Targeting InhA in drug-resistant Mycobacterium tuberculosis: potent antimycobacterial activity of diaryl ether dehydrozingerone derivatives.

Tuberculosis (TB) remains a major global threat, with 10 million new cases and 1.5 million deaths each year. In multidrug-resistant tuberculosis (MDR-TB), resistance is most commonly observed against isoniazid (INH) and rifampicin (RIF), the two frontline drugs.

Innovative perspectives on metal free contrast agents for MRI: Enhancing imaging efficacy, and AI-driven future diagnostics.

The U.S. Food and Drug Administration (FDA) has issued a boxed warning and mandated additional safety measures for all gadolinium-based contrast agents (GBCAs) used in clinical magnetic resonance imaging (MRI) due to their prolonged retention in the body and associated adverse health effects.

Integrated Multidisciplinary Approach to Acute Chest Pain: Perspectives From Family Medicine, Emergency Medicine, and Internal Medicine.

Background: Acute chest discomfort is a common clinical problem that has to be well understood and managed collaboratively by specialists from many fields of medicine.

Objective: This study aimed to explore and evaluate the perspectives of healthcare professionals in family, emergency, and internal medicine regarding the management of acute chest pain, with a specific focus on diagnostic practices, interdisciplinary collaboration, and protocol adherence to establish best practices for a unified approach.

Methodology: This cross-sectional study, conducted from June 2022 to July 2024, included 218 healthcare professionals with over a year of experience in family, emergency, and internal medicine, selected through convenient sampling from hospitals such as Lady Reading Hospital, Hayatabad Medical Complex, Mardan Medical Complex, and Government Mian Meer Hospital.

Design and synthesis of betulinic acid-dithiocarbamate conjugates as potential antifungal agents against .

Diverse betulinic acid-dithiocarbamate conjugates were designed and synthesized a two-step reaction at room temperature. Among the fourteen dithiocarbamate analogs of betulinic acid, DTC2 demonstrated the best antifungal activity against , with a minimum inhibitory concentration (MIC) of 4 μg mL, achieving 99% fungicidal activity at the same concentration. These compounds were found to be ineffective against common Gram-negative and Gram-positive pathogens, suggesting their specificity to fungi.

Modulating sonic hedgehog (SHH) pathway to create a rapid CNS-TB model: Facilitating drug discovery.

Tuberculous meningitis, a severe complication of Mycobacterium tuberculosis (M. tb) infection, involves the dissemination of bacilli in the brain. This study explored the role of the sonic hedgehog (SHH) signaling pathway in regulating blood-brain barrier (BBB) integrity, M.

Prospective long-term follow-up after grommet insertion: Hearing and functional health outcomes in children.

Objectives: To assess hearing levels and functional health outcomes of children two years after routine grommet surgery with standard care follow-up (discharge to General Practitioner (GP) care or Ear Nose and Throat (ENT) clinic appointment at 4-8 weeks).

Methods: Prospective cohort study of 89 children (average age of 7.98 years) recalled for audiological assessment 2 years after grommet surgery in a large ENT outpatient service in South Auckland, New Zealand.

Antifungal Efficacy of Ultrashort β-Peptides against Species: Mechanistic Understanding and Therapeutic Implications.

Candidiasis, a condition spurred by the unchecked proliferation of species, poses a formidable global health threat, particularly in immunocompromised individuals. The emergence of drug-resistant strains complicates management strategies, necessitating novel therapeutic avenues. Antimicrobial peptides (AMPs) have garnered attention for their potent antifungal properties and broad-spectrum activity against species.

Revitalizing antimicrobial strategies: paromomycin and dicoumarol repurposed as potent inhibitors of M.tb's replication machinery via targeting the vital protein DnaN.

Despite the WHO's recommended treatment regimen, challenges such as patient non-adherence and the emergence of drug-resistant strains persist with TB claiming 1.5 million lives annually. In this study, we propose a novel approach by targeting the DNA replication-machinery of M.

Fabrication, optimization, and validation of penicillin-loaded hydrogels for controlled drug delivery.

Bacterial infections present a major global challenge. Penicillin, a widely used antibiotic known for its effectiveness and safety, is frequently prescribed. However, its short half-life necessitates multiple high-dose daily administrations, leading to severe side-effects.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales.

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals.

Trifluoroacetic acid-mediated synthesis of xanthene constructs and their extensive anti-tuberculosis evaluation.

A diverse range of 9-substituted 1,8-dioxohexahydroxanthenes was conceptualized and synthesized through a TFA-mediated approach in near quantitative yields without the use of column chromatography. From a series of 25 compounds, we found that compounds 14c and 14r exhibited promising anti-tuberculosis potential against avirulent and virulent strains of with a Minimal Inhibitory Concentration (MIC) of 8 μg ml, achieving 99% bactericidal activity at the same concentration. This series of compounds was found to be inactive against common Gram-positive and Gram-negative pathogens, indicating that the activity is mycobacteria-specific.

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis.

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool.

Actinomycetes - The Repertoire of Diverse Bioactive Chemical Molecules: From Structures to Antibiotics.

The urgent need for novel antibiotics in the face of escalating global antimicrobial resistance necessitates innovative approaches to identify bioactive compounds. Actinomycetes, renowned for their prolific production of antimicrobial agents, stand as a cornerstone in this pursuit. Their diverse metabolites exhibit multifaceted bioactivities, including potent antituberculosis, anticancer, immunomodulatory, immuno-protective, antidiabetic, etc.

Facile synthesis of manganese-hafnium nanocomposites for multimodal MRI/CT imaging and in vitro photodynamic therapy of colon cancer.

Precise diagnosis of complex and soft tumors is challenging, which limits appropriate treatment options to achieve desired therapeutic outcomes. However, multifunctional nano-sized contrast enhancement agents based on nanoparticles improve the diagnosis accuracy of various diseases such as cancer. Herein, a facile manganese-hafnium nanocomposites (MnO-HfO NCs) system was designed for bimodal magnetic resonance imaging (MRI)/computed tomography (CT) contrast enhancement with a complimentary function of photodynamic therapy.

Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network.

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump's inlet may impact the generalization of traditional machine learning models trained on raw statistical features.

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network.

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD).

A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov-Smirnov Test.

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition.

Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest.

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity.

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann-Whitney Test.

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann-Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge.