOA) patients, with the hope to provide clinical evidence for their individual therapy. METHODS: A total of 145 middle aged EKOA patients(182 knees) who received arthroscopic surgery or conservative treatment from January 2015 to December 2016 were retrospectively enrolled, including 35 males and 110 females, aged from 47 to 79 years old with an average of (57.6±6.9) years old, and the duration of disease ranged from 6 to 48 months with an average of(14.6±8.9) months. According to treatment method, patients were divided into arthroscopic surgery group (47 patients, 58 knees) and conservative treatment group(98 patients, 124 knees). Before treatment, patients presented with symptoms of knee joint, such as pain, swelling, locking, limited flexion and extension, and weakness, as well as abnormal findings in knee X-ray (without or suspicious joint space narrow, and a few of osteophyte formation) or in knee MRI (injury or degeneration of articular cartilage or meniscus, loose body in the joint cavity and synovial hyperemia edema, etc). Related data were collected, including duration of knee symptoms, presence of meniscus injury, loose body in the joint cavity or mechanical symptoms such as locking, and visual analogue scale (VAS) and Lysholm knee function score before treatment and at the latest follow-up. Statistical analysis was performed to compare the differences in VAS or Lyshilm score before or after treatment between the low groups and within each group. RESULTS: Patients in the two groups were followed up from 60 to 76 months. In the arthroscopic surgery group, the incision healing was good and no surgical complications occurred. There were no significant differences in age, gender, BMI and follow-up time between the two groups(P>0.05). Before treatment, compared with conservative group, duration of symptoms in the arthroscopic group was longer (P<0.001), comorbidity rates of meniscus injury (P<0.001), free body (P=0.001) and mechanical symptoms (P<0.001) were higher, VAS (P<0.001) and Lysholm score (P<0.001) were worse. At the final follow-up, VAS and Lysholm score in either the conservative group or the arthroscopic group were significantly better than before treatment (P<0.05), while no significant differences between the two groups were found. The VAS was (1.5±1.2) scores in the arthroscopic group and (1.6±1.0)scores in the conservative group(P=0.549), and the Lysholm score was (84.9±12.5) scores in the arthroscopic group and (84.2±9.9) scores in the conservative group (P=0.676). CONCLUSION: Both arthroscopic surgery and conservative treatment have satisfactory intermediate clinical effect middle- aged patients with EKOA, without statistically differences. However, most of the patients before surgery in the arthroscopic treatment group had mechanical locking symptoms caused by meniscus injury or loose body. Therefore, for the middle-aged EKOA patients with mechanical locking symptoms or without obtaining satisfactory outcome after conservative treatment, arthroscopic surgery may be considered.

Effects of electroacupuncture on the expression of hypothalamic neuropeptide Y and ghrelin in pubertal rats with polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome often starts in puberty, and its pathogenesis is not clear. This study aimed to explore the pathogenesis of pubertal polycystic ovary syndrome (PCOS) and assess the therapeutic effect of electroacupuncture on pubertal PCOS. METHODS: Dihydrotestosterone (DHT) was used to induce rat models of pubertal PCOS. pubertal rats with PCOS were randomly divided into a model group (M), an electroacupuncture group (EA), and a sham acupuncture group (SA). Age-matched normal rats were regarded as normal controls (N). Rats were treated with EA or SA five times a week for 25 minutes during their 6th-7th week. At the end of the experiment, we observed any changes in ovarian morphology; detected levels of metabolic indices in serum, the hypothalamus and pancreas. RESULTS: EA significantly improved estrous cycle disorders and the ovarian polycystic morphology in pubertal rats with PCOS, but SA only improved disorders of the estrous cycle. The serum levels of insulin, neuropeptide Y(NPY) and fasting blood glucose(FBG) increased significantly (both p < 0.01), while the serum levels of ghrelin(GHRL) decreased in the model group (p < 0.01). After treatment with EA, the levels of NPY (p < 0.01) and FBG (p < 0.05) went into decrease, whereas the levels of GHRL (p < 0.05) and insulin (p < 0.01) increased. There was few differences in the hypothalamic expression of galanin (GAL), galanin-like peptide (GALP) and ghrelin receptor(GHSR) between the four groups. The upregulation of NPY mRNA and neuropeptide Y2 receptor(NPY2R) mRNA and the downregulation of GHRL protein and mRNA in the hypothalamus, and the increased expression of NPY and NPY2R as well as the decreased expression of GHRL in the arcuate nucleus (ARC) can be rescued by EA. But, surprisingly, SA seem to make no difference to the levels of FBG and insulin, and the protein expression of ghrelin in the hypothalamus and ARC. Co-expression of kisspeptin and GHSR, and co-expression of gonadotrophin releasing hormone(GnRH) and NPY2R were observed in ARC. No differences were found between groups in protein of GAL, GALP and GHRL expression in the pancreas. Neither EA nor SA can attenuate the upregulated kisspeptin protein expression in the pancreas of PCOS model rats. CONCLUSIONS: EA and SA improved the symptoms of pubertal PCOS rats, and the mechanism might be associated with regulating hypothalamic NPY and ghrelin levels.

