Here we will review different bacterial causes of respiratory tract infections and discuss the available diagnostic methods. Moreover we will provide some recently published patents and newer techniques such as respiratory panels and omics approaches and express the challenges in this path.

Respiratory tract infections (RTIs) include those infections that can lead to the involvement of different respiratory parts including the sinuses throat airways and lungs. Acute respiratory tract infection is the leading cause of death from infectious illnesses worldwide. According to the World Health Organization 1.6 to 2.2 million deaths have occurred due to acute respiratory infections in children under five years of age. About 4 million people die annually from respiratory infections 98% of which are caused by lower respiratory infections.

Depending on the type of pathogen the severity of the infection can vary from mild to severe and even cause death. The most important pathogens involved in respiratory tract infections include Streptococcus pneumoniae Haemophilus influenzae and Moraxella catarrhalis. The symptoms are often similar but the treatment can vary greatly. Therefore correct diagnosis is so important. There are several methods for diagnosing respiratory infections. Traditional tests include the culture of respiratory samples considered the primary tool for diagnosing respiratory infections in laboratories and less common standard tests include rapid and antigenic tests. It is essential to think that the culture method is reliable. In the original method of diagnosing respiratory infections some bacteria were challenging to grow successfully and many clinical laboratories needed to be equipped for viral cultures. Another issue is the time to get the results which may take up to 7 days. Rapid and antigenic tests are faster but need to be more accurate.

The clinical laboratories are trying to be equipped with molecular methods for detecting respiratory pathogens and identifying the genetic material of the infectious agent in these new methods as the primary method in their agenda.

Parthenium hysterophorous and Lantana camara are notable for their significant phytochemical and antimicrobial properties. Advancements in phytochemical research have led to the development of novel formulations and products derived from P. hysterophorus and L. camara. For instance patent extracts from these plants have been utilized in the formulation of pharmaceutical drugs herbal supplements cosmeceuticals and agricultural products. P. hysterophorous commonly known as Santa Maria feverfew or Congress grass contains various bioactive compounds like terpenoids flavonoids phenolics and alkaloids.These compounds are the key to its medicinal properties particularly its antimicrobial activity. On the other hand L. camara often referred to as wild sage is rich in phytochemicals such as terpenoids flavonoids and alkaloid glycosides.

P. hysterophorous and L. camara plants selected and checking their antimicrobial activity by agar well diffusion method.

In our study we found that the leaf extract of P. hysterophorous exhibited the most potent antibacterial activity against E. coli. P. hysterophorous exhibited the most potent antifungal activity against A. niger and T. viride with a diameter of inhibition zone measuring 12 mm followed by A. flavus and A. parasiticus. In case of L. camara the inhibitory zone ranging from 14 to 18 mm was detected against S. abony P. aeruginosa E. coli and K. pneumonia. The leaf extract of the maximum zone of inhibition in case of L. camara was shown by A. flavus (12 mm).

The present study suggests that these two weeds could be useful in the development of bactericides and fungicides

Diabetic peripheral neuropathy (DPN) is a complication of diabetes that occurs in 40 - 60 million individuals worldwide and is associated with other chronic diseases. However there are no review studies that present the state-of- the- art and technologies developed to circumvent this important health problem.

This review was conducted based on scientific papers and patents. The papers were retrieved from Lilacs PubMed and Web of Science databases and the patents from INPI ESPACENET WIPO and GOOGLE PATENTS. Thus a sample consisting of 14 scientific articles and 667 patents was analyzed.

From the analysis of the data we drew an overview of the development of biomedical technologies for DPN and detected the pioneering spirit of China the USA and Japan in the area with a focus on the treatment of DPN. Based on this we carried out a SWOT analysis to help direct future efforts in the area which should focus primarily on developing technologies for prevention early diagnosis and above all cure of the disease to reduce the important impact of this disease in various sectors of society.

This study finds a concentration of diabetic peripheral neuropathy products especially therapeutic drugs in high-income countries. It highlights the need for global collaboration and strategic focus on therapeutic adherence and preventive strategies to effectively manage DPN.

The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed studies have predominantly adopted the IPF approach while others preferred the MPF method in which host and pathogen sequences are concatenated before feature encoding.

This presents the challenge of determining which approach should be adopted for improved HPPPI prediction. Therefore this work introduces the Extended Protein Feature (EPF) method.

The proposed method combines the predictive capabilities of IPF and MPF extracting essential features handling multicollinearity and removing features with zero importance. EPF IPF and MPF were tested using bacteria parasite virus and plant HPPPI datasets and were deployed to machine learning models including Random Forest (RF) Support Vector Machine (SVM) Multilayer Perceptron (MLP) Naïve Bayes (NB) Logistic Regression (LR) and Deep Forest (DF).

The results indicated that MPF exhibited the lowest performance overall whereas IPF performed better with decision tree-based models such as RF and DF. In contrast EPF demonstrated improved performance with SVM LR NB and MLP and also yielded competitive results with DF and RF.

In conclusion the EPF approach developed in this study exhibits substantial improvements in four out of the six models evaluated. This suggests that EPF offers competitiveness with IPF and is particularly well-suited for traditional machine learning models.

Currently various types of peptides have broad implications for human health and disease. Some drug peptides play significant roles in sensory science drug research and cancer biology. The prediction and classification of peptide sequences are of significant importance to various industries. However predicting peptide sequences through biological experiments is a time-consuming and expensive process. Moreover the task of protein sequence classification and prediction faces challenges due to the high dimensionality nonlinearity and irregularity of protein sequence data along with the presence of numerous unknown or unlabeled protein sequences. Therefore an accurate and efficient method for predicting peptide category is necessary.

In our work we used two pre-trained models to extract sequence features TextCNN (Convolutional Neural Networks for Text Classification) and Transformer. We extracted the overall semantic information of the sequences using Transformer Encoder and extracted the local semantic information between sequences using TextCNN and concatenated them into a new feature. Finally we used the concatenated feature for classification prediction. To validate this approach we conducted experiments on the BP dataset THP dataset and DPP-IV dataset and compared them with some pre-trained models.

Since TextCNN and Transformer Encoder extract features from different perspectives the concatenated feature contains multi-view information which improves the accuracy of the peptide predictor.

Ultimately our model demonstrated superior metrics highlighting its efficacy in peptide sequence prediction and classification.

Variants of concern were identified in severe acute respiratory syndrome coronavirus 2 namely Alpha Beta Gamma Delta and Omicron. This study explores the mutations of the Omicron lineage and its differences from other lineages through a protein language model.

By inputting the severe acute respiratory syndrome coronavirus 2 wild-type sequence into the protein language model evolving pre-trained models-1v this study obtained the score for each position mutating to other amino acids and calculated the overall trend of a new variant of concern mutation scores.

It is found that when the proportion of unobserved mutations to observed mutations is 4:15 Omicron still generates a large number of newly emerging mutations. It was found that the overall score for the Omicron family is low and the overall ranking for the Omicron family is low.

Mutations in the Omicron lineage are different from amino acid mutations in other lineages. The findings of this paper deepen the understanding of the spatial distribution of spike protein amino acid mutations and overall trends of newly emerging mutations corresponding to different variants of concern. This also provides insights into simulating the evolution of the Omicron lineage.

Circular RNAs (circRNAs) play important regulatory roles in the progression of gastric cancer (GC) but the exact mechanisms governing their regulation remain incompletely understood. Prior studies typically used back-spliced junctions (BSJs) to represent a range of circRNA isoforms overlooking the prevalence of alternative splicing (AS) events within circRNAs which could lead to unreliable or even incorrect conclusions in subsequent analyses hindering our comprehension of the specific functions of circRNAs in GC.

This study aimed to explore the potential functional roles of the dysregulated circRNA transcripts in GC and provide new biomarkers and effective novel therapeutic strategies for GC treatment.

RNA-seq data with rRNA depletion and RNase R treatment was employed to characterize the expression profiles of circRNAs in GC and RNA-seq data only with rRNA depletion was employed to identify differentially expressed mRNAs in GC. Based on the full-sequence information and accurate isoform-level quantification of circRNA transcripts calculated by the CircAST tool we performed a series of bioinformatic analyses. A circRNA-miRNA-hub gene regulatory network was constructed to reveal the circRNA-mediated regulation of competing endogenous RNAs in GC and then the protein-protein interaction (PPI) network was built to identify hub genes.

A total of 18398 circular transcripts were successfully reconstructed in the samples. Herein 351 upregulated and 177 downregulated circRNA transcripts were identified. Functional enrichment analysis revealed that their parental genes were strongly associated with GC. After several screening steps 19 dysregulated circRNA transcripts 40 related miRNAs and 65 target genes (mRNAs) were selected to construct the ceRNA network. Through PPI analysis five hub genes (COL5A2 PDGFRB SPARC COL1A2 and COL4A1) were excavated. All these hub genes may play vital roles in gastric cancer cell proliferation and invasion.

Our study revealed a comprehensive profile of full-length circRNA transcripts in GC which could provide potential prognostic biomarkers and targets for GC treatment. The results would be helpful for further studies on the biological roles of circRNAs in GC and offer new mechanistic insights into the pathogenesis of GC.

Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted extending beyond the obvious facial malformation to encompass physiological functions oral health psychological well-being and social aspects.

The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods this study aims to establish imaging as the primary diagnostic approach for this disease.

In this work we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization the simulated dataset was constructed using the random disturbance factor which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then we proposed a novel feature reconstruction method including six types of reconstructed factors to reconstruct the existing features. Then the reconstructed features weretrained with machine learning algorithms. Finally the testing and independent data model was utilized to analyze the performance of this work.

By comparing different operator features the min operator max operator average operator and all operators can achieve good performances in both the testing set and the independent set.

With the different operator features the majority of classification models including Gradient Boosting Hist Gradient Boosting Multilayer Perceptron lightGBM and broadened learning classification algorithms can get the well-performances in the selected reconstructed feature operators.