Enhancing diagnostic accuracy in common human poxvirus infections during mpox outbreak: A dermoscopy review.

Poxviruses belong to the Poxviridae family, a group of pathogens known for their high infectivity in humans, posing significant health threats. One of the most well-known representatives of poxvirus infections is smallpox, which has been successfully eradicated. However, in recent years, there has been a resurgence in cases of mpox, another member of the Poxviridae family, raising concerns about the potential for a global pandemic or a worldwide health crisis. While the typical clinical presentation of mpox and other poxvirus infections often involves cutaneous lesions, there have been reports of various atypical and non-classic clinical manifestations. Dermoscopy has emerged as a crucial diagnostic tool, aiding dermatologists in clinical practice to make informed decisions. In this summary, we provide an overview of the clinical and dermoscopic features of representative cutaneous lesions associated with human poxvirus infections, including mpox, orf, milker's nodule, and molluscum contagiosum.

Serum Level of Brain-Derived Neurotrophic Factor (BDNF) Associated with Depression in Patients with Rosacea: A Candidate Predictive Biomarker.

Background: The biomarker to predict the depression in patients with rosacea was absent. Objective: We aimed to explore the potential association between BDNF and depression in patients with rosacea, and also to determine whether serum BDNF level is a potential biomarker for identifying depression in patients with rosacea. Methods: The patients with rosacea, rosacea with depression and healthy control were included, clinical evaluation (DLQI, RSSs, BDI-II) and serum BDNF levels detection were performed on subjects, the comparisons and correlation analysis of the obtained data were performed. Results: In clinical evaluation, whether DLQI or RSSs, rosacea with depression group was significantly higher compared to rosacea group. Besides, we found the serum BDNF levels were lower in patients with rosacea and rosacea with depression compared to healthy controls, also in the rosacea with depression group, serum BDNF levels were lower than in rosacea patients. Whatever in rosacea or rosacea with depression group, the statistical significance of serum BDNF levels between the different subtypes like the ETR and PPR was not found. In further correlation analysis, we found no correlation between serum BDNF and RSSs in patients with rosacea whatever the subtype of ETR or PPR. Interestingly, we found a negative correlation between serum BDNF levels and BDI-II in rosacea with depression group, the decreased serum BDNF levels were associated with the increased BDI-II, also the ROC confirmed it can evaluate the depression in patients with rosacea. Conclusion: Serum BDNF level is a potential biomarker for identifying depression in patients with rosacea.

A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica.

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station's sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica.

In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (Remote Control, Image Acquisition, Operation Maintenance, and Document Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China's Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.

CEBPA mutational analysis in acute myeloid leukaemia by a laboratory-developed next-generation sequencing assay.

AIM: The presence of biallelic CEBPA mutations is a favourable prognostic feature in acute myeloid leukaemia (AML). CEBPA mutations are currently identified through conventional capillary sequencing (CCS). With the increasing adoption of next-generation sequencing (NGS) platforms, challenges with regard to amplification efficiency of CEBPA due to the high GC content may be encountered, potentially resulting in suboptimal coverage. Here, the performance of an amplicon-based NGS method using a laboratory-developed CEBPA-specific Nextera XT (CEBNX) was evaluated. METHODS: Mutational analyses of the CEBPA gene of 137 AML bone marrow or peripheral blood retrospective specimens were performed by the amplification of the CEBPA gene using the Expand Long Range dNTPack and the amplicons processed by CCS and NGS. CEBPA-specific libraries were then constructed using the Nextera XT V.2 kit. All FASTQ files were then processed with the MiSeq Reporter V.2.6.2.3 using the PCR Amplicon workflow via the customised CEBPA-specific manifest file. The variant calling format files were analysed using the Illumina Variant Studio V.2.2. RESULTS: A coverage per base of 3631X to 28184X was achieved. 22 samples (16.1%) were found to contain CEBPA mutations, with variant allele frequencies (VAF) ranging from 3.8% to 58.2%. Taking CCS as the 'gold standard', sensitivity and specificity of 97% and 97% was achieved. For the transactivation domain 2 polymorphism (c.584_589dupACCCGC/p.His195_Pro196dup), the CEBNX achieved 100% sensitivity and 100% specificity relative to CCS. CONCLUSIONS: Our laboratory-developed CEBNX workflow shows high coverage and thus overcomes the challenges associated with amplification efficiency and low coverage of CEBPA. Therefore, our assay is suitable for deployment in the clinical laboratory.

Process optimization for microwave-assisted direct liquefaction of Sargassum polycystum C.Agardh using response surface methodology.

Response surface methodology (RSM) was used to optimize the microwave-assisted direct liquefaction of Sargassum polycystum C.Agardh in ethylene glycol (EG) with H(2)SO(4) as a catalyst. Based on the results of single factor experiments, EG-to-feedstock ratio, temperature and catalyst content were chosen as independent variables for a central composite rotatable design (CCRD). The optimal liquefaction conditions were estimated as: the EG-to-feedstock ratio of 18.50:1 (w/w), the temperature of 170°C, the reaction time of 15 min, catalyst content of 9.6% (catalyst/EG, w/w%) and microwave power of 400 W with the liquefaction yield of 87.70%. The bio-oils were mainly composed of fatty acid methyl ester and alkane with a long chain from C(17) to C(20).

Microwave-assisted direct liquefaction of Ulva prolifera for bio-oil production by acid catalysis.

Production of bio-oil by microwave-assisted direct liquefaction (MADL) of Ulva prolifera was investigated, and the bio-oil was analyzed by elementary analysis, Fourier transform infrared spectroscopic analysis (FT-IR), and gas chromatography-mass spectrometry (GC-MS). The results indicate that the liquefaction yield is influenced by the microwave power, liquefaction temperature, liquefaction time, catalyst content, solvent-to-feedstock ratio and moisture content. The maximum liquefaction yield of U. prolifera (moisture content of 8%) was 84.81%, which was obtained under microwave power of 600 W for 30 min at 180 °C with solvent-to-feedstock ratio of 16:1 and 6% H(2)SO(4). The bio-oil was composed of benzenecarboxylic acid, diethyl phthalate, long-chain fatty acids (C(13) to C(18)), fatty acid methyl esters and water. The results suggest that U. prolifera is a viable eco-friendly, green feedstock substitute for biofuels and chemicals production.