LINC00958/miR-627 signal axis regulates the proliferation, migration, and invasion of thyroid papillary carcinoma cells by TRIM44.

Papillary thyroid cancer (PTC) has attracted much attention due to its high morbidity and severe metastasis. Long noncoding RNA ENST00000504230 (LncRNA ENST00000504230, known as LINC00958) was overexpressed in many cancers and associated with cancer development. However, its underlying mechanism in PTC remains unclear. PTC tissues and corresponding adjacent tissues were collected for measuring the expression of LINC00958 and miR-627. MiR-627 and TRIM44 expressions were measured in in vitro cultured PTC cell lines (B-cpap and IHH4 cells) transfected with sh-LINC00958 or miR-627 mimic using RT-qPCR and western blot. Cell proliferation, migration, and invasion were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) and Transwell assays, respectively. Dual-luciferase reporter assay was performed to evaluate the target association between miR-627 and TRIM44. LINC00958 was up-regulated in PTC tissues and cells, while the expression of miR-627 was lowly expressed. Knockdown of LINC00958 inhibited the proliferation, migration, and invasion by elevating miR-627 expression in PTC cells. TRIM44 was confirmed as a target of miR-627. Overexpression of miR-627 in PTC inhibited the proliferation, migration, and invasion by down-regulating the expression of TRIM44. LINC00958 promoted proliferation, migration, and invasion in PTC by down-regulating miR-627 and activating TRIM44, indicating the potential therapeutic effect of LINC00958 on PTC.

Psychological impact, coping behaviors, and traumatic stress among healthcare workers during COVID-19 in Taiwan: An early stage experience.

OBJECTIVE: This study investigated the psychological impact on, coping behaviors of, and traumatic stress experienced by healthcare workers during the early stage of the COVID-19 pandemic and formulated effective support strategies that can be implemented by hospitals and government policymakers to help healthcare staff overcome the pandemic. METHODS: This cross-sectional study recruited clinical healthcare workers at a regional hospital in Nantou County, Taiwan. The questionnaire collected personal characteristics, data on the impact and coping behaviors of the pandemic, and Impact of Event Scale-Revised (IES-R). A total of 354 valid questionnaires were collected. The statistical methods employed were univariate and multivariate stepwise regression, and logistic regression. RESULTS: Perceived impact and coping behaviors were found to be moderate in degree, and traumatic stress was lower than that in other countries. However, our data identified the following subgroups that require special attention: those with young age, those living with minor children, nurses, those with self-rated poor mental health, and those with insufficient COVID-19-related training. CONCLUSION: Managers should pay particular attention to helping healthcare workers in high-risk groups, strengthen COVID-19 training, provide adequate protective equipment and shelter, and offer psychological counseling.